Artificial intelligence (AI) Books

Book SynopsisTable of ContentsPreface xiii 1 IoT-Based Battery Management System for Hybrid Electric Vehicle 1P. Sivaraman and C. Sharmeela 1.1 Introduction 1 1.2 Battery Configurations 3 1.3 Types of Batteries for HEV and EV 5 1.4 Functional Blocks of BMS 6 1.4.1 Components of BMS System 7 1.5 IoT-Based Battery Monitoring System 11 References 14 2 A Noble Control Approach for Brushless Direct Current Motor Drive Using Artificial Intelligence for Optimum Operation of the Electric Vehicle 17Upama Das, Pabitra Kumar Biswas and Chiranjit Sain 2.1 Introduction 18 2.2 Introduction of Electric Vehicle 19 2.2.1 Historical Background of Electric Vehicle 19 2.2.2 Advantages of Electric Vehicle 20 2.2.2.1 Environmental 20 2.2.2.2 Mechanical 20 2.2.2.3 Energy Efficiency 20 2.2.2.4 Cost of Charging Electric Vehicles 21 2.2.2.5 The Grid Stabilization 21 2.2.2.6 Range 21 2.2.2.7 Heating of EVs 22 2.2.3 Artificial Intelligence 22 2.2.4 Basics of Artificial Intelligence 23 2.2.5 Advantages of Artificial Intelligence in Electric Vehicle 24 2.3 Brushless DC Motor 24 2.4 Mathematical Representation Brushless DC Motor 25 2.5 Closed-Loop Model of BLDC Motor Drive 30 2.5.1 P-I Controller & I-P Controller 31 2.6 PID Controller 32 2.7 Fuzzy Control 33 2.8 Auto-Tuning Type Fuzzy PID Controller 34 2.9 Genetic Algorithm 35 2.10 Artificial Neural Network-Based Controller 36 2.11 BLDC Motor Speed Controller With ANN-Based PID Controller 37 2.11.1 PID Controller-Based on Neuro Action 38 2.11.2 ANN-Based on PID Controller 38 2.12 Analysis of Different Speed Controllers 39 2.13 Conclusion 41 References 42 3 Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles 49Suraj Gupta, Pabitra Kumar Biswas, Sukanta Debnath and Jonathan Laldingliana 3.1 Introduction 50 3.2 Basic Components of an Active Magnetic Bearing (AMB) 54 3.2.1 Electromagnet Actuator 54 3.2.2 Rotor 54 3.2.3 Controller 55 3.2.3.1 Position Controller 56 3.2.3.2 Current Controller 56 3.2.4 Sensors 56 3.2.4.1 Position Sensor 56 3.2.4.2 Current Sensor 57 3.2.5 Power Amplifier 57 3.3 Active Magnetic Bearing in Electric Vehicles System 58 3.4 Control Strategies of Active Magnetic Bearing for Electric Vehicles System 59 3.4.1 Fuzzy Logic Controller (FLC) 59 3.4.1.1 Designing of Fuzzy Logic Controller (FLC) Using MATLAB 60 3.4.2 Artificial Neural Network (ANN) 63 3.4.2.1 Artificial Neural Network Using MATLAB 63 3.4.3 Particle Swarm Optimization (PSO) 67 3.4.4 Particle Swarm Optimization (PSO) Algorithm 68 3.4.4.1 Implementation of Particle Swarm Optimization for Electric Vehicles System 70 3.5 Conclusion 71 References 72 4 Small-Signal Modelling Analysis of Three-Phase Power Converters for EV Applications 77Mohamed G. Hussien, Sanjeevikumar Padmanaban, Abd El-Wahab Hassan and Jens Bo Holm-Nielsen 4.1 Introduction 77 4.2 Overall System Modelling 79 4.2.1 PMSM Dynamic Model 79 4.2.2 VSI-Fed SPMSM Mathematical Model 80 4.3 Mathematical Analysis and Derivation of the Small-Signal Model 86 4.3.1 The Small-Signal Model of the System 86 4.3.2 Small-Signal Model Transfer Functions 87 4.3.3 Bode Diagram Verification 96 4.4 Conclusion 100 References 100 5 Energy Management of Hybrid Energy Storage System in PHEV With Various Driving Mode 103S. Arun Mozhi, S. Charles Raja, M. Saravanan and J. Jeslin Drusila Nesamalar 5.1 Introduction 104 5.1.1 Architecture of PHEV 104 5.1.2 Energy Storage System 105 5.2 Problem Description and Formulation 106 5.2.1 Problem Description 106 5.2.2 Objective 106 5.2.3 Problem Formulation 106 5.3 Modeling of HESS 107 5.4 Results and Discussion 108 5.4.1 Case 1: Gradual Acceleration of Vehicle 108 5.4.2 Case 2: Gradual Deceleration of Vehicle 109 5.4.3 Case 3: Unsystematic Acceleration and Deceleration of Vehicle 110 5.5 Conclusion 111 References 112 6 Reliability Approach for the Power Semiconductor Devices in EV Applications 115Krishnachaitanya, D., Chitra, A. and Biswas, S.S. 6.1 Introduction 115 6.2 Conventional Methods for Prediction of Reliability for Power Converters 116 6.3 Calculation Process of the Electronic Component 118 6.4 Reliability Prediction for MOSFETs 119 6.5 Example: Reliability Prediction for Power Semiconductor Device 121 6.6 Example: Reliability Prediction for Resistor 122 6.7 Conclusions 123 References 123 7 Modeling, Simulation and Analysis of Drive Cycles for PMSM-Based HEV With Optimal Battery Type 125Chitra, A., Srivastava, Shivam, Gupta, Anish, Sinha, Rishu, Biswas, S.S. and Vanishree, J. 7.1 Introduction 126 7.2 Modeling of Hybrid Electric Vehicle 127 7.2.1 Architectures Available for HEV 128 7.3 Series—Parallel Hybrid Architecture 129 7.4 Analysis With Different Drive Cycles 129 7.4.1 Acceleration Drive Cycle 130 7.4.1.1 For 30% State of Charge 130 7.4.1.2 For 60% State of Charge 131 7.4.1.3 For 90% State of Charge 131 7.5 Cruising Drive Cycle 132 7.6 Deceleration Drive Cycle 132 7.6.1 For 30% State of Charge 134 7.6.2 For 60% State of Charge 136 7.6.3 For 90% State of Charge 137 7.7 Analysis of Battery Types 139 7.8 Conclusion 140 References 141 8 Modified Firefly-Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions 143Chitra, A., Yogitha, G., Karthik Sivaramakrishnan, Razia Sultana, W. and Sanjeevikumar, P. 8.1 Introduction 143 8.2 System Block Diagram Specifications 146 8.3 Photovoltaic System Modeling 148 8.4 Boost Converter Design 150 8.5 Incremental Conductance Algorithm 152 8.6 Under Partial Shading Conditions 153 8.7 Firefly Algorithm 154 8.8 Implementation Procedure 156 8.9 Modified Firefly Logic 157 8.10 Results and Discussions 159 8.11 Conclusion 162 References 162 9 Induction Motor Control Schemes for Hybrid Electric Vehicles/Electric Vehicles 165Sarin, M.V., Chitra, A., Sanjeevikumar, P. and Venkadesan, A. 9.1 Introduction 166 9.2 Control Schemes of IM 167 9.2.1 Scalar Control 167 9.3 Vector Control 168 9.4 Modeling of Induction Machine 169 9.5 Controller Design 174 9.6 Simulations and Results 175 9.7 Conclusions 176 References 177 10 Intelligent Hybrid Battery Management System for Electric Vehicle 179Rajalakshmi, M. and Razia Sultana, W. 10.1 Introduction 179 10.2 Energy Storage System (ESS) 181 10.2.1 Lithium-Ion Batteries 183 10.2.1.1 Lithium Battery Challenges 183 10.2.2 Lithium–Ion Cell Modeling 184 10.2.3 Nickel-Metal Hydride Batteries 186 10.2.4 Lead-Acid Batteries 187 10.2.5 Ultracapacitors (UC) 187 10.2.5.1 Ultracapacitor Equivalent Circuit 187 10.2.6 Other Battery Technologies 189 10.3 Battery Management System 190 10.3.1 Need for BMS 191 10.3.2 BMS Components 192 10.3.3 BMS Architecture/Topology 193 10.3.4 SOC/SOH Determination 193 10.3.5 Cell Balancing Algorithms 197 10.3.6 Data Communication 197 10.3.7 The Logic and Safety Control 198 10.3.7.1 Power Up/Down Control 198 10.3.7.2 Charging and Discharging Control 199 10.4 Intelligent Battery Management System 199 10.4.1 Rule-Based Control 201 10.4.2 Optimization-Based Control 201 10.4.3 AI-Based Control 202 10.4.4 Traffic (Look Ahead Method)-Based Control 203 10.5 Conclusion 203 References 203 11 A Comprehensive Study on Various Topologies of Permanent Magnet Motor Drives for Electric Vehicles Application 207Chiranjit Sain, Atanu Banerjee and Pabitra Kumar Biswas 11.1 Introduction 208 11.2 Proposed Design Considerations of PMSM for Electric Vehicle 209 11.3 Impact of Digital Controllers 211 11.3.1 DSP-Based Digital Controller 212 11.3.2 FPGA-Based Digital Controller 212 11.4 Electric Vehicles Smart Infrastructure 212 11.5 Conclusion 214 References 215 12 A New Approach for Flux Computation Using Intelligent Technique for Direct Flux Oriented Control of Asynchronous Motor 219A. Venkadesan, K. Sedhuraman, S. Himavathi and A. Chitra 12.1 Introduction 220 12.2 Direct Field-Oriented Control of IM Drive 221 12.3 Conventional Flux Estimator 222 12.4 Rotor Flux Estimator Using CFBP-NN 223 12.5 Comparison of Proposed CFBP-NN With Existing CFBP-NN for Flux Estimation 224 12.6 Performance Study of Proposed CFBP-NN Using MATLAB/SIMULINK 225 12.7 Practical Implementation Aspects of CFBP-NN-Based Flux Estimator 229 12.8 Conclusion 231 References 231 13 A Review on Isolated DC–DC Converters Used in Renewable Power Generation Applications 233Ingilala Jagadeesh and V. Indragandhi 13.1 Introduction 233 13.2 Isolated DC–DC Converter for Electric Vehicle Applications 234 13.3 Three-Phase DC–DC Converter 238 13.4 Conclusion 238 References 239 14 Basics of Vector Control of Asynchronous Induction Motor and Introduction to Fuzzy Controller 241S.S. Biswas 14.1 Introduction 241 14.2 Dynamics of Separately Excited DC Machine 243 14.3 Clarke and Park Transforms 244 14.4 Model Explanation 251 14.5 Motor Parameters 252 14.6 PI Regulators Tuning 254 14.7 Future Scope to Include Fuzzy Control in Place of PI Controller 256 14.8 Conclusion 257 References 258 Index 259

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Book SynopsisINTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, educatioTable of ContentsPreface Acknowledgement Introduction 1 Technology Adoption and Emerging Trends 1.1 Introduction 1.2 Trends in Business technology 1.3 AI-Fueled Organizations 1.4 Connectivity of Tomorrow 1.5 Moving Beyond Marketing 1.6 Cloud Computing 1.7 Cybersecurity, Privacy, and Risk Management 1.8 Conclusion 2 Telecommunication Transformation and Intelligent Connectivity 2.1 Introduction 2.2 Cybersecurity Concerns in the 5G World 2.3 Positive Effects of Addressing Cybersecurity Challenges in 5G 2.4 Intelligent Connectivity Use-Cases 2.5 Industrial and Manufacturing Operations 2.6 Healthcare 2.7 Public Safety and Security 2.8 Conclusion 3 The Internet of Things (IoT): Potentials and the Future Trends 3.1 Introduction 3.2 Achieving the Future of IoT 3.3 Commercial Opportunities for IoT 3.4 The Industrial Internet of Things 3.5 Future Impact of IoT in Our Industry 3.6 Data Sharing in the IoT Environment 3.7 IoT Devises Environment Operation 3.8 Interoperability Issues of IoT 3.9 IoT-Cloud –Application 3.10 Regulation and Security Issues of IoT 3.11 Achieving IoT Innovations While Tackling Security and Regulation Issues 3.12 Future of IoT 3.13 Conclusion 4 The Wild Wonders of 5G Wireless Technology 4.1 Introduction 4.2 5G Architecture 4.3 5G Applications 4.4 5G Network Architecture 4.5 Security and Issues of 5G 4.6 IoT Devices in 5G Wireless 4.7 Big Data Analytics in 5G 4.8 AI Empowers a Wide Scope of Use Cases 4.9 Conclusion 5 Artificial Intelligence Technology 5.1 Introduction 5.2 Core Concepts of Artificial Intelligence 5.3 Machine Learning and Applications 5.4 Deep Learning 5.5 Neural Networks Follow a Natural Model 5.6 Classifications of Artificial Intelligence 5.7 Trends in Artificial Intelligence 5.8 Challenges of Artificial Intelligence 5.9 Funding Trends in Artificial Intelligence 5.10 Conclusion 6 AI, 5G, & IoT: Driving Forces Towards the Industry Technology Trends 6.1 Introduction 6.2 Fifth Generation of Network Technology 6.3 Internet of Things (IoT) 6.4 Industrial Internet of Things 6.5 IoT in Automotive 6.6 IoT in Agriculture 6.7 AI, IoT, and 5G Security 6.8 Conclusion 7 Intelligent Connectivity: A New Capabilities to Bring Complex Use Cases 7.1 Introduction 7.2 Machine-to-Machine Communication and the Internet of Things 7.3 Convergence of Internet of Things, Artificial Intelligence and 5G 7.4 Intelligent Connectivity Applications 7.5 Challenges and Risks of Intelligent Connectivity 7.6 Recommendations 7.7 Conclusion 8 IoT: Laws, Policies and Regulations 8.1 Introduction 8.2 Recently Published laws and Regulations 8.3 Developing Innovation and Growing the Internet of Things (DIGIT) Act 8.4 General View 8.5 Relaxation of laws by the Federal Aviation Administration's (FAA) 8.6 Supporting Innovation of Self Driving Cars by Allowing Policies 8.7 Recommendations 8.8 Conclusion 9 Artificial Intelligence and Blockchain 9.1 Introduction 9.2 Decentralized Intelligence 9.3 Applications 9.4 How Artificial Intelligence and Blockchain will Affect Society 9.5 How Augmented Reality Works 9.6 Mixed Reality 9.7 Virtual Reality 9.8 Key Components in a Virtual Reality System 9.9 Augmented Reality Uses 9.10 Applications of Virtual Reality in Business 9.11 The Future of Blockchain 9.12 Blockchain Applications 9.13 Blockchain and the Internet of Things 9.14 Law Coordination 9.15 Collaboration for Blockchain Success 10 Digital Twin Technology 10.1 Introduction 10.2 The Timeline and History of Digital Twin Technology 10.3 Technologies Employed in Digital Twin Models 10.4 The Dimension of Digital Twins Models 10.5 Digital Twin and Other Technologies 10.6 Digital Twin Technology Implementation 10.7 Benefits of Digital Twin 10.8 Application of Digital Twins 10.9 Challenges of Digital Twins 11 Artificial Intelligence, Big Data Analytics, and IoT 11.1 Introduction 11.2 Analytic 11.3 AI Technology in Big Data and IoT 11.4 AI Technology Applications and Use Cases 11.5 AI Technology Impact on the Vertical Market 11.6 AI in Big Data and IoT Market Analysis and Forecasts 11.7 Conclusion 12 Digital Transformation Trends in the Automotive Industry 12.1 Introduction 12.2 Evolution of Automotive Industry 12.3 Data-Driven Business Model and data monetization 12.4 Services of Data-Driven Business Model 12.5 Values of New Services in the New Automotive Industry 12.6 Conclusion 13 Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home 13.1 Introduction 13.2 Wireless Sensor Networks 13.3 Power Grid Impact 13.4 Benefits of Smart Grid 13.5 Internet of Things 13.6 Internet of Things on Smart Grid 13.7 Smart Grid and Artificial Intelligence 13.8 Smart Grid Programming 13.9 Conclusion 14 Artificial Intelligence, 5G and IoT: Security 14.1 Introduction 14.2 Understanding IoT 14.3 Artificial Intelligence 14.4 5G Network 14.5 Emerging Partnership of Artificial Intelligence, IoT, 5G, and Cybersecurity 14.6 Conclusion 15 Intelligent Connectivity and Agriculture 15.1 Introduction 15.2 The Potential of Wireless Sensors and IoT in Agriculture 15.3 IoT Sensory Technology with Traditional Farming 15.4 IoT Devices and Communication Techniques 15.5 IoT and all Crop Stages 15.6 Drone in Farming Applications 15.7 Conclusion 16 Applications of Artificial Intelligence, ML, and DL 16.1 Introduction 16.2 Building Artificial Intelligence Capabilities 16.3 What is Machine Learning? 16.4 Deep Learning 16.5 Machine Learning vs. Deep Learning Comparison 16.6 Feature Engineering 16.7 Application of Machine Learning 16.8 Applications of Deep learning 16.9 Future Trends 17 Big Data and Artificial Intelligence: Strategies for Leading Business Transformation 17.1 Introduction 17.2 Big Data 17.2 Machine Learning-Based Medical Systems 17.3 Artificial Intelligence for Stock Market Prediction 17.3.1 Application of Artificial Intelligence by Investors 17.4 Trends in AI and Big Data Technologies Drive Business Innovation 17.5 Driving Innovation Through Big Data 17.6 The Convergence of AI and Big Data 17.7 How AI and Big Data Will Combine to Create Business Innovation 17.8 AI and Big Data for Technological Innovation 17.9 AI and Production 17.10 AI and ML Operations Research 17.11 Collaboration Between Machines and Human 17.12 Generative Designs 17.13 Adapting to a Changing Market 17.14 Conclusion Index

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Book SynopsisTable of ContentsPreface xv 1 Introduction to Blockchain 1Akshay Mudgal 1.1 Introduction 1 1.1.1 Public Blockchain Architecture 5 1.1.2 Private Blockchain Architecture 5 1.1.3 Consortium Blockchain Architecture 5 1.2 The Privacy Challenges of Blockchain 6 1.3 De-Anonymization 8 1.3.1 Analysis of Network 9 1.3.2 Transaction Fingerprinting 9 1.3.3 DoS Attacks 9 1.3.4 Sybil Attacks 9 1.4 Transaction Pattern Exposure 10 1.4.1 Transaction Graph Analysis 10 1.4.2 AS-Level Deployment Analysis 10 1.5 Methodology: Identity Privacy Preservation 10 1.5.1 Mixing Services 10 1.5.2 Ring Signature 12 1.6 Decentralization Challenges Exist in Blockchain 14 1.7 Conclusion 15 1.8 Regulatory Challenges 16 1.9 Obstacles to Blockchain Regulation 16 1.10 The Current Regulatory Landscape 17 1.11 The Future of Blockchain Regulation 18 1.12 Business Model Challenges 19 1.12.1 Traditional Business Models 19 1.12.2 Manufacturer 19 1.12.3 Distributor 20 1.12.4 Retailer 20 1.12.5 Franchise 20 1.13 Utility Token Model 20 1.13.1 Right 21 1.13.2 Value Exchange 21 1.13.3 Toll 21 1.13.4 Function 21 1.13.5 Currency 22 1.13.6 Earning 22 1.14 Blockchain as a Service 22 1.15 Securities 23 1.16 Development Platforms 24 1.17 Scandals and Public Perceptions 25 1.17.1 Privacy Limitations 26 1.17.2 Lack of Regulations and Governance 26 1.17.3 Cost to Set Up 26 1.17.4 Huge Consumption of Energy 26 1.17.5 Public Perception 27 References 27 2 The Scope for Blockchain Ecosystem 29Manisha Suri 2.1 Introduction 30 2.2 Blockchain as Game Changer for Environment 32 2.3 Blockchain in Business Ecosystem 38 2.3.1 Business Ecosystem 39 2.3.1.1 What Is Business Model? 39 2.3.1.2 Business Model—Traditional 39 2.3.2 Are Blockchain Business Models Really Needed? 41 2.3.2.1 Blockchain Business Model 41 2.3.2.2 Model 1: Utility Token Model 41 2.3.2.3 Model 2: BaaS 43 2.3.2.4 Model 3: Securities 44 2.3.2.5 Model 4: Development Platforms 45 2.3.2.6 Model 5: Blockchain-Based Software Products 46 2.3.2.7 Model 6: Blockchain Professional Services 46 2.3.2.8 Model 7: Business Model—P2P 47 2.4 Is Blockchain Business Ecosystem Profitable? 48 2.5 How Do You “Design” a Business Ecosystem? 49 2.6 Redesigning Future With Blockchain 53 2.6.1 Is Earth Prepared for Blockchain? 53 2.7 Challenges and Opportunities 57 References 58 3 Business Use Cases of Blockchain Technology 59Vasudha Arora, Shweta Mongia, Sugandha Sharma and Shaveta Malik 3.1 Introduction to Cryptocurrency 60 3.2 What is a Bitcoin? 60 3.2.1 Bitcoin Transactions and Their Processing 62 3.2.2 Double Spending Problem 65 3.2.3 Bitcoin Mining 67 3.3 Bitcoin ICO 69 3.3.1 ICO Token 69 3.3.2 How to Participate in ICO 70 3.3.3 Types of Tokens 71 3.4 Advantages and Disadvantages of ICO 72 3.5 Merchant Acceptance of Bitcoin 73 References 75 4 Ethereum 77Shaveta Bhatia and S.S Tyagi 4.1 Introduction 78 4.2 Basic Features of Ethereum 78 4.3 Difference between Bitcoin and Ethereum 79 4.4 EVM (Ethereum Virtual Machine) 82 4.5 Gas 85 4.5.1 Gas Price Chart 85 4.6 Applications Built on the Basis of Ethereum 86 4.7 ETH 87 4.7.1 Why Users Want to Buy ETH? 87 4.7.2 How to Buy ETH? 88 4.7.3 Alternate Way to Buy ETH 88 4.7.4 Conversion of ETH to US Dollar 89 4.8 Smart Contracts 90 4.8.1 Government 90 4.8.2 Management 91 4.8.3 Benefits of Smart Contracts 91 4.8.4 Problems With Smart Contracts 92 4.8.5 Solution to Overcome This Problem 92 4.8.6 Languages to Build Smart Contracts 92 4.9 DApp (Decentralized Application or Smart Contract) 93 4.9.1 DApp in Ethereum 93 4.9.2 Applications of DApps 93 4.10 Conclusion 95 References 95 5 E-Wallet 97Ms. Vishawjyoti 5.1 Overview of Wallet Technology 97 5.2 Types of Wallet 98 5.2.1 Paper 98 5.2.2 Physical Bitcoins 99 5.2.3 Mobile 99 5.2.4 Web 100 5.2.5 Desktop 100 5.2.6 Hardware 100 5.2.7 Bank 101 5.3 Security of Bitcoin Wallets 101 5.4 Workings of Wallet Technology 101 5.5 Create HD Wallet From Seed 102 5.5.1 Initiation 103 5.5.2 Steps for Creating an HD Wallet From a 24-Word Seed Phrase Through Particl-qt Tool 104 5.5.3 Steps for Encrypting the HD Wallet 106 5.5.4 Utilization 108 5.5.5 Steps for Generating Address to Access Transactions on the HD Wallet 108 5.6 Navigating HD Wallet 109 5.7 Conclusion 110 References 110 6 Blockchain and Governance: Theory, Applications and Challenges 113Bhavya Ahuja Grover, Bhawna Chaudhary, Nikhil Kumar Rajput and Om Dukiya 6.1 Introduction 114 6.2 Governance: Centralized vs Decentralized 115 6.3 Blockchain’s Features Supportive of Decentralization 117 6.4 Noteworthy Application Areas for Blockchain-Based Governance 119 6.4.1 Public Service Governance 119 6.4.2 Knowledge and Shared Governance 121 6.4.3 Governance in Supply Chain 123 6.4.4 Governance of Foreign Aid 124 6.4.5 Environmental Governance 125 6.4.6 Corporate Governance 126 6.4.7 Economic Governance 128 6.5 Scopes and Challenges 128 6.6 Conclusion 136 References 137 7 Blockchain-Based Identity Management 141Abhishek Bhattacharya 7.1 Introduction 141 7.2 Existing Identity Management Systems and Their Challenges 142 7.3 Concept of Decentralized Identifiers 144 7.4 The Workflow of Blockchain Identity Management Systems 145 7.5 How Does it Contribute to Data Security? 148 7.6 Trending Blockchain Identity Management Projects 150 7.7 Why and How of Revocation 152 7.8 Points to Ponder 154 7.8.1 Comparison Between Traditional and Blockchain-Based Identity Management Systems 156 7.9 Conclusion 157 References 158 8 Blockchain & IoT: A Paradigm Shift for Supply Chain Management 159Abhishek Bhattacharya 8.1 Introduction 159 8.2 Supply Chain Management 160 8.2.1 The Aspects of a Supply Chain 161 8.2.2 Supply Chain Performance Dimensions 162 8.2.3 Supply Chain Migration Towards Digitalization 163 8.3 Blockchain and IoT 164 8.3.1 What Makes Blockchain Suitable for SCM? 166 8.3.1.1 Shared Ledger 167 8.3.1.2 Permissions 168 8.3.1.3 Consensus 168 8.3.1.4 Smart Contracts 169 8.3.2 The Role of Blockchain in Achieving the SCM Performance Dimensions 170 8.3.3 The Role of IoT in the Implementation of Blockchain Technology 171 8.4 Blockchain Technology and IoT Use Cases in Supply Chain Management 172 8.5 Benefits and Challenges in Blockchain-Based Supply Chain Management 173 8.6 Conclusion 176 References 176 9 Blockchain-Enabled Supply Chain Management System 179Sonal Pathak 9.1 Introduction 180 9.1.1 Supply Chain Management 180 9.2 Blockchain Technology 184 9.3 Blockchain Technology in Supply Chain Management 186 9.4 Elements of Blockchain That Affects Supply Chain 190 9.4.1 Bitcoin 195 9.5 Challenges in Implementation of Blockchain-Enabled Supply Chain 197 9.6 Conclusion 197 References 199 10 Security Concerns of Blockchain 201Neha Jain and Kamiya Chugh 10.1 Introduction: Security Concerns of Blockchain 201 10.2 Cryptocurrencies Scenarios 202 10.3 Privacy Challenges of Blockchain 203 10.3.1 Protection Problems in Blockchain 203 10.3.2 Privacy-Preserving Mechanisms Analysis 207 10.3.3 Data Anonymization-Mixing 207 10.4 Decentralization in Blockchain 208 10.4.1 Role of Decentralization in Blockchain 209 10.4.2 Analysis of PoS and DPoS 210 10.4.3 Problems With Decentralization 210 10.4.4 Decentralization Recovery Methods 212 10.5 Legal and Regulatory Issues in Blockchain 213 10.5.1 Legal Value of Blockchain and its Problems 214 10.6 Smart Contracts 218 10.7 Scandals of Blockchain 220 10.7.1 Blockchain Technologies as Stumbling Blocks to Financial Legitimacy 223 10.8 Is Blockchain the Rise of Trustless Trust? 223 10.8.1 Why Do We Need a System of Trust? 226 10.9 Blockchain Model Challenges 227 References 229 11 Acceptance and Adoption of Blockchain Technology: An Examination of the Security & Privacy Challenges 231Amandeep Dhaliwal and Sahil Malik 11.1 Introduction 231 11.1.1 Research Methodology 233 11.1.2 Analysis 233 11.2 Security Issues of Blockchain 233 11.2.1 The Majority Attack (51% Attacks) 233 11.2.2 The Fork Problems 234 11.2.2.1 Hard Fork 234 11.2.2.2 Soft Fork 235 11.2.3 Scale of Blockchain 235 11.2.4 Time Confirmation of Blockchain Data— Double-Spend Attack/Race Attack 235 11.2.5 Current Regulations Problems 236 11.2.6 Scalability and Storage Capacity 236 11.2.7 DOS Attack/Sybil Attack/Eclipse Attack/Bugs 237 11.2.8 Legal Issues 237 11.2.9 Security of Wallets 238 11.2.10 The Increased Computing Power 238 11.3 Privacy Challenges of Bitcoin 238 11.3.1 De-Anonymization 239 11.3.1.1 Network Analysis 239 11.3.1.2 Address Clustering 239 11.3.1.3 Transaction Finger Printing 240 11.3.2 Transaction Pattern Exposure 240 11.3.2.1 Transaction Graph Analysis 240 11.3.2.2 Autonomous System-Level Deployment Analysis 241 11.4 Blockchain Application-Based Solutions 241 11.4.1 Bitcoins 241 11.4.2 IoT 242 11.4.2.1 MyBit 242 11.4.3 Aero Token 242 11.4.4 The Chain of Things 243 11.4.5 The Modum 243 11.4.6 Twin of Things 243 11.4.7 The Blockchain of Things 244 11.4.8 Blockchain Solutions: Cloud Computing 244 11.5 Conclusion and Future Work 245 References 245 12 Deficiencies in Blockchain Technology and Potential Augmentation in Cyber Security 251Eshan Bajal, Madhulika Bhatia, Lata Nautiyal and Madhurima Hooda 12.1 Introduction 252 12.2 Security Issues in Blockchain Technology 252 12.3 Privacy Challenges 253 12.3.1 BGP Hijacking Attack 255 12.3.2 BDoS (Blockchain Denial of Service) 255 12.3.3 Forcing Other Miners to Stop Mining 256 12.4 Decentralization Challenges 256 12.5 Regulatory Challenges 260 12.5.1 Principles to Follow While Regulating 262 12.5.1.1 Flexible to Legal Innovation 262 12.5.1.2 Experimentation Should be Encouraged 263 12.5.1.3 Focus on the Immediate Implications 264 12.5.1.4 Regulators Should Engage in a Transnational Conversation 264 12.5.2 Regulatory Strategies 265 12.5.2.1 Wait-and-See 265 12.5.2.2 Imposing Narrowing and Broadening Guidance 266 12.5.2.3 Sandboxing 266 12.5.2.4 Issue a New Legislation 267 12.5.2.5 Use Blockchain in Regulation 268 12.6 Business Model Challenges 269 12.7 Scandals and Public Perception 271 12.8 Why Blockchain is Trustless 277 12.8.1 Trust Mechanism 278 12.8.2 Anonymity 279 12.8.3 Use in Digital Wallets 279 12.8.4 Forgery Resistance 279 12.9 Use of Blockchain in Cybersecurity 280 12.9.1 Blockchain Database 281 12.9.2 DNS Security 283 12.9.3 IoT Security 283 12.9.4 DDoS Prevention 286 12.9.5 CDN (Content Delivery Network) 286 12.9.6 SMS Authentication 287 References 288 13 Internet of Things and Blockchain 295Priyanka Sharma 13.1 History of ‘Internet of Things’ 296 13.2 IoT Devices 298 13.3 Sensors and Actuators 302 13.4 Cloud and Haze-Based Engineering 307 13.5 Blockchain and IoT 315 13.6 Edge Computing 321 13.7 Contextual Analyses 324 13.8 Fate of Blockchain and IoT 332 References 332 14 Blockchain Applications 337Boby Singh, Rohit Pahwa, Hari Om Tanwar and Nikita Gupta 14.1 Introduction to Blockchain 337 14.1.1 Uses of Blockchain in Administration 339 14.2 Blockchain in Big Data Predictive Task Automation 340 14.2.1 How Can Blockchain Help Big Data? 341 14.2.2 Blockchain Use Cases in Big Data 341 14.3 Digital Identity Verification 342 14.3.1 Why Digital Identity Matters? 343 14.3.2 Blockchain (Definition and its Features) 343 14.3.3 Why do we Need Blockchain in Digital Identity? 344 14.3.4 How Does a Blockchain Works? 345 14.3.5 Why is a Blockchain Secure? 345 14.3.6 What’s Blockchain Identification Management? 346 14.3.7 Advantages 347 14.4 Blockchain Government 348 14.4.1 Decentralized Government Services 349 14.4.2 Liquid Democracy and Random Sample Election 350 14.5 Blockchain Science 351 14.5.1 FoldingCoin 351 14.5.2 GridCoin (GRC) 352 14.5.3 Global Public Health 353 14.5.4 Bitcoin Genomics 354 14.6 Blockchain Health 355 14.6.1 Health Coin 355 14.6.2 EMR on Blockchain 355 14.6.3 Bit Coin Health Notary 356 14.7 Blockchain Learning 357 14.7.1 Bitcoin MOOCs 357 14.7.2 Smart Contract Literacy 357 14.7.3 LearnCoin 359 References 359 15 Advance Concepts of Blockchain 361Raj Kumar 15.1 Community Supercomputing 361 15.2 Blockchain Genomics 364 15.3 Blockchain Learning 365 15.4 Community Coin 366 15.4.1 Monetary and Non-Monetary Currencies 367 15.4.2 Difference Between Monetary and Non-Monetary Assets 369 15.4.3 Currency Multiplicity 369 15.4.4 List of Some Prominent Alternate Coins is Given Below 370 15.5 Demurrage Currencies 371 Reading List 371 Index 373

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Book SynopsisTable of ContentsPreface xv 1 Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media 1Ravi Kishore Devarapalli and Anupam Biswas 1.1 Introduction 2 1.2 Social Networks 4 1.2.1 Types of Social Networks 4 1.3 What is Cyber-Crime? 7 1.3.1 Definition 7 1.3.2 Types of Cyber-Crimes 7 1.3.2.1 Hacking 7 1.3.2.2 Cyber Bullying 7 1.3.2.3 Buying Illegal Things 8 1.3.2.4 Posting Videos of Criminal Activity 8 1.3.3 Cyber-Crimes on Social Networks 8 1.4 Rumor Detection 9 1.4.1 Models 9 1.4.1.1 Naïve Bayes Classifier 10 1.4.1.2 Support Vector Machine 13 1.4.2 Combating Misinformation on Instagram 14 1.5 Factors to Detect Rumor Source 15 1.5.1 Network Structure 15 1.5.1.1 Network Topology 16 1.5.1.2 Network Observation 16 1.5.2 Diffusion Models 18 1.5.2.1 SI Model 18 1.5.2.2 SIS Model 19 1.5.2.3 SIR Model 19 1.5.2.4 SIRS Model 20 1.5.3 Centrality Measures 21 1.5.3.1 Degree Centrality 21 1.5.3.2 Closeness Centrality 21 1.5.3.3 Betweenness Centrality 22 1.6 Source Detection in Network 22 1.6.1 Single Source Detection 23 1.6.1.1 Network Observation 23 1.6.1.2 Query-Based Approach 25 1.6.1.3 Anti-Rumor-Based Approach 26 1.6.2 Multiple Source Detection 26 1.7 Conclusion 27 References 28 2 Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction 31Jaiprakash Narain Dwivedi 2.1 Introduction 32 2.2 Advancement of Internet 33 2.3 Internet of Things (IoT) and Machine to Machine (M2M) Communication 34 2.4 A Definition of Security Frameworks 38 2.5 M2M Devices and Smartphone Technology 39 2.6 Explicit Hazards to M2M Devices Declared by Smartphone Challenges 41 2.7 Security and Privacy Issues in IoT 43 2.7.1 Dynamicity and Heterogeneity 43 2.7.2 Security for Integrated Operational World with Digital World 44 2.7.3 Information Safety with Equipment Security 44 2.7.4 Data Source Information 44 2.7.5 Information Confidentiality 44 2.7.6 Trust Arrangement 44 2.8 Protection in Machine to Machine Communication 48 2.9 Use Cases for M2M Portability 52 2.10 Conclusion 53 References 54 3 Crime Predictive Model Using Big Data Analytics 57Hemanta Kumar Bhuyan and Subhendu Kumar Pani 3.1 Introduction 58 3.1.1 Geographic Information System (GIS) 59 3.2 Crime Data Mining 60 3.2.1 Different Methods for Crime Data Analysis 62 3.3 Visual Data Analysis 63 3.4 Technological Analysis 65 3.4.1 Hadoop and MapReduce 65 3.4.1.1 Hadoop Distributed File System (HDFS) 65 3.4.1.2 MapReduce 65 3.4.2 Hive 67 3.4.2.1 Analysis of Crime Data using Hive 67 3.4.2.2 Data Analytic Module With Hive 68 3.4.3 Sqoop 68 3.4.3.1 Pre-Processing and Sqoop 68 3.4.3.2 Data Migration Module With Sqoop 68 3.4.3.3 Partitioning 68 3.4.3.4 Bucketing 68 3.4.3.5 R-Tool Analyse Crime Data 69 3.4.3.6 Correlation Matrix 69 3.5 Big Data Framework 69 3.6 Architecture for Crime Technical Model 72 3.7 Challenges 73 3.8 Conclusions 74 References 75 4 The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks 79Sushobhan Majumdar 4.1 Introduction 80 4.2 Database and Methods 81 4.3 Discussion and Analysis 82 4.4 Role of Remote Sensing and GIS 83 4.5 Cartographic Model 83 4.5.1 Spatial Data Management 85 4.5.2 Battlefield Management 85 4.5.3 Terrain Analysis 86 4.6 Mapping Techniques Used for Defense Purposes 87 4.7 Naval Operations 88 4.7.1 Air Operations 89 4.7.2 GIS Potential in Military 89 4.8 Future Sphere of GIS in Military Science 89 4.8.1 Defense Site Management 90 4.8.2 Spatial Data Management 90 4.8.3 Intelligence Capability Approach 90 4.8.4 Data Converts Into Information 90 4.8.5 Defense Estate Management 91 4.9 Terrain Evolution 91 4.9.1 Problems Regarding the Uses of Remote Sensing and GIS 91 4.9.2 Recommendations 92 4.10 Conclusion 92 References 93 5 Text Mining for Secure Cyber Space 95Supriya Raheja and Geetika Munjal 5.1 Introduction 95 5.2 Literature Review 97 5.2.1 Text Mining With Latent Semantic Analysis 100 5.3 Latent Semantic Analysis 101 5.4 Proposed Work 102 5.5 Detailed Work Flow of Proposed Approach 104 5.5.1 Defining the Stop Words 106 5.5.2 Stemming 107 5.5.3 Proposed Algorithm: A Hybrid Approach 109 5.6 Results and Discussion 111 5.6.1 Analysis Using Hybrid Approach 111 5.7 Conclusion 115 References 115 6 Analyses on Artificial Intelligence Framework to Detect Crime Pattern 119R. Arshath Raja, N. Yuvaraj and N.V. Kousik 6.1 Introduction 120 6.2 Related Works 121 6.3 Proposed Clustering for Detecting Crimes 122 6.3.1 Data Pre-Processing 123 6.3.2 Object-Oriented Model 124 6.3.3 MCML Classification 124 6.3.4 GAA 124 6.3.5 Consensus Clustering 124 6.4 Performance Evaluation 124 6.4.1 Precision 125 6.4.2 Sensitivity 125 6.4.3 Specificity 131 6.4.4 Accuracy 131 6.5 Conclusions 131 References 132 7 A Biometric Technology-Based Framework for Tackling and Preventing Crimes 133Ebrahim A.M. Alrahawe, Vikas T. Humbe and G.N. Shinde 7.1 Introduction 134 7.2 Biometrics 135 7.2.1 Biometric Systems Technologies 137 7.2.2 Biometric Recognition Framework 141 7.2.3 Biometric Applications/Usages 142 7.3 Surveillance Systems (CCTV) 144 7.3.1 CCTV Goals 146 7.3.2 CCTV Processes 146 7.3.3 Fusion of Data From Multiple Cameras 149 7.3.4 Expanding the Use of CCTV 149 7.3.5 CCTV Effectiveness 150 7.3.6 CCTV Limitations 150 7.3.7 Privacy and CCTV 150 7.4 Legality to Surveillance and Biometrics vs. Privacy and Human Rights 151 7.5 Proposed Work (Biometric-Based CCTV System) 153 7.5.1 Biometric Surveillance System 154 7.5.1.1 System Component and Flow Diagram 154 7.5.2 Framework 156 7.6 Conclusion 158 References 159 8 Rule-Based Approach for Botnet Behavior Analysis 161Supriya Raheja, Geetika Munjal, Jyoti Jangra and Rakesh Garg 8.1 Introduction 161 8.2 State-of-the-Art 163 8.3 Bots and Botnets 166 8.3.1 Botnet Life Cycle 166 8.3.2 Botnet Detection Techniques 167 8.3.3 Communication Architecture 168 8.4 Methodology 171 8.5 Results and Analysis 175 8.6 Conclusion and Future Scope 177 References 177 9 Securing Biometric Framework with Cryptanalysis 181Abhishek Goel, Siddharth Gautam, Nitin Tyagi, Nikhil Sharma and Martin Sagayam 9.1 Introduction 182 9.2 Basics of Biometric Systems 184 9.2.1 Face 185 9.2.2 Hand Geometry 186 9.2.3 Fingerprint 187 9.2.4 Voice Detection 187 9.2.5 Iris 188 9.2.6 Signature 189 9.2.7 Keystrokes 189 9.3 Biometric Variance 192 9.3.1 Inconsistent Presentation 192 9.3.2 Unreproducible Presentation 192 9.3.3 Fault Signal/Representational Accession 193 9.4 Performance of Biometric System 193 9.5 Justification of Biometric System 195 9.5.1 Authentication (“Is this individual really the authenticate user or not?”) 195 9.5.2 Recognition (“Is this individual in the database?”) 196 9.5.3 Concealing (“Is this a needed person?”) 196 9.6 Assaults on a Biometric System 196 9.6.1 Zero Effort Attacks 197 9.6.2 Adversary Attacks 198 9.6.2.1 Circumvention 198 9.6.2.2 Coercion 198 9.6.2.3 Repudiation 198 9.6.2.4 DoB (Denial of Benefit) 199 9.6.2.5 Collusion 199 9.7 Biometric Cryptanalysis: The Fuzzy Vault Scheme 199 9.8 Conclusion & Future Work 203 References 205 10 The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates 209Galal A. AL-Rummana, Abdulrazzaq H. A. Al-Ahdal and G.N. Shinde 10.1 Introduction: An Overview of Big Data and Cyber Crime 210 10.2 Techniques for the Analysis of BigData 211 10.3 Important Big Data Security Techniques 216 10.4 Conclusion 219 References 219 11 Crime Pattern Detection Using Data Mining 221Dipalika Das and Maya Nayak 11.1 Introduction 221 11.2 Related Work 222 11.3 Methods and Procedures 224 11.4 System Analysis 227 11.5 Analysis Model and Architectural Design 230 11.6 Several Criminal Analysis Methods in Use 233 11.7 Conclusion and Future Work 235 References 235 12 Attacks and Security Measures in Wireless Sensor Network 237Nikhil Sharma, Ila Kaushik, Vikash Kumar Agarwal, Bharat Bhushan and Aditya Khamparia 12.1 Introduction 238 12.2 Layered Architecture of WSN 239 12.2.1 Physical Layer 239 12.2.2 Data Link Layer 239 12.2.3 Network Layer 240 12.2.4 Transport Layer 240 12.2.5 Application Layer 241 12.3 Security Threats on Different Layers in WSN 241 12.3.1 Threats on Physical Layer 241 12.3.1.1 Eavesdropping Attack 241 12.3.1.2 Jamming Attack 242 12.3.1.3 Imperil or Compromised Node Attack 242 12.3.1.4 Replication Node Attack 242 12.3.2 Threats on Data Link Layer 242 12.3.2.1 Collision Attack 243 12.3.2.2 Denial of Service (DoS) Attack 243 12.3.2.3 Intelligent Jamming Attack 243 12.3.3 Threats on Network Layer 243 12.3.3.1 Sybil Attack 243 12.3.3.2 Gray Hole Attack 243 12.3.3.3 Sink Hole Attack 244 12.3.3.4 Hello Flooding Attack 244 12.3.3.5 Spoofing Attack 244 12.3.3.6 Replay Attack 244 12.3.3.7 Black Hole Attack 244 12.3.3.8 Worm Hole Attack 245 12.3.4 Threats on Transport Layer 245 12.3.4.1 De-Synchronization Attack 245 12.3.4.2 Flooding Attack 245 12.3.5 Threats on Application Layer 245 12.3.5.1 Malicious Code Attack 245 12.3.5.2 Attack on Reliability 246 12.3.6 Threats on Multiple Layer 246 12.3.6.1 Man-in-the-Middle Attack 246 12.3.6.2 Jamming Attack 246 12.3.6.3 Dos Attack 246 12.4 Threats Detection at Various Layers in WSN 246 12.4.1 Threat Detection on Physical Layer 247 12.4.1.1 Compromised Node Attack 247 12.4.1.2 Replication Node Attack 247 12.4.2 Threat Detection on Data Link Layer 247 12.4.2.1 Denial of Service Attack 247 12.4.3 Threat Detection on Network Layer 248 12.4.3.1 Black Hole Attack 248 12.4.3.2 Worm Hole Attack 248 12.4.3.3 Hello Flooding Attack 249 12.4.3.4 Sybil Attack 249 12.4.3.5 Gray Hole Attack 250 12.4.3.6 Sink Hole Attack 250 12.4.4 Threat Detection on the Transport Layer 251 12.4.4.1 Flooding Attack 251 12.4.5 Threat Detection on Multiple Layers 251 12.4.5.1 Jamming Attack 251 12.5 Various Parameters for Security Data Collection in WSN 252 12.5.1 Parameters for Security of Information Collection 252 12.5.1.1 Information Grade 252 12.5.1.2 Efficacy and Proficiency 253 12.5.1.3 Reliability Properties 253 12.5.1.4 Information Fidelity 253 12.5.1.5 Information Isolation 254 12.5.2 Attack Detection Standards in WSN 254 12.5.2.1 Precision 254 12.5.2.2 Germane 255 12.5.2.3 Extensibility 255 12.5.2.4 Identifiability 255 12.5.2.5 Fault Forbearance 255 12.6 Different Security Schemes in WSN 256 12.6.1 Clustering-Based Scheme 256 12.6.2 Cryptography-Based Scheme 256 12.6.3 Cross-Checking-Based Scheme 256 12.6.4 Overhearing-Based Scheme 257 12.6.5 Acknowledgement-Based Scheme 257 12.6.6 Trust-Based Scheme 257 12.6.7 Sequence Number Threshold-Based Scheme 258 12.6.8 Intrusion Detection System-Based Scheme 258 12.6.9 Cross-Layer Collaboration-Based Scheme 258 12.7 Conclusion 264 References 264 13 Large Sensing Data Flows Using Cryptic Techniques 269Hemanta Kumar Bhuyan 13.1 Introduction 270 13.2 Data Flow Management 271 13.2.1 Data Flow Processing 271 13.2.2 Stream Security 272 13.2.3 Data Privacy and Data Reliability 272 13.2.3.1 Security Protocol 272 13.3 Design of Big Data Stream 273 13.3.1 Data Stream System Architecture 273 13.3.1.1 Intrusion Detection Systems (IDS) 274 13.3.2 Malicious Model 275 13.3.3 Threat Approaches for Attack Models 276 13.4 Utilization of Security Methods 277 13.4.1 System Setup 278 13.4.2 Re-Keying 279 13.4.3 New Node Authentication 279 13.4.4 Cryptic Techniques 280 13.5 Analysis of Security on Attack 280 13.6 Artificial Intelligence Techniques for Cyber Crimes 281 13.6.1 Cyber Crime Activities 282 13.6.2 Artificial Intelligence for Intrusion Detection 282 13.6.3 Features of an IDPS 284 13.7 Conclusions 284 References 285 14 Cyber-Crime Prevention Methodology 291Chandra Sekhar Biswal and Subhendu Kumar Pani 14.1 Introduction 292 14.1.1 Evolution of Cyber Crime 294 14.1.2 Cybercrime can be Broadly Defined as Two Types 296 14.1.3 Potential Vulnerable Sectors of Cybercrime 296 14.2 Credit Card Frauds and Skimming 297 14.2.1 Matrimony Fraud 297 14.2.2 Juice Jacking 298 14.2.3 Technicality Behind Juice Jacking 299 14.3 Hacking Over Public WiFi or the MITM Attacks 299 14.3.1 Phishing 300 14.3.2 Vishing/Smishing 302 14.3.3 Session Hijacking 303 14.3.4 Weak Session Token Generation/Predictable Session Token Generation 304 14.3.5 IP Spoofing 304 14.3.6 Cross-Site Scripting (XSS) Attack 305 14.4 SQLi Injection 306 14.5 Denial of Service Attack 307 14.6 Dark Web and Deep Web Technologies 309 14.6.1 The Deep Web 309 14.6.2 The Dark Web 310 14.7 Conclusion 311 References 312 Index 313

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Book SynopsisINTEGRATION OF RENEWABLE ENERGY SOURCES WITH SMART GRID Provides comprehensive coverage of renewable energy and its integration with smart grid technologies. This book starts with an overview of renewable energy technologies, smart grid technologies, and energy storage systems and covers the details of renewable energy integration with smart grid and the corresponding controls. It also provides an enhanced perspective on the power scenario in developing countries. The requirement of the integration of smart grid along with the energy storage systems is deeply discussed to acknowledge the importance of sustainable development of a smart city. The methodologies are made quite possible with highly efficient power convertor topologies and intelligent control schemes. These control schemes are capable of providing better control with the help of machine intelligence techniques and artificial intelligence. The book also addresses modern power convertor topologies and theTable of ContentsPreface xv 1 Renewable Energy Technologies 1V. Chamundeswari, R. Niraimathi, M. Shanthi and A. Mahaboob Subahani 1. Introduction 1 1.1 Types of Renewable Energy 2 1.1.1 Solar Energy 3 1.1.2 Wind Energy 7 1.1.3 Fuel Cell 8 1.1.4 Biomass Energy 11 1.1.5 Hydro-Electric Energy 13 1.1.6 Geothermal Energy 14 References 17 2 Present Power Scenario in India 19Niraimathi R., Pradeep V., Shanthi M. and Kathiresh M. 2.1 Introduction 20 2.2 Thermal Power Plant 20 2.2.1 Components of Thermal Power Plant 21 2.2.2 Major Thermal Power Plants in India 23 2.3 Gas-Based Power Generation 24 2.3.1 Basics of Gas-Based Power Generation 24 2.3.2 Major Gas-Based Power Plants in India 25 2.4 Nuclear Power Plants 26 2.4.1 India’s Hold in Nuclear Power 27 2.4.2 Major Nuclear Power Plants 27 2.4.3 Currently Operational Nuclear Power Plants 28 2.4.4 Challenges of Nuclear Power Plants 28 2.5 Hydropower Generation 29 2.5.1 Pumped Storage Plants 29 2.6 Solar Power 30 2.6.1 Photovoltaic 30 2.6.2 Photovoltaic Solar Power System 30 2.6.3 Concentrated Solar Power System 31 2.6.4 Major Solar Parks in India 32 2.7 Wind Energy 32 2.8 The Inherited Structure 34 References 34 3 Introduction to Smart Grid 37G. R. Hemanth, S. Charles Raja and P. Venkatesh 3.1 Need for Smart Grid in India 38 3.2 Present Power Scenario in India 38 3.2.1 Performance of Generation From Conventional Sources 40 3.2.2 Status of Renewable Energy Sources 40 3.3 Electric Grid 43 3.3.1 Evolving Scenario of the Electric Grid 45 3.3.1.1 Integrated Grid 46 3.3.1.2 Prosumers 46 3.3.1.3 Transmission v/s Energy Storage 47 3.3.1.4 Changing Nature of Loads 47 3.3.1.5 Electric Vehicles 48 3.3.1.6 Microgrids 48 3.4 Overview of Smart Grids 49 3.4.1 Purpose of Smart Grid 49 3.5 Smart Grid Components for Transmission System 50 3.5.1 Supervisory Control and Data Acquisition System 50 3.5.1.1 SCADA Overview 51 3.5.1.2 Components of SCADA 51 3.5.2 Energy Management System 52 3.5.3 Wide-Area Monitoring System 52 3.6 Smart Grid Functions Used in Distribution System 53 3.6.1 Supervisory Control and Data Acquisition System 53 3.6.2 Distribution Management System 54 3.6.3 Distribution Automation 54 3.6.4 Substation Automation 55 3.6.5 Advanced Metering Infrastructure 55 3.6.6 Geographical Information System 57 3.6.7 Peak Load Management 58 3.6.8 Demand Response 58 3.6.9 Power Quality Management 59 3.6.10 Outage Management System 59 3.6.11 Distribution Transformer Monitoring System 59 3.6.12 Enterprise Application Integration 59 3.6.13 Smart Street Lights 60 3.6.14 Energy Storage 60 3.6.15 Cyber Security 60 3.6.16 Analytics 60 3.7 Case Study: Techno-Economic Analysis 61 3.7.1 Peak Load Shaving and Metering Efficiency 61 3.7.2 Outage Management System 63 3.7.3 Loss Detection 64 3.7.4 Tamper Analysis 66 3.8 Case Study: Solar PV Awareness of Puducherry SG Pilot Project 69 3.9 Recent Trends in Smart Grids 70 3.9.1 Smart GRIP Architecture 70 3.9.2 Implementation of Smart Meter With Prepaid Facility 74 References 74 4 Internet of Things–Based Advanced Metering Infrastructure (AMI) for Smart Grids 77V. Gomathy, V. Kavitha, C. Nayantara, J. Mohammed Feros Khan, Vimalarani G. and S. Sheeba Rani 4.1 Introduction 78 4.1.1 Smart Grids 78 4.1.2 Smart Meters 80 4.2 Advanced Metering Infrastructure 81 4.2.1 Smart Devices 82 4.2.2 Communication 83 4.2.3 Data Management System 85 4.2.4 Mathematical Modeling 87 4.2.5 Energy Theft Detection Techniques 89 4.3 IoT-Based Advanced Metering Infrastructure 89 4.3.1 Intrusion Detection System 90 4.4 Results 93 4.5 Discussion 94 4.6 Conclusion and Future Scope 97 References 97 5 Requirements for Integrating Renewables With Smart Grid 101Indrajit Sarkar 5.1 Introduction 102 5.1.1 Smart Grid 102 5.1.2 Renewable Energy Resources 105 5.1.3 How Smart Grids Enable Renewables 111 5.1.4 Smart Grid and Distributed Generation 111 5.1.5 Grid Integration Terminologies 112 5.2 Challenges in Integrating Renewables Into Smart Grid 112 5.2.1 The Power Flow Control of Distributed Energy Resources 113 5.2.2 Investments on New Renewable Energy Generations 113 5.2.3 Transmission Expansion 114 5.2.4 Improved Flexibility 114 5.2.5 High Penetration of Renewables in Future 115 5.2.6 Standardizing Control of ESS 115 5.2.7 Regulations 116 5.2.8 Standards 116 5.3 Conclusion 116 References 117 6 Grid Energy Storage Technologies 119Chandra Sekhar Nalamati 6.1 Introduction 120 6.1.1 Need of Energy Storage System 121 6.1.2 Services Provided by Energy Storage System 122 6.2 Grid Energy Storage Technologies: Classification 123 6.2.1 Pumped Hydro Storage System 123 6.2.2 Compressed Air Storage System 124 6.2.3 Flywheel Energy Storage System 125 6.2.4 Superconducting Magnet Storage System 125 6.2.5 Battery Storage System 127 6.2.6 Capacitors and Super Capacitor Storage System 129 6.2.7 Fuel Cell Energy Storage System 130 6.2.8 Thermal Storage System 131 6.3 Grid Energy Storage Technologies: Analogy 132 6.4 Applications of Energy Storage System 135 6.5 Power Conditioning of Energy Storage System 136 6.6 Conclusions 136 References 137 7 Multi-Mode Power Converter Topology for Renewable Energy Integration With Smart Grid 141M. Sathiyanathan, S. Jaganathan and R. L. Josephine 7.1 Introduction 142 7.2 Literature Survey 144 7.3 System Architecture 145 7.3.1 Solar PV Array 146 7.3.2 Wind Energy Generator 147 7.4 Modes of Operation of Multi-Mode Power Converter 149 7.4.1 Buck Mode 150 7.4.2 Boost Mode 152 7.4.3 Bi-Directional Mode 155 7.5 Control Scheme 158 7.5.1 Mode Selection 159 7.5.2 Maximum Power Point Tracking 159 7.5.3 Reconfigurable SPWM Generation 161 7.6 Results and Discussion 163 7.7 Conclusion 167 References 168 8 Decoupled Control With Constant DC Link Voltage for PV-Fed Single-Phase Grid Connected Systems 171C. Maria Jenisha 8.1 Introduction 171 8.2 Schematic of the Grid-Tied Solar PV System 173 8.2.1 DC Link Voltage Controller 175 8.2.2 MPPT Controller 176 8.2.3 SPWM-Based dq Controller 176 8.3 Simulation and Experimental Results of the Grid Tied Solar PV System 178 8.4 Conclusion 183 References 184 9 Wind Energy Conversion System Feeding Remote Microgrid 187K. Arthishri and N. Kumaresan 9.1 Introduction 188 9.2 Literature Review 189 9.3 Direct Grid Connected Configurations of Three-Phase WDIG Feeding Single-Phase Grid 191 9.4 Three-Phase WDIG Feeding Single-Phase Grid With Power Converters 191 9.5 Performance of the Three-Phase Wind Generator System Feeding Power to Single-Phase Grid 193 9.5.1 Wind Turbine Characteristics 193 9.5.2 Generator Analysis 194 9.6 Power Converter Configurations 198 9.6.1 Configuration 1: WDIG With Uncontrolled Rectifier–Line Commutated Inverter 198 9.6.2 Configuration 2: WDIG With Uncontrolled Rectifier–(DC-DC)–Line Commutated Inverter 200 9.6.2.1 Closed-Loop Operation of UR-DC/DC-LCI Configuration 200 9.6.3 Configuration 3: WDIG With Uncontrolled Rectifier–Voltage Source Inverter 201 9.6.3.1 Closed-Loop Operation of UR-VSI Configuration 202 9.7 Conclusion 204 References 204 10 Microgrid Protection 209Suman M., Srividhya S. and Padmagirisan P. 10.1 Introduction 209 10.2 Necessity of Distributed Energy Resources 210 10.3 Concept of Microgrid 210 10.4 Why the Protection With Microgrid is Different From the Conventional Distribution System Protection 211 10.4.1 Role of the Type of DER on Protection 212 10.5 Foremost Challenges in Microgrid Protection 212 10.5.1 Relay Blinding 212 10.5.2 Variations in Fault Current Level 213 10.5.3 Selectivity 214 10.5.4 False/Unnecessary Tripping 214 10.5.5 Loss of Mains (Islanding Condition) 214 10.6 Microgrid Protection 215 10.6.1 Overcurrent Protection 215 10.6.2 Distance Protection 216 10.6.2.1 Effect of Distributed Generator Inclusion in the Distribution System on Distance Relay 218 10.6.3 Differential Protection 219 10.6.3.1 Drawbacks in Differential Protection 220 10.6.4 Hybrid Tripping Relay Characteristic 220 10.6.5 Voltage-Based Methods 221 10.6.6 Adaptive Protection Methods 222 10.7 Literature Survey 223 10.8 Comparison of Various Existing Protection Schemes for Microgrids 225 10.9 Loss of Mains (Islanding) 225 10.10 Necessity to Detect the Unplanned Islanding 227 10.10.1 Health Hazards to Maintenance Personnel 227 10.10.2 Unsynchronized Reclosing 228 10.10.3 Ineffective Grounding 228 10.10.4 Inept Protection 229 10.10.5 Loss of Voltage and Frequency Control 229 10.11 Unplanned Islanding Identification Methods 229 10.11.1 Communication-Based Methods (Remote Method) 230 10.11.2 Non-Communication–Based Methods (Local Method) 230 10.11.2.1 Passive Method 230 10.11.2.2 Active Method 231 10.11.2.3 Hybrid Method 232 10.12 Comparison of Unplanned Islanding Identification Methods 234 10.13 Discussion 234 10.14 Conclusion 235 References 235 11 Microgrid Optimization and Integration of Renewable Energy Resources: Innovation, Challenges and Prospects 239Blesslin Sheeba T., G. Jims John Wessley, Kanagaraj V., Kamatchi S., A. Radhika and Janeera D.A. 11.1 Introduction 240 11.2 Microgrids 242 11.3 Renewable Energy Sources 245 11.3.1 Renewable Energy Technologies (RETs) 246 11.3.2 Distributed Storage Technologies 247 11.3.3 Combined Heat and Power 248 11.4 Integration of RES in Microgrid 248 11.5 Microgrid Optimization Schemes 250 11.5.1 Load Forecasting Schemes 251 11.5.2 Generation Unit Control 252 11.5.3 Storage Unit Control 252 11.5.4 Data Monitoring and Transmission 253 11.5.4.1 Communication Systems 254 11.5.5 Energy Management and Power Flow 256 11.6 Challenges in Implementation of Microgrids 257 11.7 Future Prospects of Microgrids 259 11.8 Conclusion 259 References 260 12 Challenges in Planning and Operation of Large-Scale Renewable Energy Resources Such as Solar and Wind 263J. Vishnupriyan and A. Dhanasekaran 12.1 Introduction 264 12.2 Solar Grid Integration 265 12.3 Wind Energy Grid Integration 267 12.4 Challenges in the Integration of Renewable Energy Systems with Grid 267 12.4.1 Disturbances in the Grid Side 269 12.4.2 Virtual Synchronous Machine Method 271 12.4.3 Frequency Control 272 12.4.4 Solar Photovoltaic Array in Frequency Regulation 275 12.4.5 Harmonics 275 12.5 Electrical Energy Storage (EES) 276 12.6 Conclusion 277 References 278 13 Mitigating Measures to Address Challenges of Renewable Integration—Forecasting, Scheduling, Dispatch, Balancing, Monitoring, and Control 281K. Latha Maheswari, B. Sathya and A. Maideen Abdhulkader Jeylani 13.1 Introduction 282 13.2 Microgrid 283 13.2.1 Types of Microgrid 284 13.2.1.1 DC Microgrid 284 13.2.1.2 AC Microgrid 285 13.2.1.3 Hybrid AC-DC Microgrid 286 13.3 Large-Scale Integration of Renewables: Issues and Challenges 287 13.4 A Review on Short-Term Load Forecasting Methods 288 13.4.1 Short-Term Load Forecasting Methods 290 13.4.1.1 Statistical Technique 290 13.5 Overview on Control of Microgrid 291 13.5.1 Need for Microgrid Control 291 13.5.2 Fully Centralized Control 292 13.5.3 Decentralized Control 292 13.5.4 Hierarchical Control 293 13.5.4.1 Primary Control 293 13.5.4.2 Secondary Control 295 13.5.4.3 Tertiary Control 295 13.6 Measures to Support Large-Scale Renewable Integration 296 13.6.1 Basic Idea of Preventive Control 297 13.6.1.1 Maximum Output Control Mode 297 13.6.1.2 Output Following Mode 298 References 298 14 Mitigation Measures for Power Quality Issues in Renewable Energy Integration and Impact of IoT in Grid Control 305Hepsiba D., L.D. Vijay Anand, Granty Regina Elwin J., J.B. Shajilin and D. Ruth Anita Shirley 14.1 Introduction 306 14.2 Impact of Power Quality Issues 308 14.2.1 Power Quality in Renewable Energy 314 14.2.2 Power Quality Issues in Wind and Solar Renewable Energy 316 14.2.2.1 Wind Renewable Energy 316 14.2.2.2 Solar Renewable Energy 317 14.3 Mitigation of Power Quality Issues 317 14.3.1 UPQC 317 14.3.2 DVR 318 14.3.3 D-STATCOM 319 14.3.4 UPS 319 14.3.5 TVSS 320 14.3.6 Internet of Things in Distributed Generations Systems 320 14.4 Discussions 321 14.5 Conclusion and Future Scope 322 References 323 15 Smart Grid Implementations and Feasibilities 327Suresh N. S., Padmavathy N. S., S. Arul Daniel and Ramakrishna Kappagantu 15.1 Introduction 328 15.1.1 Smart Grid Technologies—Literature Review 328 15.2 Need for Smart Grid 329 15.2.1 Smart Grid Description 330 15.3 Smart Grid Sensing, Measurement, Control, and Automation Technologies 331 15.3.1 Advanced Metering Infrastructure 332 15.3.2 Key Components of AMI 332 15.3.3 Smart Meter 332 15.3.4 Communication Infrastructure and Protocols for AMI 333 15.3.4.1 Data Concentrator Unit 334 15.3.5 Benefits of AMI 335 15.3.6 Peak Load Management 336 15.3.7 Distribution Management System 336 15.3.8 Distribution Automation System 337 15.4 Implementation of Smart Grid Project 339 15.4.1 Challenges and Issues of SG Implementation 339 15.4.2 Smart Grid Implementation in India: Puducherry Pilot Project 341 15.4.3 Power Quality of the Smart Grid 341 15.5 Solar PV System Implementation Barriers 342 15.6 Smart Grid and Microgrid in Other Areas 343 15.6.1 Maritime Power System 343 15.6.2 Space Electrical Grids 343 15.7 Conclusion 344 References 345 Index 347

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Book SynopsisMACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning anTable of ContentsPreface xiii 1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements 1M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi 1.1 Introduction 2 1.2 Smart City Structure in India 3 1.2.1 Bhubaneswar City 3 1.2.1.1 Specifications 3 1.2.1.2 Healthcare and Mobility Services 3 1.2.1.3 Productivity 4 1.2.2 Smart City in Pune 4 1.2.2.1 Specifications 5 1.2.2.2 Transport and Mobility 5 1.2.2.3 Water and Sewage Management 5 1.3 Status of Smart Cities in India 5 1.3.1 Funding Process by Government 6 1.4 Analysis of Smart City Setup 7 1.4.1 Physical Infrastructure-Based 7 1.4.2 Social Infrastructure-Based 7 1.4.3 Urban Mobility 8 1.4.4 Solid Waste Management System 8 1.4.5 Economical-Based Infrastructure 9 1.4.6 Infrastructure-Based Development 9 1.4.7 Water Supply System 10 1.4.8 Sewage Networking 10 1.5 Ideal Planning for the Sewage Networking Systems 10 1.5.1 Availability and Ideal Consumption of Resources 10 1.5.2 Anticipating Future Demand 11 1.5.3 Transporting Networks to Facilitate 11 1.5.4 Control Centers for Governing the City 12 1.5.5 Integrated Command and Control Center 12 1.6 Heritage of Culture Based on Modern Advancement 13 1.7 Funding and Business Models to Leverage 14 1.7.1 Fundings 15 1.8 Community-Based Development 16 1.8.1 Smart Medical Care 16 1.8.2 Smart Safety for The IT 16 1.8.3 IoT Communication Interface With ML 17 1.8.4 Machine Learning Algorithms 17 1.8.5 Smart Community 18 1.9 Revolutionary Impact With Other Locations 18 1.10 Finding Balanced City Development 20 1.11 E-Industry With Enhanced Resources 20 1.12 Strategy for Development of Smart Cities 21 1.12.1 Stakeholder Benefits 21 1.12.2 Urban Integration 22 1.12.3 Future Scope of City Innovations 22 1.12.4 Conclusion 23 References 24 2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan 27W. H. Rankothge 2.1 Introduction 28 2.2 Background 29 2.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 29 2.2.2 Rice Distribution 31 2.3 Methodology 31 2.3.1 Requirements of the Proposed Platform 32 2.3.2 Data to Evaluate the ‘isRice” Platform 34 2.3.3 Implementation of Prediction Modules 34 2.3.3.1 Recurrent Neural Network 35 2.3.3.2 Long Short-Term Memory 36 2.3.3.3 Paddy Harvest Prediction Function 37 2.3.3.4 Rice Demand Prediction Function 39 2.3.4 Implementation of Rice Distribution Planning Module 40 2.3.4.1 Genetic Algorithm–Based Rice Distribution Planning 41 2.3.5 Front-End Implementation 44 2.4 Results and Discussion 45 2.4.1 Paddy Harvest Prediction Function 45 2.4.2 Rice Demand Prediction Function 46 2.4.3 Rice Distribution Planning Module 46 2.5 Conclusion 49 References 49 3 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity 53Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni 3.1 Introduction 54 3.2 Literature Survey 56 3.3 Proposed Model 58 3.4 Results 61 3.5 Conclusion 64 References 64 4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) 67Dineshbabu V., Arul Kumar V. P. and Gowtham M. S. 4.1 Introduction 68 4.2 Related Works 69 4.3 Industry 4.0 Production and Dashboard Design 69 4.4 Results and Discussion 70 4.5 Conclusion 73 References 73 5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation 75S. Pradeep Kumar and G. Kalpana 5.1 Introduction 75 5.2 Types of CAPTCHAs 78 5.2.1 Text-Based CAPTCHA 78 5.2.2 Image-Based CAPTCHA 80 5.2.3 Audio-Based CAPTCHA 80 5.2.4 Video-Based CAPTCHA 81 5.2.5 Puzzle-Based CAPTCHA 82 5.3 Related Work 82 5.4 Proposed Technique 82 5.5 Text-Based CAPTCHA Scheme 83 5.6 Breaking Text-Based CAPTCHA’s Scheme 85 5.6.1 Individual Character-Based Segmentation Method 85 5.6.2 Character Width-Based Segmentation Method 86 5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 87 5.7.1 Graphical Operation 87 5.7.2 Two-Dimensional Composite Transformation Calculation 89 5.8 Graphical Text-Based CAPTCHA in Online Application 91 5.9 Conclusion and Future Enhancement 93 References 94 6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning 97Pradeep Kumar S., Jayanthi K. and Selvakumari S. 6.1 Introduction 98 6.1.1 Internet of Things 98 6.1.2 Deep Learning 98 6.1.3 Detecting the Traffic Sign With the Mask R-CNN 99 6.1.3.1 Mask R-Convolutional Neural Network 99 6.1.3.2 Color Space Conversion 100 6.2 Experimental Evaluation 101 6.2.1 Implementation Details 101 6.2.2 Traffic Sign Classification 101 6.2.3 Traffic Sign Detection 102 6.2.4 Sample Outputs 103 6.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 103 6.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 103 6.2.6 Python Code 108 6.3 Conclusion 109 References 110 7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View 113R. Bhuvanya and M. Kavitha 7.1 Introduction 114 7.1.1 Modules of Recommender System 114 7.1.2 Evaluation Structure 115 7.1.3 Contribution of the Paper 115 7.1.4 Organization of the Paper 116 7.2 Evaluation Metrics 116 7.2.1 Offline Analytics 116 7.2.1.1 Prediction Accuracy Metrics 116 7.2.1.2 Decision Support Metrics 118 7.2.1.3 Rank Aware Top-N Metrics 120 7.2.2 Item and List-Based Metrics 122 7.2.2.1 Coverage 122 7.2.2.2 Popularity 123 7.2.2.3 Personalization 123 7.2.2.4 Serendipity 123 7.2.2.5 Diversity 123 7.2.2.6 Churn 124 7.2.2.7 Responsiveness 124 7.2.3 User Studies and Online Evaluation 125 7.2.3.1 Usage Log 125 7.2.3.2 Polls 126 7.2.3.3 Lab Experiments 126 7.2.3.4 Online A/B Test 126 7.3 Related Works 127 7.3.1 Categories of Recommendation 129 7.3.2 Data Mining Methods of Recommender System 129 7.3.2.1 Data Pre-Processing 129 7.3.2.2 Data Analysis 131 7.4 Experimental Setup 135 7.5 Summary and Conclusions 142 References 143 8 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT 147Pradeep Kumar S., Pushpakumar R. and Selvakumari S. 8.1 Introduction 148 8.2 Prelims 148 8.2.1 Digital Image Processing 148 8.2.2 Deep Learning 149 8.2.3 WSN 149 8.2.4 Raspberry Pi 152 8.2.5 Thermal Sensor 152 8.2.6 Relay 152 8.2.7 TensorFlow 153 8.2.8 Convolution Neural Network (CNN) 153 8.3 Proposed System 154 8.4 Math Model 156 8.5 Results 158 8.6 Conclusion 161 References 161 9 Route Optimization for Perishable Goods Transportation System 167Kowsalyadevi A. K., Megala M. and Manivannan C. 9.1 Introduction 167 9.2 Related Works 168 9.2.1 Need for Route Optimization 170 9.3 Proposed Methodology 171 9.4 Proposed Work Implementation 174 9.5 Conclusion 178 References 178 10 Fake News Detection Using Machine Learning Algorithms 181M. Kavitha, R. Srinivasan and R. Bhuvanya 10.1 Introduction 181 10.2 Literature Survey 183 10.3 Methodology 193 10.3.1 Data Retrieval 195 10.3.2 Data Pre-Processing 195 10.3.3 Data Visualization 196 10.3.4 Tokenization 196 10.3.5 Feature Extraction 196 10.3.6 Machine Learning Algorithms 197 10.3.6.1 Logistic Regression 197 10.3.6.2 Naïve Bayes 198 10.3.6.3 Random Forest 200 10.3.6.4 XGBoost 200 10.4 Experimental Results 202 10.5 Conclusion 203 References 203 11 Opportunities and Challenges in Machine Learning With IoT 209Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana 11.1 Introduction 209 11.2 Literature Review 210 11.2.1 A Designed Architecture of ML on Big Data 210 11.2.2 Machine Learning 211 11.2.3 Types of Machine Learning 212 11.2.3.1 Supervised Learning 212 11.2.3.2 Unsupervised Learning 215 11.3 Why Should We Care About Learning Representations? 217 11.4 Big Data 218 11.5 Data Processing Opportunities and Challenges 219 11.5.1 Data Redundancy 219 11.5.2 Data Noise 220 11.5.3 Heterogeneity of Data 220 11.5.4 Discretization of Data 220 11.5.5 Data Labeling 221 11.5.6 Imbalanced Data 221 11.6 Learning Opportunities and Challenges 221 11.7 Enabling Machine Learning With IoT 223 11.8 Conclusion 224 References 225 12 Machine Learning Effects on Underwater Applications and IoUT 229Mamta Nain, Nitin Goyal and Manni Kumar 12.1 Introduction 229 12.2 Characteristics of IoUT 231 12.3 Architecture of IoUT 232 12.3.1 Perceptron Layer 233 12.3.2 Network Layer 234 12.3.3 Application Layer 234 12.4 Challenges in IoUT 234 12.5 Applications of IoUT 235 12.6 Machine Learning 240 12.7 Simulation and Analysis 241 12.8 Conclusion 242 References 242 13 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms 247Monika Chaudhary, Nitin Goyal and Aadil Mushtaq 13.1 Introduction 248 13.2 Internet of Underwater Things 248 13.2.1 Challenges in IoUT 249 13.3 Routing Protocols of IoUT 250 13.4 Machine Learning in IoUT 255 13.4.1 Types of Machine Learning Algorithms 258 13.5 Performance Evaluation 259 13.6 Conclusion 260 References 260 14 Chest X-Ray for Pneumonia Detection 265Sarang Sharma, Sheifali Gupta and Deepali Gupta 14.1 Introduction 266 14.2 Background 267 14.3 Research Methodology 268 14.4 Results and Discussion 271 14.4.1 Results 271 14.4.2 Discussion 271 14.5 Conclusion 273 Acknowledgment 273 References 274 Index 275

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Book SynopsisDEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these arTable of ContentsForeword xv Preface xvii 1 Biometric Identification Using Deep Learning for Advance Cloud Security 1Navani Siroya and Manju Mandot 1.1 Introduction 2 1.2 Techniques of Biometric Identification 3 1.2.1 Fingerprint Identification 3 1.2.2 Iris Recognition 4 1.2.3 Facial Recognition 4 1.2.4 Voice Recognition 5 1.3 Approaches 6 1.3.1 Feature Selection 6 1.3.2 Feature Extraction 6 1.3.3 Face Marking 7 1.3.4 Nearest Neighbor Approach 8 1.4 Related Work, A Review 9 1.5 Proposed Work 10 1.6 Future Scope 12 1.7 Conclusion 12 References 12 2 Privacy in Multi-Tenancy Cloud Using Deep Learning 15Shweta Solanki and Prafull Narooka 2.1 Introduction 15 2.2 Basic Structure 16 2.2.1 Basic Structure of Cloud Computing 17 2.2.2 Concept of Multi-Tenancy 18 2.2.3 Concept of Multi-Tenancy with Cloud Computing 19 2.3 Privacy in Cloud Environment Using Deep Learning 21 2.4 Privacy in Multi-Tenancy with Deep Learning Concept 22 2.5 Related Work 23 2.6 Conclusion 24 References 25 3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 27S J Savitha, Dr. M Paulraj and K Saranya 3.1 Introduction 27 3.2 Related Works 29 3.3 Methods 32 3.3.1 EEG Signal Pre-Processing 32 3.3.1.1 Discrete Fourier Transform (DFT) 32 3.3.1.2 Least Mean Square (LMS) Algorithm 32 3.3.1.3 Discrete Cosine Transform (DCT) 33 3.3.2 Feature Extraction Techniques 33 3.3.3 Classification Techniques 33 3.4 BCI Applications 34 3.4.1 Possible BCI Uses 36 3.4.2 Communication 36 3.4.3 Movement Control 36 3.4.4 Environment Control 37 3.4.5 Locomotion 38 3.5 Cloud-Based EEG Overview 38 3.5.1 Data Backup and Restoration 39 3.6 Conclusion 40 References 40 4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 43R. Amirtha Katesa Sai Raj, M. Arun Kumar, S. Dinesh, U. Harisudhan and Dr. R. Uthirasamy 4.1 Introduction 44 4.2 Study of Bi-Facial Solar Panel 45 4.3 Proposed System 46 4.3.1 Block Diagram 46 4.3.2 DC Motor Mechanism 47 4.3.3 Battery Bank 48 4.3.4 System Management Using IoT 48 4.3.5 Structure of Proposed System 50 4.3.6 Spoiler Design 51 4.3.7 Working Principle of Proposed System 52 4.3.8 Design and Analysis 53 4.4 Applications of IoT in Renewable Energy Resources 53 4.4.1 Wind Turbine Reliability Using IoT 54 4.4.2 Siting of Wind Resource Using IoT 55 4.4.3 Application of Renewable Energy in Medical Industries 56 4.4.4 Data Analysis Using Deep Learning 57 4.5 Conclusion 59 References 59 5 Background Mosaicing Model for Wide Area Surveillance System 63Dr. E. Komagal 5.1 Introduction 64 5.2 Related Work 64 5.3 Methodology 65 5.3.1 Feature Extraction 66 5.3.2 Background Deep Learning Model Based on Mosaic 67 5.3.3 Foreground Segmentation 70 5.4 Results and Discussion 70 5.5 Conclusion 72 References 72 6 Prediction of CKD Stage 1 Using Three Different Classifiers 75Thamizharasan, K., Yamini, P., Shimola, A. and Sudha, S. 6.1 Introduction 75 6.2 Materials and Methods 78 6.3 Results and Discussion 84 6.4 Conclusions and Future Scope 89 References 89 7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 93Phavithra Selvaraj, Sruthi, M.S., Sridaran, M. and Dr. Jobin Christ M.C. 7.1 Introduction 93 7.2 Methodology 95 7.2.1 Data Acquisition 95 7.2.2 Image Preprocessing 96 7.2.3 Segmentation 97 7.2.4 Feature Extraction 98 7.2.5 Classification 99 7.3 Results and Discussions 100 7.3.1 Preprocessing 100 7.3.2 Classification 103 7.3.3 Validation 104 7.4 Conclusion 106 References 106 8 Convolutional Networks 109Simran Kaur and Rashmi Agrawal 8.1 Introduction 110 8.2 Convolution Operation 110 8.3 CNN 110 8.4 Practical Applications 112 8.4.1 Audio Data 112 8.4.2 Image Data 112 8.4.3 Text Data 113 8.5 Challenges of Profound Models 113 8.6 Deep Learning In Object Detection 114 8.7 CNN Architectures 114 8.8 Challenges of Item Location 118 8.8.1 Scale Variation Problem 118 8.8.2 Occlusion Problem 119 8.8.3 Deformation Problem 120 References 121 9 Categorization of Cloud Computing & Deep Learning 123Disha Shrmali 9.1 Introduction to Cloud Computing 123 9.1.1 Cloud Computing 123 9.1.2 Cloud Computing: History and Evolution 124 9.1.3 Working of Cloud 125 9.1.4 Characteristics of Cloud Computing 127 9.1.5 Different Types of Cloud Computing Service Models 128 9.1.5.1 Infrastructure as A Service (IAAS) 128 9.1.5.2 Platform as a Service (PAAS) 129 9.1.5.3 Software as a Service (SAAS) 129 9.1.6 Cloud Computing Advantages and Disadvantages 130 9.1.6.1 Advantages of Cloud Computing 130 9.1.6.2 Disadvantages of Cloud Computing 132 9.2 Introduction to Deep Learning 133 9.2.1 History and Revolution of Deep Learning 134 9.2.1.1 Development of Deep Learning Algorithms 134 9.2.1.2 The FORTRAN Code for Back Propagation 135 9.2.1.3 Deep Learning from the 2000s and Beyond 135 9.2.1.4 The Cat Experiment 136 9.2.2 Neural Networks 137 9.2.2.1 Artificial Neural Networks 137 9.2.2.2 Deep Neural Networks 138 9.2.3 Applications of Deep Learning 138 9.2.3.1 Automatic Speech Recognition 138 9.2.3.2 Electromyography (EMG) Recognition 139 9.2.3.3 Image Recognition 139 9.2.3.4 Visual Art Processing 140 9.2.3.5 Natural Language Processing 140 9.2.3.6 Drug Discovery and Toxicology 140 9.2.3.7 Customer Relationship Management 141 9.2.3.8 Recommendation Systems 141 9.2.3.9 Bioinformatics 141 9.2.3.10 Medical Image Analysis 141 9.2.3.11 Mobile Advertising 141 9.2.3.12 Image Restoration 142 9.2.3.13 Financial Fraud Detection 142 9.2.3.14 Military 142 9.3 Conclusion 142 References 143 10 Smart Load Balancing in Cloud Using Deep Learning 145Astha Parihar and Shweta Sharma 10.1 Introduction 146 10.2 Load Balancing 147 10.2.1 Static Algorithm 148 10.2.2 Dynamic (Run-Time) Algorithms 148 10.3 Load Adjusting in Distributing Computing 149 10.3.1 Working of Load Balancing 151 10.4 Cloud Load Balancing Criteria (Measures) 152 10.5 Load Balancing Proposed for Cloud Computing 153 10.5.1 Calculation of Load Balancing in the Whole System 154 10.6 Load Balancing in Next Generation Cloud Computing 155 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 157 10.7.1 Quantum Isochronous Parallel 158 10.7.2 Phase Isochronous Parallel 159 10.7.3 Dynamic Isochronous Coordinate Strategy 161 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 161 10.8.1 Adaptive Quick Reassignment (AdaptQR) 162 10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel) 163 10.9 Conclusion 164 References 165 11 Biometric Identification for Advanced Cloud Security 167Yojna khandelwal and Kapil Chauhan 11.1 Introduction 168 11.1.1 Biometric Identification 168 11.1.2 Biometric Characteristic 169 11.1.3 Types of Biometric Data 169 11.1.3.1 Face Recognition 169 11.1.3.2 Hand Vein 170 11.1.3.3 Signature Verification 170 11.1.3.4 Iris Recognition 170 11.1.3.5 Voice Recognition 170 11.1.3.6 Fingerprints 171 11.2 Literature Survey 172 11.3 Biometric Identification in Cloud Computing 174 11.3.1 How Biometric Authentication is Being Used on the Cloud Platform 176 11.4 Models and Design Goals 177 11.4.1 Models 177 11.4.1.1 System Model 177 11.4.1.2 Threat Model 177 11.4.2 Design Goals 178 11.5 Face Recognition Method as a Biometric Authentication 179 11.6 Deep Learning Techniques for Big Data in Biometrics 180 11.6.1 Issues and Challenges 181 11.6.2 Deep Learning Strategies For Biometric Identification 182 11.7 Conclusion 185 References 185 12 Application of Deep Learning in Cloud Security 189Jaya Jain 12.1 Introduction 190 12.2 Literature Review 191 12.3 Deep Learning 192 12.4 The Uses of Fields in Deep Learning 195 12.5 Conclusion 202 References 203 13 Real Time Cloud Based Intrusion Detection 207Ekta Bafna 13.1 Introduction 207 13.2 Literature Review 209 13.3 Incursion In Cloud 211 13.3.1 Denial of Service (DoS) Attack 212 13.3.2 Insider Attack 212 13.3.3 User To Root (U2R) Attack 213 13.3.4 Port Scanning 213 13.4 Intrusion Detection System 213 13.4.1 Signature-Based Intrusion Detection System (SIDS) 213 13.4.2 Anomaly-Based Intrusion Detection System (AIDS) 214 13.4.3 Intrusion Detection System Using Deep Learning 215 13.5 Types of IDS in Cloud 216 13.5.1 Host Intrusion Detection System 216 13.5.2 Network Based Intrusion Detection System 217 13.5.3 Distributed Based Intrusion Detection System 217 13.6 Model of Deep Learning 218 13.6.1 ConvNet Model 218 13.6.2 Recurrent Neural Network 219 13.6.3 Multi-Layer Perception Model 219 13.7 KDD Dataset 221 13.8 Evaluation 221 13.9 Conclusion 223 References 223 14 Applications of Deep Learning in Cloud Security 225Disha Shrmali and Shweta Sharma 14.1 Introduction 226 14.1.1 Data Breaches 226 14.1.2 Accounts Hijacking 227 14.1.3 Insider Threat 227 14.1.3.1 Malware Injection 227 14.1.3.2 Abuse of Cloud Services 228 14.1.3.3 Insecure APIs 228 14.1.3.4 Denial of Service Attacks 228 14.1.3.5 Insufficient Due Diligence 229 14.1.3.6 Shared Vulnerabilities 229 14.1.3.7 Data Loss 229 14.2 Deep Learning Methods for Cloud Cyber Security 230 14.2.1 Deep Belief Networks 230 14.2.1.1 Deep Autoencoders 230 14.2.1.2 Restricted Boltzmann Machines 232 14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers 233 14.2.1.4 Recurrent Neural Networks 233 14.2.1.5 Convolutional Neural Networks 234 14.2.1.6 Generative Adversarial Networks 235 14.2.1.7 Recursive Neural Networks 236 14.2.2 Applications of Deep Learning in Cyber Security 237 14.2.2.1 Intrusion Detection and Prevention Systems (IDS/IPS) 237 14.2.2.2 Dealing with Malware 237 14.2.2.3 Spam and Social Engineering Detection 238 14.2.2.4 Network Traffic Analysis 238 14.2.2.5 User Behaviour Analytics 238 14.2.2.6 Insider Threat Detection 239 14.2.2.7 Border Gateway Protocol Anomaly Detection 239 14.2.2.8 Verification if Keystrokes were Typed by a Human 240 14.3 Framework to Improve Security in Cloud Computing 240 14.3.1 Introduction to Firewalls 241 14.3.2 Importance of Firewalls 242 14.3.2.1 Prevents the Passage of Unwanted Content 242 14.3.2.2 Prevents Unauthorized Remote Access 243 14.3.2.3 Restrict Indecent Content 243 14.3.2.4 Guarantees Security Based on Protocol and IP Address 244 14.3.2.5 Protects Seamless Operations in Enterprises 244 14.3.2.6 Protects Conversations and Coordination Contents 244 14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content 245 14.3.3 Types of Firewalls 245 14.3.3.1 Proxy-Based Firewalls 245 14.3.3.2 Stateful Firewalls 246 14.3.3.3 Next-Generation Firewalls (NGF) 247 14.3.3.4 Web Application Firewalls (WAF) 247 14.3.3.5 Working of WAF 248 14.3.3.6 How Web Application Firewalls (WAF) Work 248 14.3.3.7 Attacks that Web Application Firewalls Prevent 250 14.3.3.8 Cloud WAF 251 14.4 WAF Deployment 251 14.4.1 Web Application Firewall (WAF) Security Models 252 14.4.2 Firewall-as-a-Service (FWaaS) 252 14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW) 253 14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing 253 14.5 Conclusion 254 References 254 About the Editors 257 Index 263

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Book SynopsisSMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthc

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Book SynopsisINDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case stuTable of ContentsPreface xvii 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes 1.1 Introduction 2 1.2 Relationship Between Artificial Intelligence and IoT 5 1.2.1 AI Concept 6 1.2.2 IoT Concept 10 1.3 IoT Ecosystem 15 1.3.1 Industry 4.0 Concept 18 1.3.2 Industrial Internet of Things 19 1.4 Discussion 21 1.5 Trends 23 1.6 Conclusions 24 References 26 2 Analysis on Security in IoT Devices—An Overview 31T. Nalini and T. Murali Krishna 2.1 Introduction 32 2.2 Security Properties 33 2.3 Security Challenges of IoT 34 2.3.1 Classification of Security Levels 35 2.3.1.1 At Information Level 36 2.3.1.2 At Access Level 36 2.3.1.3 At Functional Level 36 2.3.2 Classification of IoT Layered Architecture 37 2.3.2.1 Edge Layer 37 2.3.2.2 Access Layer 37 2.3.2.3 Application Layer 37 2.4 IoT Security Threats 38 2.4.1 Physical Device Threats 39 2.4.1.1 Device-Threats 39 2.4.1.2 Resource Led Constraints 39 2.4.2 Network-Oriented Communication Assaults 39 2.4.2.1 Structure 40 2.4.2.2 Protocol 40 2.4.3 Data-Based Threats 41 2.4.3.1 Confidentiality 41 2.4.3.2 Availability 41 2.4.3.3 Integrity 42 2.5 Assaults in IoT Devices 43 2.5.1 Devices of IoT 43 2.5.2 Gateways and Networking Devices 44 2.5.3 Cloud Servers and Control Devices 45 2.6 Security Analysis of IoT Platforms 46 2.6.1 ARTIK 46 2.6.2 GiGA IoT Makers 47 2.6.3 AWS IoT 47 2.6.4 Azure IoT 47 2.6.5 Google Cloud IoT (GC IoT) 48 2.7 Future Research Approaches 49 2.7.1 Blockchain Technology 51 2.7.2 5G Technology 52 2.7.3 Fog Computing (FC) and Edge Computing (EC) 52 References 54 3 Smart Automation, Smart Energy, and Grid Management Challenges 59J. Gayathri Monicka and C. Amuthadevi 3.1 Introduction 60 3.2 Internet of Things and Smart Grids 62 3.2.1 Smart Grid in IoT 63 3.2.2 IoT Application 64 3.2.3 Trials and Imminent Investigation Guidelines 66 3.3 Conceptual Model of Smart Grid 67 3.4 Building Computerization 71 3.4.1 Smart Lighting 73 3.4.2 Smart Parking 73 3.4.3 Smart Buildings 74 3.4.4 Smart Grid 75 3.4.5 Integration IoT in SG 77 3.5 Challenges and Solutions 81 3.6 Conclusions 83 References 83 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89C. Amuthadevi and J. Gayathri Monicka 4.1 Introduction 89 4.1.1 Fundamental Terms in IIoT 91 4.1.1.1 Cloud Computing 92 4.1.1.2 Big Data Analytics 92 4.1.1.3 Fog/Edge Computing 92 4.1.1.4 Internet of Things 93 4.1.1.5 Cyber-Physical-System 94 4.1.1.6 Artificial Intelligence 95 4.1.1.7 Machine Learning 95 4.1.1.8 Machine-to-Machine Communication 99 4.1.2 Intelligent Analytics 99 4.1.3 Predictive Maintenance 100 4.1.4 Disaster Predication and Safety Management 101 4.1.4.1 Natural Disasters 101 4.1.4.2 Disaster Lifecycle 102 4.1.4.3 Disaster Predication 103 4.1.4.4 Safety Management 104 4.1.5 Optimization 105 4.2 Existing Technology and Its Review 106 4.2.1 Survey on Predictive Analysis in Natural Disasters 106 4.2.2 Survey on Safety Management and Recovery 108 4.2.3 Survey on Optimizing Solutions in Natural Disasters 109 4.3 Research Limitation 110 4.3.1 Forward-Looking Strategic Vision (FVS) 110 4.3.2 Availability of Data 111 4.3.3 Load Balancing 111 4.3.4 Energy Saving and Optimization 111 4.3.5 Cost Benefit Analysis 112 4.3.6 Misguidance of Analysis 112 4.4 Finding 113 4.4.1 Data Driven Reasoning 113 4.4.2 Cognitive Ability 113 4.4.3 Edge Intelligence 113 4.4.4 Effect of ML Algorithms and Optimization 114 4.4.5 Security 114 4.5 Conclusion and Future Research 114 4.5.1 Conclusion 114 4.5.2 Future Research 114 References 115 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar 5.1 Introduction 120 5.2 Fuzzy Logic 121 5.2.1 Fuzzy Sets 121 5.2.2 Fuzzy Logic Basics 122 5.2.3 Fuzzy Logic and Power System 122 5.2.4 Fuzzy Logic—Automatic Generation Control 123 5.2.5 Fuzzy Microgrid Wind 123 5.3 Genetic Algorithm 123 5.3.1 Important Aspects of Genetic Algorithm 124 5.3.2 Standard Genetic Algorithm 126 5.3.3 Genetic Algorithm and Its Application 127 5.3.4 Power System and Genetic Algorithm 127 5.3.5 Economic Dispatch Using Genetic Algorithm 128 5.4 Artificial Neural Network 128 5.4.1 The Biological Neuron 129 5.4.2 A Formal Definition of Neural Network 130 5.4.3 Neural Network Models 131 5.4.4 Rosenblatt’s Perceptron 131 5.4.5 Feedforward and Recurrent Networks 132 5.4.6 Back Propagation Algorithm 133 5.4.7 Forward Propagation 133 5.4.8 Algorithm 134 5.4.9 Recurrent Network 135 5.4.10 Examples of Neural Networks 136 5.4.10.1 AND Operation 136 5.4.10.2 OR Operation 137 5.4.10.3 XOR Operation 137 5.4.11 Key Components of an Artificial Neuron Network 138 5.4.12 Neural Network Training 141 5.4.13 Training Types 142 5.4.13.1 Supervised Training 142 5.4.13.2 Unsupervised Training 142 5.4.14 Learning Rates 142 5.4.15 Learning Laws 143 5.4.16 Restructured Power System 144 5.4.17 Advantages of Precise Forecasting of the Price 145 5.5 Conclusion 145 References 146 6 Recent Advances in Wearable Antennas: A Survey 149Harvinder Kaur and Paras Chawla 6.1 Introduction 150 6.2 Types of Antennas 153 6.2.1 Description of Wearable Antennas 153 6.2.1.1 Microstrip Patch Antenna 153 6.2.1.2 Substrate Integrated Waveguide Antenna 153 6.2.1.3 Planar Inverted-F Antenna 153 6.2.1.4 Monopole Antenna 153 6.2.1.5 Metasurface Loaded Antenna 154 6.3 Design of Wearable Antennas 154 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design 154 6.3.1.1 Conducting Coating on Substrate 154 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 157 6.3.1.3 Partial Ground Plane 158 6.3.2 Logo Antennas 159 6.3.3 Embroidered Antenna 159 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap 160 6.3.5 Wearable Reconfigurable Antenna 161 6.4 Textile Antennas 162 6.5 Comparison of Wearable Antenna Designs 168 6.6 Fractal Antennas 168 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 171 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 172 6.6.3 Double-Fractal Layer Wearable Antenna 172 6.6.4 Development of Embroidered Sierpinski Carpet Antenna 172 6.7 Future Challenges of Wearable Antenna Designs 174 6.8 Conclusion 174 References 175 7 An Overview of IoT and Its Application With Machine Learning in Data Center 181Manikandan Ramanathan and Kumar Narayanan 7.1 Introduction 181 7.1.1 6LoWPAN 183 7.1.2 Data Protocols 185 7.1.2.1 CoAP 185 7.1.2.2 MQTT 187 7.1.2.3 Rest APIs 187 7.1.3 IoT Components 189 7.1.3.1 Hardware 190 7.1.3.2 Middleware 190 7.1.3.3 Visualization 191 7.2 Data Center and Internet of Things 191 7.2.1 Modern Data Centers 191 7.2.2 Data Storage 191 7.2.3 Computing Process 192 7.2.3.1 Fog Computing 192 7.2.3.2 Edge Computing 194 7.2.3.3 Cloud Computing 194 7.2.3.4 Distributed Computing 195 7.2.3.5 Comparison of Cloud Computing and Fog Computing 196 7.3 Machine Learning Models and IoT 196 7.3.1 Classifications of Machine Learning Supported in IoT 197 7.3.1.1 Supervised Learning 197 7.3.1.2 Unsupervised Learning 198 7.3.1.3 Reinforcement Learning 198 7.3.1.4 Ensemble Learning 199 7.3.1.5 Neural Network 199 7.4 Challenges in Data Center and IoT 199 7.4.1 Major Challenges 199 7.5 Conclusion 201 References 201 8 Impact of IoT to Meet Challenges in Drone Delivery System 203J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi 8.1 Introduction 204 8.1.1 IoT Components 204 8.1.2 Main Division to Apply IoT in Aviation 205 8.1.3 Required Field of IoT in Aviation 206 8.1.3.1 Airports as Smart Cities or Airports as Platforms 207 8.1.3.2 Architecture of Multidrone 208 8.1.3.3 The Multidrone Design has the Accompanying Prerequisites 208 8.2 Literature Survey 209 8.3 Smart Airport Architecture 211 8.4 Barriers to IoT Implementation 215 8.4.1 How is the Internet of Things Converting the Aviation Enterprise? 216 8.5 Current Technologies in Aviation Industry 216 8.5.1 Methodology or Research Design 217 8.6 IoT Adoption Challenges 218 8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges 218 8.7 Transforming Airline Industry With Internet of Things 219 8.7.1 How the IoT Is Improving the Aviation Industry 219 8.7.1.1 IoT: Game Changer for Aviation Industry 220 8.7.2 Applications of AI in the Aviation Industry 220 8.7.2.1 Ticketing Systems 220 8.7.2.2 Flight Maintenance 221 8.7.2.3 Fuel Efficiency 221 8.7.2.4 Crew Management 221 8.7.2.5 Flight Health Checks and Maintenance 221 8.7.2.6 In-Flight Experience Management 222 8.7.2.7 Luggage Tracking 222 8.7.2.8 Airport Management 222 8.7.2.9 Just the Beginning 222 8.8 Revolution of Change (Paradigm Shift) 222 8.9 The Following Diagram Shows the Design of the Application 223 8.10 Discussion, Limitations, Future Research, and Conclusion 224 8.10.1 Growth of Aviation IoT Industry 224 8.10.2 IoT Applications—Benefits 225 8.10.3 Operational Efficiency 225 8.10.4 Strategic Differentiation 225 8.10.5 New Revenue 226 8.11 Present and Future Scopes 226 8.11.1 Improving Passenger Experience 226 8.11.2 Safety 227 8.11.3 Management of Goods and Luggage 227 8.11.4 Saving 227 8.12 Conclusion 227 References 227 9 IoT-Based Water Management System for a Healthy Life 229N. Meenakshi, V. Pandimurugan and S. Rajasoundaran 9.1 Introduction 230 9.1.1 Human Activities as a Source of Pollutants 230 9.2 Water Management Using IoT 231 9.2.1 Water Quality Management Based on IoT Framework 232 9.3 IoT Characteristics and Measurement Parameters 233 9.4 Platforms and Configurations 235 9.5 Water Quality Measuring Sensors and Data Analysis 239 9.6 Wastewater and Storm Water Monitoring Using IoT 241 9.6.1 System Initialization 241 9.6.2 Capture and Storage of Information 241 9.6.3 Information Modeling 241 9.6.4 Visualization and Management of the Information 243 9.7 Sensing and Sampling of Water Treatment Using IoT 244 References 246 10 Fuel Cost Optimization Using IoT in Air Travel 249P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya 10.1 Introduction 250 10.1.1 Introduction to IoT 250 10.1.2 Processing IoT Data 250 10.1.3 Advantages of IoT 251 10.1.4 Disadvantages of IoT 251 10.1.5 IoT Standards 251 10.1.6 Lite Operating System (Lite OS) 251 10.1.7 Low Range Wide Area Network (LoRaWAN) 252 10.2 Emerging Frameworks in IoT 252 10.2.1 Amazon Web Service (AWS) 252 10.2.2 Azure 252 10.2.3 Brillo/Weave Statement 252 10.2.4 Calvin 252 10.3 Applications of IoT 253 10.3.1 Healthcare in IoT 253 10.3.2 Smart Construction and Smart Vehicles 254 10.3.3 IoT in Agriculture 254 10.3.4 IoT in Baggage Tracking 254 10.3.5 Luggage Logbook 254 10.3.6 Electrical Airline Logbook 254 10.4 IoT for Smart Airports 255 10.4.1 IoT in Smart Operation in Airline Industries 257 10.4.2 Fuel Emissions on Fly 258 10.4.3 Important Things in Findings 258 10.5 Related Work 258 10.6 Existing System and Analysis 264 10.6.1 Technology Used in the System 265 10.7 Proposed System 268 10.8 Components in Fuel Reduction 276 10.9 Conclusion 276 10.10 Future Enhancements 277 References 277 11 Object Detection in IoT-Based Smart Refrigerators Using CNN 281Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K. 11.1 Introduction 282 11.2 Literature Survey 283 11.3 Materials and Methods 287 11.3.1 Image Processing 292 11.3.2 Product Sensing 292 11.3.3 Quality Detection 293 11.3.4 Android Application 293 11.4 Results and Discussion 294 11.5 Conclusion 299 References 299 12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301Latha Parthiban, Maithili Devi Reddy and A. Kumaravel 12.1 Introduction 302 12.2 Literature Review 302 12.3 Data Mining Tasks 304 12.3.1 Classification 305 12.3.2 Regression 306 12.3.3 Clustering 306 12.3.4 Summarization 306 12.3.5 Dependency Modeling 306 12.3.6 Association Rule Discovery 307 12.3.7 Outlier Detection 307 12.3.8 Prediction 307 12.4 Feature Selection Techniques in Data Mining 308 12.4.1 GAs for Feature Selection 308 12.4.2 GP for Feature Selection 309 12.4.3 PSO for Feature Selection 310 12.4.4 ACO for Feature Selection 311 12.5 Classification With Neural Predictive Classifier 312 12.5.1 Neural Predictive Classifier 313 12.5.2 MapReduce Function on Neural Class 317 12.6 Conclusions 319 References 319 13 Impact of COVID-19 on IIoT 321K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao 13.1 Introduction 321 13.1.1 The Use of IoT During COVID-19 321 13.1.2 Consumer IoT 322 13.1.3 Commercial IoT 322 13.1.4 Industrial Internet of Things (IIoT) 322 13.1.5 Infrastructure IoT 322 13.1.6 Role of IoT in COVID-19 Response 323 13.1.7 Telehealth Consultations 323 13.1.8 Digital Diagnostics 323 13.1.9 Remote Monitoring 323 13.1.10 Robot Assistance 323 13.2 The Benefits of Industrial IoT 326 13.2.1 How IIoT is Being Used 327 13.2.2 Remote Monitoring 327 13.2.3 Predictive Maintenance 328 13.3 The Challenges of Wide-Spread IIoT Implementation 329 13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 330 13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 330 13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 331 13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 332 13.3.5 Building on the Lessons of 2020 332 13.4 Effects of COVID-19 on Industrial Manufacturing 332 13.4.1 New Challenges for Industrial Manufacturing 333 13.4.2 Smarter Manufacturing for Actionable Insights 333 13.4.3 A Promising Future for IIoT Adoption 334 13.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due to COVID-19 Impact 335 13.6 The Impact of COVID-19 on IoT Applications 337 13.6.1 Decreased Interest in Consumer IoT Devices 338 13.6.2 Remote Asset Access Becomes Important 338 13.6.3 Digital Twins Help With Scenario Planning 339 13.6.4 New Uses for Drones 339 13.6.5 Specific IoT Health Applications Surge 340 13.6.6 Track and Trace Solutions Get Used More Extensively 340 13.6.7 Smart City Data Platforms Become Key 340 13.7 The Impact of COVID-19 on Technology in General 341 13.7.1 Ongoing Projects Are Paused 341 13.7.2 Some Enterprise Technologies Take Off 341 13.7.3 Declining Demand for New Projects/Devices/ Services 342 13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 342 13.7.5 The Digital Divide Widens 343 13.8 The Impact of COVID-19 on Specific IoT Technologies 343 13.8.1 IoT Networks Largely Unaffected 343 13.8.2 Technology Roadmaps Get Delayed 344 13.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 344 13.10 The Potential of IoT in Coronavirus Like Disease Control 345 13.11 Conclusion 346 References 346 14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas 14.1 Introduction 350 14.2 Literature Review 353 14.3 Design of Smart Ambulance Booking System Through App 356 14.4 Smart Ambulance Booking 359 14.4.1 Welcome Page 360 14.4.2 Sign Up 360 14.4.3 Home Page 361 14.4.4 Ambulance Section 361 14.4.5 Ambulance Selection Page 362 14.4.6 Confirmation of Booking and Tracking 363 14.5 Result and Discussion 363 14.5.1 How It Works? 365 14.6 Conclusion 365 14.7 Future Scope 366 References 366 15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369Resmi G. Nair and N. Kumar 15.1 Introduction 370 15.2 Literature Survey 371 15.3 Problem Statement 372 15.4 Proposed Methodology 373 15.4.1 Design a Smart Wearable Device 373 15.4.2 Normalization 374 15.4.3 Feature Extraction 377 15.4.4 Classification 378 15.4.5 Polynomial HMAC Algorithm 379 15.5 Result and Discussion 382 15.5.1 Accuracy 382 15.5.2 Positive Predictive Value 382 15.5.3 Sensitivity 383 15.5.4 Specificity 383 15.5.5 False Out 383 15.5.6 False Discovery Rate 383 15.5.7 Miss Rate 383 15.5.8 F-Score 383 15.6 Conclusion 390 References 390 Index 393

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Book SynopsisTable of ContentsPreface xvii 1 Unmanned Aerial Vehicle (UAV): A Comprehensive Survey 1Rohit Chaurasia and Vandana Mohindru 1.1 Introduction 2 1.2 Related Work 2 1.3 UAV Technology 3 1.3.1 UAV Platforms 3 1.3.1.1 Fixed-Wing Drones 3 1.3.1.2 Multi-Rotor Drones 4 1.3.1.3 Single-Rotor Drones 5 1.3.1.4 Fixed-Wing Hybrid VTOL 6 1.3.2 Categories of the Military Drones 6 1.3.3 How Drones Work 8 1.3.3.1 Firmware—Platform Construction and Design 9 1.3.4 Comparison of Various Technologies 10 1.3.4.1 Drone Types & Sizes 10 1.3.4.2 Radar Positioning and Return to Home 10 1.3.4.3 GNSS on Ground Control Station 11 1.3.4.4 Collision Avoidance Technology and Obstacle Detection 11 1.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU 12 1.3.4.6 UAV Drone Propulsion System 12 1.3.4.7 Flight Parameters Through Telemetry 13 1.3.4.8 Drone Security & Hacking 13 1.3.4.9 3D Maps and Models With Drone Sensors 13 1.3.5 UAV Communication Network 15 1.3.5.1 Classification on the Basis of Spectrum Perspective 15 1.3.5.2 Various Types of Radio communication Links 16 1.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System 18 1.3.5.4 Frequency Bands for the Operation of UAS 19 1.3.5.5 Cellular Technology for UAS Operation 19 1.4 Application of UAV 21 1.4.1 In Military 21 1.4.2 In Geomorphological Mapping and Other Similar Sectors 22 1.4.3 In Agriculture 22 1.5 UAV Challenges 23 1.6 Conclusion and Future Scope 24 References 24 2 Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope 29Jolly Parikh and Anuradha Basu 2.1 Introduction 30 2.2 Technical Challenges 30 2.2.1 Variations in Channel Characteristics 32 2.2.2 UAV-Assisted Cellular Network Planning and Provisioning 33 2.2.3 Millimeter Wave Cellular Connected UAVs 34 2.2.4 Deployment of UAV 35 2.2.5 Trajectory Optimization 36 2.2.6 On-Board Energy 37 2.3 Conclusion 37 References 37 3 Battery and Energy Management in UAV-Based Networks 43Santosh Kumar, Amol Vasudeva and Manu Sood 3.1 Introduction 43 3.2 The Need for Energy Management in UAV-Based Communication Networks 45 3.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks 46 3.2.2 Non-Homogeneous Power Consumption 47 3.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity 47 3.2.4 Short-Range Line-of-Sight Communication 48 3.2.5 Time Constraint (Time-Limited Spectrum Access) 48 3.2.6 Energy Constraint 49 3.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection) 49 3.3 Efficient Battery and Energy Management Proposed Techniques in Literature 50 3.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion 51 3.3.2 Compressed Sensing 52 3.3.3 Power Allocation and Position Optimization 53 3.3.4 Non-Orthogonal Multiple Access (NOMA) 53 3.3.5 Wireless Charging/Power Transfer (WPT) 54 3.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner 55 3.3.7 Efficient Deployment and Movement of UAVs 55 3.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint 56 3.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection 57 3.3.10 Trust Management 57 3.3.11 Self-Organization-Based Clustering 58 3.3.12 Bandwidth/Spectrum-Sharing Between UAVs 59 3.3.13 Using Millimeter Wave With SWIPT 59 3.3.14 Energy Harvesting 60 3.4 Conclusion 61 References 67 4 Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study 73Nagesh Kumar 4.1 Introduction 73 4.1.1 Introduction to UAV 74 4.1.2 Communication in UAV 75 4.2 Literature Survey Process 77 4.2.1 Research Questions 77 4.2.2 Information Source 77 4.3 Routing in UAV 78 4.3.1 Communication Methods in UAV 78 4.3.1.1 Single-Hop Communication 79 4.3.1.2 Multi-Hop Communication 80 4.4 Challenges and Issues 82 4.4.1 Energy Consumption 82 4.4.2 Mobility of Devices 82 4.4.3 Density of UAVs 82 4.4.4 Changes in Topology 85 4.4.5 Propagation Models 85 4.4.6 Security in Routing 85 4.5 Conclusion 85 References 86 5 A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs) 89Shaik Johny Basha and Jagan Mohan Reddy Danda 5.1 Introduction 89 5.2 Applications of UAVs and Their Market Opportunity 90 5.2.1 Applications 90 5.2.2 Market Opportunity 92 5.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs) 92 5.3.1 Confidentiality Attacks 93 5.3.2 Integrity Attacks 95 5.3.3 Availability Attacks 96 5.3.4 Authenticity Attacks 97 5.4 Research Challenges 99 5.4.1 Security Concerns 99 5.4.2 Safety Concerns 99 5.4.3 Privacy Concerns 100 5.4.4 Scalability Issues 100 5.4.5 Limited Resources 100 5.5 Conclusion 101 References 101 6 Internet of Things and UAV: An Interoperability Perspective 105Bharti Rana and Yashwant Singh 6.1 Introduction 106 6.2 Background 108 6.2.1 Issues, Controversies, and Problems 109 6.3 Internet of Things (IoT) and UAV 110 6.4 Applications of UAV-Enabled IoT 113 6.5 Research Issues in UAV-Enabled IoT 114 6.6 High-Level UAV-Based IoT Architecture 117 6.6.1 UAV Overview 117 6.6.2 Enabling IoT Scalability 119 6.6.3 Enabling IoT Intelligence 120 6.6.4 Enabling Diverse IoT Applications 121 6.7 Interoperability Issues in UAV-Based IoT 121 6.8 Conclusion 123 References 124 7 Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence 129Swarnjeet Kaur, Kulwant Singh and Amanpreet Singh 7.1 Introduction 130 7.2 Military 132 7.3 Attack 133 7.4 Journalism 134 7.5 Search and Rescue 136 7.6 Disaster Relief 138 7.7 Conclusion 139 References 139 8 Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT 143Madhuri S. Wakode and Rajesh B. Ingle 8.1 Introduction 144 8.1.1 Organization of the Work 145 8.2 Introduction to UAV and IoT 145 8.2.1 UAV 145 8.2.2 IoT 146 8.2.3 UAV-Enabled IoT 147 8.2.4 Blockchain 150 8.3 Security and Privacy Issues in UAV-Enabled IoT 151 8.4 Blockchain-Based Solutions to Various Security Issues 153 8.5 Research Directions 154 8.6 Conclusion 154 8.7 Future Work 155 References 155 9 Efficient Energy Management Systems in UAV-Based IoT Networks 159V. Mounika Reddy, Neelima K. and G. Naresh 9.1 Introduction 160 9.2 Energy Harvesting Methods 161 9.2.1 Basic Energy Harvesting Mechanisms 162 9.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms 163 9.2.3 mm Wave Energy Harvesting Mechanism 164 9.2.4 Full Duplex Wireless Energy Harvesting Mechanism 165 9.3 Energy Recharge Methods 165 9.4 Efficient Energy Utilization Methods 166 9.4.1 GLRM Method 166 9.4.2 DRL Mechanism 167 9.4.3 Onboard Double Q-Learning Mechanism 168 9.4.4 Collision-Free Scheduling Mechanism 168 9.5 Conclusion 170 References 170 10 A Survey on IoE-Enabled Unmanned Aerial Vehicles 173K. Siddharthraju, R. Dhivyadevi, M. Supriya, B. Jaishankar and Shanmugaraja T. 10.1 Introduction 174 10.2 Overview of Internet of Everything 176 10.2.1 Emergence of IoE 176 10.2.2 Expectation of IoE 177 10.2.2.1 Scalability 177 10.2.2.2 Intelligence 178 10.2.2.3 Diversity 178 10.2.3 Possible Technologies 179 10.2.3.1 Enabling Scalability 179 10.2.3.2 Enabling Intelligence 180 10.2.3.3 Enabling Diversity 180 10.2.4 Challenges of IoE 181 10.2.4.1 Coverage Constraint 181 10.2.4.2 Battery Constraint 181 10.2.4.3 Computing Constraint 181 10.2.4.4 Security Constraint 182 10.3 Overview of Unmanned Aerial Vehicle (UAV) 182 10.3.1 Unmanned Aircraft System (UAS) 183 10.3.2 UAV Communication Networks 183 10.3.2.1 Ad Hoc Multi-UAV Networks 183 10.3.2.2 UAV-Aided Communication Networks 184 10.4 UAV and IoE Integration 184 10.4.1 Possibilities to Carry UAVs 184 10.4.1.1 Widespread Connectivity 185 10.4.1.2 Environmentally Aware 185 10.4.1.3 Peer-Maintenance of Communications 185 10.4.1.4 Detector Control and Reusing 185 10.4.2 UAV-Enabled IoE 186 10.4.3 Vehicle Detection Enabled IoE Optimization 186 10.4.3.1 Weak-Connected Locations 186 10.4.3.2 Regions with Low Network Support 186 10.5 Open Research Issues 187 10.6 Discussion 187 10.6.1 Resource Allocation 187 10.6.2 Universal Standard Design 188 10.6.3 Security Mechanism 188 10.7 Conclusion 189 References 189 11 Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities 193Madhuri S. Wakode 11.1 Introduction 194 11.1.1 Related Work 195 11.1.2 Contributions 195 11.1.3 Organization of the Work 195 11.2 Overview of UAV-Enabled IoT Systems 196 11.2.1 UAV-Enabled IoT Systems for Smart Cities 197 11.3 Overview of Big Data Analytics 197 11.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems 198 11.4.1 Big Data Analytics in UAV-Enabled IoT Applications 199 11.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems 201 11.5 Challenges 202 11.6 Conclusion 202 11.7 Future Work 203 References 203 12 Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module 207Lakshit Kohli, Manglesh Saurabh, Ishaan Bhatia, Nidhi Sindhwani and Manjula Vijh 12.1 Introduction 208 12.2 Existing System 208 12.3 Proposed System 210 12.4 IoT Sensors and Architecture 212 12.4.1 Sensors and Theory 212 12.4.2 Architectures Available 213 12.4.2.1 3-Layer IoT Architecture 213 12.4.2.2 5-Layer IoT Architecture 214 12.4.2.3 Architecture & Supporting Modules 215 12.4.2.4 Integration Approach 215 12.4.2.5 System of Modules 216 12.5 Advantages of the Proposed System 217 12.6 Design 218 12.6.1 System Design 219 12.6.2 Auto-Leveling 219 12.6.3 Amphibious Landing Module 221 12.6.4 Processing Module 223 12.6.5 Surround Sense Module 223 12.7 Results 224 12.8 Conclusion 227 12.9 Future Scope 228 References 228 13 Mind Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI) 231Prasath M.S., Naveen R. and Sivaraj G. 13.1 Introduction 232 13.1.1 Classification of UAVs 232 13.1.2 Drone Controlling 232 13.2 Mind-Controlled UAV With BCI Technology 233 13.3 Layout and Architecture of BCI Technology 234 13.4 Hardware Components 235 13.4.1 Neurosky Mindwave Headset 235 13.4.2 Microcontroller Board—Arduino 236 13.4.3 A Computer 237 13.4.4 Drone for Quadcopter 238 13.5 Software Components 239 13.5.1 Processing P3 Software 239 13.5.2 Arduino IDE Software 240 13.5.3 ThinkGear Connector 240 13.6 Hardware and Software Integration 241 13.7 Conclusion 243 References 244 14 Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0 247Dhirendra Siddharth, Dilip Kumar Saini and Ajay Kumar 14.1 Introduction 247 14.2 Drone Technology as an Instrument for Increasing Farm Productivity 248 14.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology 249 14.3.1 Methodology and Development of ICT 250 14.4 Strong Intelligence From UAV to the Agricultural Sector 252 14.4.1 Latest Agricultural Drone History 252 14.4.2 The Challenges 254 14.4.3 SAP’s Next Wave of Drone Technologies 254 14.4.4 SAP Connected Agriculture 256 14.4.5 Cases of Real-World Use 257 14.4.5.1 Crop Surveying 257 14.4.5.2 Capture the Plantation 258 14.4.5.3 Image Processing 258 14.4.5.4 Working to Create GeoTiles and an Image Pyramid 259 14.5 Drones-Based Sensor Platforms 260 14.5.1 Context and Challenges 260 14.5.2 Stakeholder and End Consumer Benefits 261 14.5.3 The Technology 262 14.5.3.1 Provisions of the Unmanned Aerial Vehicles 262 14.6 Jobs of Space Technology in Crop Insurance 263 14.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk 267 14.7.1 A Modern Working 267 14.7.2 Discovering Drone Mapping Technology 268 14.7.3 From Lowland to Uplands, Drone Mapping Technology 269 14.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability 269 14.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles 270 14.8.1 System and Application Based on UAV-WSN 270 14.8.2 Using a Complex Comprehensive System 271 14.8.3 Benefits Assessment of Conventional System and the UAV-Based System 271 14.8.3.1 Merit 272 14.8.3.2 Saving Expenses 272 14.8.3.3 Traditional Agriculture 273 14.8.3.4 UAV-WSN System-Based Agriculture 273 14.9 Conclusion 273 References 273 15 IoT-Based UAV Platform Revolutionized in Smart Healthcare 277Umesh Kumar Gera, Dilip Kumar Saini, Preeti Singh and Dhirendra Siddharth 15.1 Introduction 278 15.2 IoT-Based UAV Platform for Emergency Services 279 15.3 Healthcare Internet of Things: Technologies, Advantages 281 15.3.1 Advantage 281 15.3.1.1 Concurrent Surveillance and Tracking 281 15.3.1.2 From End-To-End Networking and Availability 282 15.3.1.3 Information and Review Assortment 282 15.3.1.4 Warnings and Recording 282 15.3.1.5 Wellbeing Remote Assistance 283 15.3.1.6 Research 283 15.3.2 Complications 283 15.3.2.1 Privacy and Data Security 283 15.3.2.2 Integration: Various Protocols and Services 284 15.3.2.3 Overload and Accuracy of Data 284 15.3.2.4 Expenditure 284 15.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones 285 15.4.1 Hearables 285 15.4.2 Ingestible Sensors 285 15.4.3 Moodables 285 15.4.4 Technology of Computer Vision 286 15.4.5 Charting for Healthcare 286 15.5 Drones That Will Revolutionize Healthcare 286 15.5.1 Integrated Enhancement in Efficiency 286 15.5.2 Offering Personalized Healthcare 287 15.5.3 The Big Data Manipulation 287 15.5.4 Safety and Privacy Optimization 287 15.5.5 Enabling M2M Communication 288 15.6 Healthcare Revolutionizing Drones 288 15.6.1 Google Drones 288 15.6.2 Healthcare Integrated Rescue Operations (HiRO) 289 15.6.3 EHang 289 15.6.4 TU Delft 289 15.6.5 Project Wing 289 15.6.6 Flirtey 289 15.6.7 Seattle’s VillageReach 290 15.6.8 ZipLine 290 15.7 Conclusion 290 References 290 Index 295

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Book SynopsisTable of ContentsPreface xiii 1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1Kalpana Rangra and Amitava Choudhury 1.1 Introduction 1 1.2 Conclusions 9 2 Smart Farming Using Machine Learning and IoT 13Alo Sen, Rahul Roy and Satya Ranjan Dash 2.1 Introduction 14 2.2 Related Work 15 2.3 Problem Identification 22 2.4 Objective Behind the Integrated Agro-IoT System 23 2.5 Proposed Prototype of the Integrated Agro-IoT System 23 2.6 Hardware Component Requirement for the Integrated Agro-IoT System 26 2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 30 2.8 Conclusions 31 2.9 Future Work 32 3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35P.K. Paul 3.1 Introduction 36 3.2 Objectives 36 3.3 Methods 37 3.4 Agricultural Informatics: An Account 37 3.5 Agricultural Informatics & Technological Components: Basics & Emergence 40 3.6 IoT: Basics and Characteristics 41 3.7 IoT: The Applications & Agriculture Areas 43 3.8 Agricultural Informatics & IoT: The Scenario 45 3.9 IoT in Agriculture: Requirement, Issues & Challenges 49 3.10 Development, Economy and Growth: Agricultural Informatics Context 50 3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 51 3.12 Suggestions 60 3.13 Conclusion 60 4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67Pushan Kumar Dutta and Susanta Mitra 4.1 Introduction 68 4.2 Related Work 69 4.3 Smart Production With the Introduction of Drones and IoT 72 4.4 Agricultural Drones 75 4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 77 4.6 Conclusion 81 5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89Parijata Majumdar and Sanjoy Mitra 5.1 Introduction 90 5.2 Machine Learning (ML)-Based IoT Solution 90 5.3 Motivation of the Work 91 5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 91 5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 92 5.6 Challenges 112 5.7 Conclusion and Future Work 113 6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117Alok Negi, Krishan Kumar and Prachi Chauhan 6.1 Introduction 117 6.2 Related Work 119 6.3 Proposed Work 121 6.4 Results and Evaluation 124 6.5 Conclusion 127 7 Deep Residual Neural Network for Plant Seedling Image Classification 131Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi 7.1 Introduction 131 7.2 Related Work 136 7.3 Proposed Work 139 7.4 Result and Evaluation 142 7.5 Conclusion 144 8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 8.1 Introduction 148 8.2 Background & Related Works 150 8.3 Proposed Model 155 8.4 Methodology 160 8.5 Performance Analysis 165 8.6 Future Research Direction 166 8.7 Conclusion 167 9 An Integrated Application of IoT-Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning 171Avishek Banerjee, Arnab Mitra and Arindam Biswas 9.1 Introduction 172 9.2 Literature Review 175 9.3 Proposed Hybrid Algorithms (GA-MWPSO) 177 9.4 Reliability Optimization and Coverage Optimization Model 179 9.5 Problem Description 181 9.6 Numerical Examples, Results and Discussion 182 9.7 Conclusion 183 10 Decryption and Design of a Multicopter Unmanned Aerial Vehicle (UAV) for Heavy Lift Agricultural Operations 189Raghuvirsinh Pravinsinh Parmar 10.1 Introduction 190 10.2 History of Multicopter UAVs 192 10.3 Basic Components of Multicopter UAV 193 10.4 Working and Control Mechanism of Multicopter UAV 207 10.5 Design Calculations and Selection of Components 210 10.6 Conclusion 218 11 IoT-Enabled Agricultural System Application, Challenges and Security Issues 223Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 11.1 Introduction 224 11.2 Background & Related Works 226 11.3 Challenges to Implement IoT-Enabled Systems 232 11.4 Security Issues and Measures 240 11.5 Future Research Direction 243 11.6 Conclusion 244 12 Plane Region Step Farming, Animal and Pest Attack Control Using Internet of Things 249Sahadev Roy, Kaushal Mukherjee and Arindam Biswas 12.1 Introduction 250 12.2 Proposed Work 254 12.3 Irrigation Methodology 257 12.4 Sensor Connection Using Internet of Things 259 12.5 Placement of Sensor in the Field 263 12.6 Conclusion 267 References 268 Index 271

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Book SynopsisADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployedTable of ContentsPreface xvii 1 Internet of Medical Things—State-of-the-Art 1Kishor Joshi and Ruchi Mehrotra 1.1 Introduction 2 1.2 Historical Evolution of IoT to IoMT 2 1.2.1 IoT and IoMT—Market Size 4 1.3 Smart Wearable Technology 4 1.3.1 Consumer Fitness Smart Wearables 4 1.3.2 Clinical-Grade Wearables 5 1.4 Smart Pills 7 1.5 Reduction of Hospital-Acquired Infections 8 1.5.1 Navigation Apps for Hospitals 8 1.6 In-Home Segment 8 1.7 Community Segment 9 1.8 Telehealth and Remote Patient Monitoring 9 1.9 IoMT in Healthcare Logistics and Asset Management 12 1.10 IoMT Use in Monitoring During COVID-19 13 1.11 Conclusion 14 References 15 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma 2.1 Introduction 22 2.2 Related Works 23 2.3 Architecture 25 2.3.1 Device Layer 25 2.3.2 Fog Layer 26 2.3.3 Cloud Layer 26 2.4 Issues and Challenges 26 2.5 Conclusion 29 References 30 3 Study of Thyroid Disease Using Machine Learning 33Shanu Verma, Rashmi Popli and Harish Kumar 3.1 Introduction 34 3.2 Related Works 34 3.3 Thyroid Functioning 35 3.4 Category of Thyroid Cancer 36 3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37 3.5.1 Decision Tree Algorithm 38 3.5.2 Support Vector Machines 39 3.5.3 Random Forest 39 3.5.4 Logistic Regression 39 3.5.5 Naïve Bayes 40 3.6 Conclusion 41 References 41 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi 4.1 Introduction 44 4.1.1 Introduction to IoT 44 4.1.2 Introduction to Vulnerability, Attack, and Threat 45 4.2 IoT in Healthcare 46 4.2.1 Confidentiality 46 4.2.2 Integrity 46 4.2.3 Authorization 46 4.2.4 Availability 47 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48 4.4 Conclusion 54 References 54 5 Methods of Lung Segmentation Based on CT Images 59Amit Verma and Thipendra P. Singh 5.1 Introduction 59 5.2 Semi-Automated Algorithm for Lung Segmentation 60 5.2.1 Algorithm for Tracking to Lung Edge 60 5.2.2 Outlining the Region of Interest in CT Images 62 5.2.2.1 Locating the Region of Interest 62 5.2.2.2 Seed Pixels and Searching Outline 62 5.3 Automated Method for Lung Segmentation 63 5.3.1 Knowledge-Based Automatic Model for Segmentation 63 5.3.2 Automatic Method for Segmenting the Lung CT Image 64 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64 5.5 Conclusion 65 References 65 6 Handling Unbalanced Data in Clinical Images 69Amit Verma 6.1 Introduction 70 6.2 Handling Imbalance Data 71 6.2.1 Cluster-Based Under-Sampling Technique 72 6.2.2 Bootstrap Aggregation (Bagging) 75 6.3 Conclusion 76 References 76 7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81Ishita Banerjee and Madhumathy P. 7.1 Introduction 82 7.2 Literature Survey 84 7.3 Procedure 86 7.4 Results 93 7.5 Conclusion 97 References 97 8 Smart IoT Devices for the Elderly and People with Disabilities 101K. N. D. Saile and Kolisetti Navatha 8.1 Introduction 101 8.2 Need for IoT Devices 102 8.3 Where Are the IoT Devices Used? 103 8.3.1 Home Automation 103 8.3.2 Smart Appliances 104 8.3.3 Healthcare 104 8.4 Devices in Home Automation 104 8.4.1 Automatic Lights Control 104 8.4.2 Automated Home Safety and Security 104 8.5 Smart Appliances 105 8.5.1 Smart Oven 105 8.5.2 Smart Assistant 105 8.5.3 Smart Washers and Dryers 106 8.5.4 Smart Coffee Machines 106 8.5.5 Smart Refrigerator 106 8.6 Healthcare 106 8.6.1 Smart Watches 107 8.6.2 Smart Thermometer 107 8.6.3 Smart Blood Pressure Monitor 107 8.6.4 Smart Glucose Monitors 107 8.6.5 Smart Insulin Pump 108 8.6.6 Smart Wearable Asthma Monitor 108 8.6.7 Assisted Vision Smart Glasses 109 8.6.8 Finger Reader 109 8.6.9 Braille Smart Watch 109 8.6.10 Smart Wand 109 8.6.11 Taptilo Braille Device 110 8.6.12 Smart Hearing Aid 110 8.6.13 E-Alarm 110 8.6.14 Spoon Feeding Robot 110 8.6.15 Automated Wheel Chair 110 8.7 Conclusion 112 References 112 9 IoT-Based Health Monitoring and Tracking System for Soldiers 115Kavitha N. and Madhumathy P. 9.1 Introduction 116 9.2 Literature Survey 117 9.3 System Requirements 118 9.3.1 Software Requirement Specification 119 9.3.2 Functional Requirements 119 9.4 System Design 119 9.4.1 Features 121 9.4.1.1 On-Chip Flash Memory 122 9.4.1.2 On-Chip Static RAM 122 9.4.2 Pin Control Block 122 9.4.3 UARTs 123 9.4.3.1 Features 123 9.4.4 System Control 123 9.4.4.1 Crystal Oscillator 123 9.4.4.2 Phase-Locked Loop 124 9.4.4.3 Reset and Wake-Up Timer 124 9.4.4.4 Brown Out Detector 125 9.4.4.5 Code Security 125 9.4.4.6 External Interrupt Inputs 125 9.4.4.7 Memory Mapping Control 125 9.4.4.8 Power Control 126 9.4.5 Real Monitor 126 9.4.5.1 GPS Module 126 9.4.6 Temperature Sensor 127 9.4.7 Power Supply 128 9.4.8 Regulator 128 9.4.9 LCD 128 9.4.10 Heart Rate Sensor 129 9.5 Implementation 129 9.5.1 Algorithm 130 9.5.2 Hardware Implementation 130 9.5.3 Software Implementation 131 9.6 Results and Discussions 133 9.6.1 Heart Rate 133 9.6.2 Temperature Sensor 135 9.6.3 Panic Button 135 9.6.4 GPS Receiver 135 9.7 Conclusion 136 References 136 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 137G. K. Kamalam and S. Anitha 10.1 Introduction 138 10.2 Literature Survey 139 10.3 Medical Data Classification 141 10.3.1 Structured Data 142 10.3.2 Semi-Structured Data 142 10.4 Data Analysis 142 10.4.1 Descriptive Analysis 142 10.4.2 Diagnostic Analysis 143 10.4.3 Predictive Analysis 143 10.4.4 Prescriptive Analysis 143 10.5 ML Methods Used in Healthcare 144 10.5.1 Supervised Learning Technique 144 10.5.2 Unsupervised Learning 145 10.5.3 Semi-Supervised Learning 145 10.5.4 Reinforcement Learning 145 10.6 Probability Distributions 145 10.6.1 Discrete Probability Distributions 146 10.6.1.1 Bernoulli Distribution 146 10.6.1.2 Uniform Distribution 147 10.6.1.3 Binomial Distribution 147 10.6.1.4 Normal Distribution 148 10.6.1.5 Poisson Distribution 148 10.6.1.6 Exponential Distribution 149 10.7 Evaluation Metrics 150 10.7.1 Classification Accuracy 150 10.7.2 Confusion Matrix 150 10.7.3 Logarithmic Loss 151 10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 152 10.7.5 Area Under Curve (AUC) 152 10.7.6 Precision 153 10.7.7 Recall 153 10.7.8 F1 Score 153 10.7.9 Mean Absolute Error 154 10.7.10 Mean Squared Error 154 10.7.11 Root Mean Squared Error 155 10.7.12 Root Mean Squared Logarithmic Error 155 10.7.13 R-Squared/Adjusted R-Squared 156 10.7.14 Adjusted R-Squared 156 10.8 Proposed Methodology 156 10.8.1 Neural Network 158 10.8.2 Triangular Membership Function 158 10.8.3 Data Collection 159 10.8.4 Secured Data Storage 159 10.8.5 Data Retrieval and Merging 161 10.8.6 Data Aggregation 162 10.8.7 Data Partition 162 10.8.8 Fuzzy Rules for Prediction of Heart Disease 163 10.8.9 Fuzzy Rules for Prediction of Diabetes 164 10.8.10 Disease Prediction With Severity and Diagnosis 165 10.9 Experimental Results 166 10.10 Conclusion 169 References 169 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan 11.1 Introduction 174 11.2 Background Elements 180 11.2.1 Security Comparison Between Traditional and IoT Networks 185 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 187 11.3.1 Security Protocols 187 11.3.2 Enabling Technologies 188 11.4 CloudIoT Health System Framework 191 11.4.1 Data Perception/Acquisition 192 11.4.2 Data Transmission/Communication 193 11.4.3 Cloud Storage and Warehouse 194 11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 194 11.4.5 Design Considerations 197 11.5 Security Challenges and Vulnerabilities 199 11.5.1 Security Characteristics and Objectives 200 11.5.1.1 Confidentiality 202 11.5.1.2 Integrity 202 11.5.1.3 Availability 202 11.5.1.4 Identification and Authentication 202 11.5.1.5 Privacy 203 11.5.1.6 Light Weight Solutions 203 11.5.1.7 Heterogeneity 203 11.5.1.8 Policies 203 11.5.2 Security Vulnerabilities 203 11.5.2.1 IoT Threats and Vulnerabilities 205 11.5.2.2 Cloud-Based Threats 208 11.6 Security Countermeasures and Considerations 214 11.6.1 Security Countermeasures 214 11.6.1.1 Security Awareness and Survey 214 11.6.1.2 Security Architecture and Framework 215 11.6.1.3 Key Management 216 11.6.1.4 Authentication 217 11.6.1.5 Trust 218 11.6.1.6 Cryptography 219 11.6.1.7 Device Security 219 11.6.1.8 Identity Management 220 11.6.1.9 Risk-Based Security/Risk Assessment 220 11.6.1.10 Block Chain–Based Security 220 11.6.1.11 Automata-Based Security 220 11.6.2 Security Considerations 234 11.7 Open Research Issues and Security Challenges 237 11.7.1 Security Architecture 237 11.7.2 Resource Constraints 238 11.7.3 Heterogeneous Data and Devices 238 11.7.4 Protocol Interoperability 238 11.7.5 Trust Management and Governance 239 11.7.6 Fault Tolerance 239 11.7.7 Next-Generation 5G Protocol 240 11.8 Discussion and Analysis 240 11.9 Conclusion 241 References 242 12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan 12.1 Introduction Machine Learning 256 12.2 Importance of Machine Learning 256 12.2.1 ML vs. Classical Algorithms 258 12.2.2 Learning Supervised 259 12.2.3 Unsupervised Learning 261 12.2.4 Network for Neuralism 263 12.2.4.1 Definition of the Neural Network 263 12.2.4.2 Neural Network Elements 263 12.3 Procedure 265 12.3.1 Dataset and Seizure Identification 265 12.3.2 System 265 12.4 Feature Extraction 266 12.5 Experimental Methods 266 12.5.1 Stepwise Feature Optimization 266 12.5.2 Post-Classification Validation 268 12.5.3 Fusion of Classification Methods 268 12.6 Experiments 269 12.7 Framework for EEG Signal Classification 269 12.8 Detection of the Preictal State 270 12.9 Determination of the Seizure Prediction Horizon 271 12.10 Dynamic Classification Over Time 272 12.11 Conclusion 273 References 273 13 Use of Machine Learning in Healthcare 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi 13.1 Introduction 276 13.2 Uses of Machine Learning in Pharma and Medicine 276 13.2.1 Distinguish Illnesses and Examination 277 13.2.2 Drug Discovery and Manufacturing 277 13.2.3 Scientific Imaging Analysis 278 13.2.4 Twisted Therapy 278 13.2.5 AI to Know-Based Social Change 278 13.2.6 Perception Wellness Realisms 279 13.2.7 Logical Preliminary and Exploration 279 13.2.8 Publicly Supported Perceptions Collection 279 13.2.9 Better Radiotherapy 280 13.2.10 Incidence Forecast 280 13.3 The Ongoing Preferences of ML in Human Services 281 13.4 The Morals of the Use of Calculations in Medicinal Services 284 13.5 Opportunities in Healthcare Quality Improvement 288 13.5.1 Variation in Care 288 13.5.2 Inappropriate Care 289 13.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 289 13.5.4 The Fact That People Are Unable to do What They Know Works 289 13.5.5 A Waste 290 13.6 A Team-Based Care Approach Reduces Waste 290 13.7 Conclusion 291 References 292 14 Methods of MRI Brain Tumor Segmentation 295Amit Verma 14.1 Introduction 295 14.2 Generative and Descriptive Models 296 14.2.1 Region-Based Segmentation 300 14.2.2 Generative Model With Weighted Aggregation 300 14.3 Conclusion 302 References 303 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 305Varun Sapra and Luxmi Sapra 15.1 Introduction 306 15.2 Data Set 307 15.2.1 Data Insights 308 15.3 Feature Engineering 310 15.4 Framework for Early Detection of Disease 312 15.4.1 Deep Neural Network 313 15.5 Result 314 15.6 Conclusion 315 References 315 16 A Comprehensive Analysis on Masked Face Detection Algorithms 319Pranjali Singh, Amitesh Garg and Amritpal Singh 16.1 Introduction 320 16.2 Literature Review 321 16.3 Implementation Approach 325 16.3.1 Feature Extraction 325 16.3.2 Image Processing 325 16.3.3 Image Acquisition 325 16.3.4 Classification 325 16.3.5 MobileNetV2 326 16.3.6 Deep Learning Architecture 326 16.3.7 LeNet-5, AlexNet, and ResNet-50 326 16.3.8 Data Collection 326 16.3.9 Development of Model 327 16.3.10 Training of Model 328 16.3.11 Model Testing 328 16.4 Observation and Analysis 328 16.4.1 CNN Algorithm 328 16.4.2 SSDNETV2 Algorithm 330 16.4.3 SVM 331 16.5 Conclusion 332 References 333 17 IoT-Based Automated Healthcare System 335Darpan Anand and Aashish Kumar 17.1 Introduction 335 17.1.1 Software-Defined Network 336 17.1.2 Network Function Virtualization 337 17.1.3 Sensor Used in IoT Devices 338 17.2 SDN-Based IoT Framework 341 17.3 Literature Survey 343 17.4 Architecture of SDN-IoT for Healthcare System 344 17.5 Challenges 345 17.6 Conclusion 347 References 347 Index 351

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Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391

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Book SynopsisARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists. Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose. The paradigm in renewable energy and climatTable of ContentsPreface xv Section I: Renewable Energy 1 1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3Amany Alshawi 1.1 Introduction 3 1.2 History of AI for Sustainability and Smart Energy Practices 4 1.3 Energy and Resources Scenarios on the Global Scale 5 1.4 Statistical Basis of AI in Sustainability Practices 6 1.4.1 General Statistics 6 1.4.2 Environmental Stress–Based Statistics 8 1.4.2.1 Climate Change 9 1.4.2.2 Biodiversity 10 1.4.2.3 Deforestation 10 1.4.2.4 Changes in Chemistry of Oceans 10 1.4.2.5 Nitrogen Cycle 10 1.4.2.6 Water Crisis 11 1.4.2.7 Air Pollution 11 1.5 Major Challenges Faced by AI in Sustainability 11 1.5.1 Concentration of Wealth 11 1.5.2 Talent-Related and Business-Related Challenges of AI 12 1.5.3 Dependence on Machine Learning 14 1.5.4 Cybersecurity Risks 15 1.5.5 Carbon Footprint of AI 16 1.5.6 Issues in Performance Measurement 16 1.6 Major Opportunities of AI in Sustainability 17 1.6.1 AI and Water-Related Hazards Management 17 1.6.2 AI and Smart Cities 18 1.6.3 AI and Climate Change 21 1.6.4 AI and Environmental Sustainability 23 1.6.5 Impacts of AI in Transportation 24 1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25 1.6.7 Opportunities in the Energy Sector 26 1.7 Conclusion and Future Direction 26 References 27 2 Recent Applications of Machine Learning in Solar Energy Prediction 33N. Kapilan, R.P. Reddy and Vidhya P. 2.1 Introduction 34 2.2 Solar Energy 34 2.3 AI, ML and DL 36 2.4 Data Preprocessing Techniques 38 2.5 Solar Radiation Estimation 38 2.6 Solar Power Prediction 43 2.7 Challenges and Opportunities 45 2.8 Future Research Directions 46 2.9 Conclusion 46 Acknowledgement 47 References 47 3 Mathematical Analysis on Power Generation – Part I 53G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy 3.1 Introduction 54 3.2 Methodology for Derivations 55 3.3 Energy Discussions 59 3.4 Data Analysis 63 Acknowledgement 67 References 67 Supplementary 69 4 Mathematical Analysis on Power Generation – Part II 87G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy 4.1 Energy Analysis 88 4.2 Power Efficiency Method 89 4.3 Data Analysis 91 Acknowledgement 96 References 97 Supplementary - II 100 5 Sustainable Energy Materials 117G. Udhaya Sankar 5.1 Introduction 117 5.2 Different Methods 119 5.2.1 Co-Precipitation Method 119 5.2.2 Microwave-Assisted Solvothermal Method 120 5.2.3 Sol-Gel Method 120 5.3 X-R ay Diffraction Analysis 120 5.4 FTIR Analysis 122 5.5 Raman Analysis 124 5.6 UV Analysis 125 5.7 SEM Analysis 127 5.8 Energy Dispersive X-Ray Analysis 127 5.9 Thermoelectric Application 129 5.9.1 Thermal Conductivity 129 5.9.2 Electrical Conductivity 131 5.9.3 Seebeck Coefficient 131 5.9.4 Power Factor 132 5.9.5 Figure of Merit 133 5.10 Limitations and Future Direction 133 5.11 Conclusion 133 Acknowledgement 134 References 134 6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal 6.1 Introduction 137 6.1.1 Conventional MPPT Control Techniques 138 6.2 Other MPPT Control Methods 142 6.2.1 Proportional Integral Derivative Controllers 142 6.2.2 Fuzzy Logic Controller 144 6.2.2.1 Fuzzy Inference System 150 6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151 6.2.3 Artificial Neural Network 151 6.2.3.1 Biological Neural Networks 152 6.2.3.2 Architectures of Artificial Neural Networks 155 6.2.3.3 Training of Artificial Neural Networks 157 6.2.3.4 Radial Basis Function 158 6.2.4 Neuro-Fuzzy Inference Approach 158 6.2.4.1 Adaptive Neuro-Fuzzy Approach 161 6.2.4.2 Hybrid Training Algorithm 161 6.3 Conclusion 167 References 167 Section II: Climate Change 171 7 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability 173Mesut Toğaçar 7.1 Introduction 174 7.2 Materials 177 7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177 7.2.2 CO2 Emission of Vehicles 178 7.2.3 Countries’ CO2 Emission Amount 179 7.2.4 Stability Level in Electric Grids 179 7.3 Artificial Intelligence Approaches 181 7.3.1 Machine Learning Methods 182 7.3.1.1 Support Vector Machine 183 7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184 7.3.1.3 Gradient Boost 185 7.3.1.4 Decision Tree 186 7.3.1.5 Random Forest 186 7.3.2 Deep Learning Methods 188 7.3.2.1 Convolutional Neural Networks 189 7.3.2.2 Long Short-Term Memory 191 7.3.2.3 Bi-Directional LSTM and CNN 192 7.3.2.4 Recurrent Neural Network 193 7.3.3 Activation Functions 195 7.3.3.1 Rectified Linear Unit 195 7.3.3.2 Softmax Function 196 7.4 Experimental Analysis 196 7.5 Discussion 210 7.6 Conclusion 211 Funding 212 Ethical Approval 212 Conflicts of Interest 212 References 212 8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217Sumit Sharma, J. Joshua Thomas and Pandian Vasant 8.1 Introduction 218 8.1.1 Indian Scenario of Renewable Energy 218 8.1.2 Solar Radiation at Earth 220 8.1.3 Solar Photovoltaic Technologies 220 8.1.3.1 Types of SPV Systems 221 8.1.3.2 Types of Solar Photovoltaic Cells 222 8.1.3.3 Effects of Temperature 223 8.1.3.4 Conversion Efficiency 223 8.1.4 Losses in PV Systems 224 8.1.5 Performance of Solar Power Plants 224 8.2 Literature Review 225 8.3 Experimental Setup 228 8.3.1 Selection of Site and Development of Experimental Facilities 229 8.3.2 Methodology 229 8.3.3 Experimental Instrumentation 230 8.3.3.1 Solar Photovoltaic Modules 230 8.3.3.2 PV Grid-Connected Inverter 232 8.3.3.3 Pyranometer 232 8.3.3.4 Digital Thermometer 234 8.3.3.5 Lightning Arrester 235 8.3.3.6 Data Acquisition System 236 8.3.4 Formula Used and Sample Calculations 236 8.3.5 Assumptions and Limitations 237 8.4 Results Discussion 238 8.4.1 Phases of Data Collection 238 8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238 8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238 8.4.2.2 Capacity Utilization Factor and Performance Ratio 241 8.4.2.3 Evaluation of MLR Model 242 8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246 8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246 8.4.3.2 Capacity Utilization Factor and Performance Ratio 246 8.4.3.3 Evaluation of MLR Model 246 8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252 8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252 8.4.4.2 Capacity Utilization Factor and Performance Ratio 255 8.4.4.3 Evaluation of MLR Model 256 8.4.5 Regression Analysis for the Whole Period 258 8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267 8.4.7 Regression Outputs Summary 268 8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268 8.4.9 Losses Due to Dust Accumulation 270 8.4.10 Economic Analysis 270 8.5 Future Research Directions 271 8.6 Conclusion 271 References 272 9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher 9.1 Introduction 278 9.1.1 Benefits of the Use of Biogas as a Fuel in India 278 9.1.2 Biogas Generators in India 279 9.1.3 Biogas 279 9.1.3.1 Process of Biogas Production 280 9.2 Literature Review 281 9.2.1 Wastes and Environment 281 9.2.2 Economic and Environmental Considerations 283 9.2.3 Factor Affecting Yield and Production of Biogas 285 9.2.3.1 The Temperature 285 9.2.3.2 PH and Buffering Systems 287 9.2.3.3 C/N Ratio 287 9.2.3.4 Substrate Type 289 9.2.3.5 Retention Time 289 9.2.3.6 Total Solids 289 9.2.4 Advantages of Anaerobic Digestion to Society 290 9.2.4.1 Electricity Generation 290 9.2.4.2 Fertilizer Production 290 9.2.4.3 Pathogen Reduction 290 9.3 Methodology 290 9.3.1 Set Up of Compact Biogas Plant and Equipments 290 9.3.2 Assembling and Fabrication of Biogas Plant 292 9.3.3 Design and Technology of Compact Biogas Plant 294 9.3.4 Gas Quantity and Quality 295 9.3.5 Calculation of Gas Quantity in Gas Holder 295 9.4 Analysis of Compact Biogas Plant 299 9.4.1 Experiment Result 299 9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299 9.4.1.2 Testing on Kitchen Waste 300 9.4.1.3 Testing on Fruits Waste 302 9.4.2 Comparison of Biogas by Different Substrate 304 9.4.3 Production of Biogas Per Day at Different Waste 304 9.4.4 Variation of PH Value 307 9.4.5 Variation of Average pH Value 307 9.4.6 Variation of Temperature 308 9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309 9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311 9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313 9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313 9.5.2 Calculation 316 9.5.3 Heat Balance Sheet 322 9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326 9.5.5 Calculation 330 9.5.6 Heat Balance Sheet 335 9.6 General Comments 336 9.7 Conclusion 339 9.8 Future Scope 340 References 340 10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh Abbreviations 346 10.1 Introduction 346 10.1.1 Global Scenario of Energy and Emissions 347 10.1.2 Diesel Engine Emissions 348 10.1.3 Mitigation of NOx and Particulate Matter 350 10.1.4 Low-Temperature Combustion Engine Fuels 350 10.2 Scope of the Current Article 351 10.3 HCCI Technology 352 10.3.1 Principle of HCCI 353 10.3.2 Performance and Emissions with HCCI 354 10.4 Partially Premixed Compression Ignition (PPCI) 354 10.5 Exhaust Gas Recirculation (EGR) 355 10.6 Reactivity Controlled Compression Ignition (RCCI) 356 10.7 LTC Through Fuel Additives 357 10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358 10.8.1 Brake Thermal Efficiency (BTE) 359 10.8.2 Nitrogen Oxide (NOx) 359 10.8.3 Soot and Particulate Matter (PM) 360 10.9 Conclusion and Future Scope 361 Acknowledgement 361 References 361 11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich 11.1 Introduction 372 11.2 Materials and Methods 374 11.3 Results 379 11.4 Discussion 382 11.5 Conclusions 385 References 386 12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin 12.1 Introduction 390 12.2 Background 392 12.3 Main Focus of the Chapter 402 12.4 Solutions and Recommendations 417 Acknowledgements 417 References 418 13 Monitoring System Based Micro-Controller for Biogas Digester 423Ahmed Abdelouareth and Mohamed Tamali 13.1 Introduction 423 13.2 Related Work 424 13.3 Methods and Material 425 13.3.1 Identification of Needs 425 13.3.2 ADOLMS Software Setup 425 13.3.3 ADOLMS Sensors 426 13.3.4 ADOLMS Hardware Architecture 428 13.4 Results 430 13.5 Conclusion 432 Acknowledgements 433 References 433 14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435Tatyana G. Krotova Introduction 435 Methodology 436 Findings 436 Conclusion 454 References 455 About the Editors 457 Index 459

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Book SynopsisINTELLIGENT SECURITY SYSTEMS Dramatically improve your cybersecurity using AI and machine learning In Intelligent Security Systems, distinguished professor and computer scientist Dr. Leon Reznik delivers an expert synthesis of artificial intelligence, machine learning and data science techniques, applied to computer security to assist readers in hardening their computer systems against threats. Emphasizing practical and actionable strategies that can be immediately implemented by industry professionals and computer device's owners, the author explains how to install and harden firewalls, intrusion detection systems, attack recognition tools, and malware protection systems. He also explains how to recognize and counter common hacking activities. This book bridges the gap between cybersecurity education and new data science programs, discussing how cutting-edge artificial intelligence and machine learning techniques can work for and against cybersecurity effTable of ContentsAcknowledgments ix Introduction xi 1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together? 1 1.1 The Current Security Landscape 1 1.2 Computer Security Basic Concepts 7 1.3 Sources of Security Threats 9 1.4 Attacks Against IoT and Wireless Sensor Networks 13 1.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science 18 1.6 Fuzzy Logic and Systems 31 1.7 Machine Learning 35 1.8 Artificial Neural Networks (ANN) 43 1.9 Genetic Algorithms (GA) 50 1.10 Hybrid Intelligent Systems 51 Review Questions 52 Exercises 53 References 54 2 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense? 57 2.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From? 57 2.2 Firewall Operational Models or How Do They Work? 65 2.3 Basic Firewall Architectures or How Are They Built Up? 70 2.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together? 75 2.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented? 82 2.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent? 96 Review Questions 104 Exercises 106 References 107 3 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense? 109 3.1 Definition, Goals, and Primary Functions 109 3.2 IDS from a Historical Perspective 113 3.3 Typical IDS Architecture Topologies, Components, and Operational Ranges 116 3.4 IDS Types: Classification Approaches 121 3.5 IDS Performance Evaluation 131 3.6 Artificial Intelligence and Machine Learning Techniques in IDS Design 136 3.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment 159 3.8 Intrusion Detection Tools 163 Review Questions 172 Exercises 174 References 175 4 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How? 177 4.1 Malware Definition, History, and Trends in Development 177 4.2 Malware Classification 182 4.3 Spam 214 4.4 Software Vulnerabilities 216 4.5 Principles of Malware Detection and Anti-malware Protection 219 4.6 Malware Detection Algorithms 229 4.7 Anti-malware Tools 237 Review Questions 240 Exercises 242 References 243 5 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us? 247 5.1 Hacker’s Activities and Protection Against 247 5.2 Data Science Investigation of Ordinary Users’ Practice 273 5.3 User’s Authentication 288 5.4 User’s Anonymity, Attacks Against It, and Protection 301 Review Questions 309 Exercises 310 References 311 6 Adversarial Machine Learning: Who Is Machine Learning Working For? 315 6.1 Adversarial Machine Learning Definition 315 6.2 Adversarial Attack Taxonomy 316 6.3 Defense Strategies 320 6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case 322 6.5 Generative Adversarial Networks 327 Review Questions 333 Exercises 334 References 335 Index 337

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Book SynopsisCYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online servicessuch as e-commerce, e-health, social networks, and other major cyber applicationsit has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.Table of ContentsContents Preface xiii Acknowledgments xvii Acronyms xix 1 Cybersecurity in the Era of Artificial Intelligence 1 1.1 Artificial Intelligence for Cybersecurity . 2 1.1.1 Artificial Intelligence 2 1.1.2 Machine Learning 4 1.1.3 Data-Driven Workflow for Cybersecurity . 6 1.2 Key Areas and Challenges 7 1.2.1 Anomaly Detection . 8 1.2.2 Trustworthy Artificial Intelligence . 10 1.2.3 Privacy Preservation . 10 1.3 Toolbox to Build Secure and Intelligent Systems . 11 1.3.1 Machine Learning and Deep Learning . 12 1.3.2 Privacy-Preserving Machine Learning . 14 1.3.3 Adversarial Machine Learning . 15 1.4 Data Repositories for Cybersecurity Research . 16 1.4.1 NSL-KDD . 17 1.4.2 UNSW-NB15 . 17 v 1.4.3 EMBER 18 1.5 Summary 18 2 Cyber Threats and Gateway Defense 19 2.1 Cyber Threats . 19 2.1.1 Cyber Intrusions . 20 2.1.2 Distributed Denial of Services Attack . 22 2.1.3 Malware and Shellcode . 23 2.2 Gateway Defense Approaches 23 2.2.1 Network Access Control 24 2.2.2 Anomaly Isolation 24 2.2.3 Collaborative Learning . 24 2.2.4 Secure Local Data Learning 25 2.3 Emerging Data-Driven Methods for Gateway Defense 26 2.3.1 Semi-Supervised Learning for Intrusion Detection 26 2.3.2 Transfer Learning for Intrusion Detection 27 2.3.3 Federated Learning for Privacy Preservation . 28 2.3.4 Reinforcement Learning for Penetration Test 29 2.4 Case Study: Reinforcement Learning for Automated Post-Breach Penetration Test . 30 2.4.1 Literature Review 30 2.4.2 Research Idea 31 2.4.3 Training Agent using Deep Q-Learning 32 2.5 Summary 34 vi 3 Edge Computing and Secure Edge Intelligence 35 3.1 Edge Computing . 35 3.2 Key Advances in Edge Computing . 38 3.2.1 Security 38 3.2.2 Reliability . 41 3.2.3 Survivability . 42 3.3 Secure Edge Intelligence . 43 3.3.1 Background and Motivation 44 3.3.2 Design of Detection Module 45 3.3.3 Challenges against Poisoning Attacks . 48 3.4 Summary 49 4 Edge Intelligence for Intrusion Detection 51 4.1 Edge Cyberinfrastructure . 51 4.2 Edge AI Engine 53 4.2.1 Feature Engineering . 53 4.2.2 Model Learning . 54 4.2.3 Model Update 56 4.2.4 Predictive Analytics . 56 4.3 Threat Intelligence 57 4.4 Preliminary Study . 57 4.4.1 Dataset 57 4.4.2 Environment Setup . 59 4.4.3 Performance Evaluation . 59 vii 4.5 Summary 63 5 Robust Intrusion Detection 65 5.1 Preliminaries 65 5.1.1 Median Absolute Deviation . 65 5.1.2 Mahalanobis Distance 66 5.2 Robust Intrusion Detection . 67 5.2.1 Problem Formulation 67 5.2.2 Step 1: Robust Data Preprocessing 68 5.2.3 Step 2: Bagging for Labeled Anomalies 69 5.2.4 Step 3: One-Class SVM for Unlabeled Samples . 70 5.2.5 Step 4: Final Classifier . 74 5.3 Experiment and Evaluation . 76 5.3.1 Experiment Setup 76 5.3.2 Performance Evaluation . 81 5.4 Summary 92 6 Efficient Preprocessing Scheme for Anomaly Detection 93 6.1 Efficient Anomaly Detection . 93 6.1.1 Related Work . 95 6.1.2 Principal Component Analysis . 97 6.2 Efficient Preprocessing Scheme for Anomaly Detection . 98 6.2.1 Robust Preprocessing Scheme . 99 6.2.2 Real-Time Processing 103 viii 6.2.3 Discussions 103 6.3 Case Study . 104 6.3.1 Description of the Raw Data 105 6.3.2 Experiment 106 6.3.3 Results 108 6.4 Summary 109 7 Privacy Preservation in the Era of Big Data 111 7.1 Privacy Preservation Approaches 111 7.1.1 Anonymization 111 7.1.2 Differential Privacy . 112 7.1.3 Federated Learning . 114 7.1.4 Homomorphic Encryption 116 7.1.5 Secure Multi-Party Computation . 117 7.1.6 Discussions 118 7.2 Privacy-Preserving Anomaly Detection . 120 7.2.1 Literature Review 121 7.2.2 Preliminaries . 123 7.2.3 System Model and Security Model 124 7.3 Objectives and Workflow . 126 7.3.1 Objectives . 126 7.3.2 Workflow . 128 7.4 Predicate Encryption based Anomaly Detection . 129 7.4.1 Procedures 129 ix 7.4.2 Development of Predicate . 131 7.4.3 Deployment of Anomaly Detection 132 7.5 Case Study and Evaluation . 134 7.5.1 Overhead . 134 7.5.2 Detection . 136 7.6 Summary 137 8 Adversarial Examples: Challenges and Solutions 139 8.1 Adversarial Examples . 139 8.1.1 Problem Formulation in Machine Learning 140 8.1.2 Creation of Adversarial Examples . 141 8.1.3 Targeted and Non-Targeted Attacks . 141 8.1.4 Black-Box and White-Box Attacks 142 8.1.5 Defenses against Adversarial Examples 142 8.2 Adversarial Attacks in Security Applications 143 8.2.1 Malware 143 8.2.2 Cyber Intrusions . 143 8.3 Case Study: Improving Adversarial Attacks Against Malware Detectors 144 8.3.1 Background 144 8.3.2 Adversarial Attacks on Malware Detectors 145 8.3.3 MalConv Architecture 147 8.3.4 Research Idea 148 8.4 Case Study: A Metric for Machine Learning Vulnerability to Adversarial Examples . 149 8.4.1 Background 149 8.4.2 Research Idea 150 8.5 Case Study: Protecting Smart Speakers from Adversarial Voice Commands . 153 8.5.1 Background 153 8.5.2 Challenges 154 8.5.3 Directions and Tasks 155 8.6 Summary 157 xi

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Book SynopsisINTELLIGENT SYSTEMS FOR REHABILITATION ENGINEERING Encapsulates different case studies where technology can be used as assistive technology for the physically challenged, visually and hearing impaired. Rehabilitation engineering includes the development of technological solutions and devices to assist individuals with disabilities, while also supporting the recovery of the disabled who have lost their physical and cognitive functions. These systems can be designed and built to meet a wide range of needs that can help individuals with mobility, communication, vision, hearing, and cognition. The growing technological developments in machine learning, deep learning, robotics, virtual intelligence, etc., play an important role in rehabilitation engineering. Intelligent Systems for Rehabilitation Engineering focuses on trending research of intelligent systems in rehabilitation engineering which involves the design and development of innovative technologies and techniques including rehabili

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Book SynopsisMachine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing Table of ContentsPreface xix Acknowledgment xxiii 1 Machine Learning–Based Data Analysis 1M. Deepika and K. Kalaiselvi 1.1 Introduction 1 1.2 Machine Learning for the Internet of Things Using Data Analysis 4 1.2.1 Computing Framework 6 1.2.2 Fog Computing 6 1.2.3 Edge Computing 6 1.2.4 Cloud Computing 7 1.2.5 Distributed Computing 7 1.3 Machine Learning Applied to Data Analysis 7 1.3.1 Supervised Learning Systems 8 1.3.2 Decision Trees 9 1.3.3 Decision Tree Types 9 1.3.4 Unsupervised Machine Learning 10 1.3.5 Association Rule Learning 10 1.3.6 Reinforcement Learning 10 1.4 Practical Issues in Machine Learning 11 1.5 Data Acquisition 12 1.6 Understanding the Data Formats Used in Data Analysis Applications 13 1.7 Data Cleaning 14 1.8 Data Visualization 15 1.9 Understanding the Data Analysis Problem-Solving Approach 15 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 16 1.11 Statistical Data Analysis Techniques 17 1.11.1 Hypothesis Testing 18 1.11.2 Regression Analysis 18 1.12 Text Analysis and Visual and Audio Analysis 18 1.13 Mathematical and Parallel Techniques for Data Analysis 19 1.13.1 Using Map-Reduce 20 1.13.2 Leaning Analysis 20 1.13.3 Market Basket Analysis 21 1.14 Conclusion 21 References 22 2 Machine Learning for Cyber-Immune IoT Applications 25Suchismita Sahoo and Sushree Sangita Sahoo 2.1 Introduction 25 2.2 Some Associated Impactful Terms 27 2.2.1 IoT 27 2.2.2 IoT Device 28 2.2.3 IoT Service 29 2.2.4 Internet Security 29 2.2.5 Data Security 30 2.2.6 Cyberthreats 31 2.2.7 Cyber Attack 31 2.2.8 Malware 32 2.2.9 Phishing 32 2.2.10 Ransomware 33 2.2.11 Spear-Phishing 33 2.2.12 Spyware 34 2.2.13 Cybercrime 34 2.2.14 IoT Cyber Security 35 2.2.15 IP Address 36 2.3 Cloud Rationality Representation 36 2.3.1 Cloud 36 2.3.2 Cloud Data 37 2.3.3 Cloud Security 38 2.3.4 Cloud Computing 38 2.4 Integration of IoT With Cloud 40 2.5 The Concepts That Rules Over 41 2.5.1 Artificial Intelligent 41 2.5.2 Overview of Machine Learning 41 2.5.2.1 Supervised Learning 41 2.5.2.2 Unsupervised Learning 42 2.5.3 Applications of Machine Learning in Cyber Security 43 2.5.4 Applications of Machine Learning in Cybercrime 43 2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 43 2.5.6 Distributed Denial-of-Service 44 2.6 Related Work 45 2.7 Methodology 46 2.8 Discussions and Implications 48 2.9 Conclusion 49 References 49 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh 3.1 Introduction 53 3.2 Related Work 55 3.3 Predictive Data Analytics in Retail 56 3.3.1 ML for Predictive Data Analytics 58 3.3.2 Use Cases 59 3.3.3 Limitations and Challenges 61 3.4 Proposed Model 61 3.4.1 Case Study 63 3.5 Conclusion and Future Scope 68 References 69 4 Emerging Cloud Computing Trends for Business Transformation 71Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy 4.1 Introduction 71 4.1.1 Computing Definition Cloud 72 4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 73 4.1.3 Limitations of Cloud Computing 74 4.2 History of Cloud Computing 74 4.3 Core Attributes of Cloud Computing 75 4.4 Cloud Computing Models 77 4.4.1 Cloud Deployment Model 77 4.4.2 Cloud Service Model 79 4.5 Core Components of Cloud Computing Architecture: Hardware and Software 83 4.6 Factors Need to Consider for Cloud Adoption 84 4.6.1 Evaluating Cloud Infrastructure 84 4.6.2 Evaluating Cloud Provider 85 4.6.3 Evaluating Cloud Security 86 4.6.4 Evaluating Cloud Services 86 4.6.5 Evaluating Cloud Service Level Agreements (SLA) 87 4.6.6 Limitations to Cloud Adoption 87 4.7 Transforming Business Through Cloud 88 4.8 Key Emerging Trends in Cloud Computing 89 4.8.1 Technology Trends 90 4.8.2 Business Models 92 4.8.3 Product Transformation 92 4.8.4 Customer Engagement 92 4.8.5 Employee Empowerment 93 4.8.6 Data Management and Assurance 93 4.8.7 Digitalization 93 4.8.8 Building Intelligence Cloud System 93 4.8.9 Creating Hyper-Converged Infrastructure 94 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 94 4.10 Conclusion 95 References 96 5 Security of Sensitive Data in Cloud Computing 99Kirti Wanjale, Monika Mangla and Paritosh Marathe 5.1 Introduction 100 5.1.1 Characteristics of Cloud Computing 100 5.1.2 Deployment Models for Cloud Services 101 5.1.3 Types of Cloud Delivery Models 102 5.2 Data in Cloud 102 5.2.1 Data Life Cycle 103 5.3 Security Challenges in Cloud Computing for Data 105 5.3.1 Security Challenges Related to Data at Rest 106 5.3.2 Security Challenges Related to Data in Use 107 5.3.3 Security Challenges Related to Data in Transit 107 5.4 Cross-Cutting Issues Related to Network in Cloud 108 5.5 Protection of Data 109 5.6 Tighter IAM Controls 114 5.7 Conclusion and Future Scope 117 References 117 6 Cloud Cryptography for Cloud Data Analytics in IoT 119N. Jayashri and K. Kalaiselvi 6.1 Introduction 120 6.2 Cloud Computing Software Security Fundamentals 120 6.3 Security Management 122 6.4 Cryptography Algorithms 123 6.4.1 Types of Cryptography 123 6.5 Secure Communications 127 6.6 Identity Management and Access Control 133 6.7 Autonomic Security 137 6.8 Conclusion 139 References 139 7 Issues and Challenges of Classical Cryptography in Cloud Computing 143Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul 7.1 Introduction 144 7.1.1 Problem Statement and Motivation 145 7.1.2 Contribution 146 7.2 Cryptography 146 7.2.1 Cryptography Classification 147 7.2.1.1 Classical Cryptography 147 7.2.1.2 Homomorphic Encryption 149 7.3 Security in Cloud Computing 150 7.3.1 The Need for Security in Cloud Computing 151 7.3.2 Challenges in Cloud Computing Security 152 7.3.3 Benefits of Cloud Computing Security 153 7.3.4 Literature Survey 154 7.4 Classical Cryptography for Cloud Computing 157 7.4.1 RSA 157 7.4.2 AES 157 7.4.3 DES 158 7.4.4 Blowfish 158 7.5 Homomorphic Cryptosystem 158 7.5.1 Paillier Cryptosystem 159 7.5.1.1 Additive Homomorphic Property 159 7.5.2 RSA Homomorphic Cryptosystem 160 7.5.2.1 Multiplicative Homomorphic Property 160 7.6 Implementation 160 7.7 Conclusion and Future Scope 162 References 162 8 Cloud-Based Data Analytics for Monitoring Smart Environments 167D. Karthika 8.1 Introduction 167 8.2 Environmental Monitoring for Smart Buildings 169 8.2.1 Smart Environments 169 8.3 Smart Health 171 8.3.1 Description of Solutions in General 171 8.3.2 Detection of Distress 172 8.3.3 Green Protection 173 8.3.4 Medical Preventive/Help 174 8.4 Digital Network 5G and Broadband Networks 174 8.4.1 IoT-Based Smart Grid Technologies 174 8.5 Emergent Smart Cities Communication Networks 175 8.5.1 RFID Technologies 177 8.5.2 Identifier Schemes 177 8.6 Smart City IoT Platforms Analysis System 177 8.7 Smart Management of Car Parking in Smart Cities 178 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 178 8.9 Virtual Integrated Storage System 179 8.10 Convolutional Neural Network (CNN) 181 8.10.1 IEEE 802.15.4 182 8.10.2 BLE 182 8.10.3 ITU-T G.9959 (Z-Wave) 183 8.10.4 NFC 183 8.10.5 LoRaWAN 184 8.10.6 Sigfox 184 8.10.7 NB-IoT 184 8.10.8 PLC 184 8.10.9 MS/TP 184 8.11 Challenges and Issues 185 8.11.1 Interoperability and Standardization 185 8.11.2 Customization and Adaptation 186 8.11.3 Entity Identification and Virtualization 187 8.11.4 Big Data Issue in Smart Environments 187 8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 188 8.13 Case Study 189 8.14 Conclusion 191 References 191 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 195Nidhi Rajak and Ranjit Rajak 9.1 Introduction 195 9.2 Workflow Model 197 9.3 System Computing Model 198 9.4 Major Objective of Scheduling 198 9.5 Task Computational Attributes for Scheduling 198 9.6 Performance Metrics 200 9.7 Heuristic Task Scheduling Algorithms 201 9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 202 9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 208 9.7.3 As Late As Possible (ALAP) Algorithm 213 9.7.4 Performance Effective Task Scheduling (PETS) Algorithm 217 9.8 Performance Analysis and Results 220 9.9 Conclusion 224 References 224 10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study 227Pradnya S. Borkar and Reena Thakur 10.1 Introduction 228 10.1.1 Internet of Things 229 10.1.2 Cloud Computing 230 10.1.3 Environmental Monitoring 232 10.2 Background and Motivation 234 10.2.1 Challenges and Issues 234 10.2.2 Technologies Used for Designing Cloud-Based Data Analytics 240 10.2.2.1 Communication Technologies 241 10.2.3 Cloud-Based Data Analysis Techniques and Models 243 10.2.3.1 MapReduce for Data Analysis 243 10.2.3.2 Data Analysis Workflows 246 10.2.3.3 NoSQL Models 247 10.2.4 Data Mining Techniques 248 10.2.5 Machine Learning 251 10.2.5.1 Significant Importance of Machine Learning and Its Algorithms 253 10.2.6 Applications 253 10.3 Conclusion 261 References 262 11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care 273Aradhana Behura, Shibani Sahu and Manas Ranjan Kabat 11.1 Introduction 274 11.2 Survey on Architectural WBAN 278 11.3 Suggested Strategies 280 11.3.1 System Overview 280 11.3.2 Motivation 281 11.3.3 DSCB Protocol 281 11.3.3.1 Network Topology 282 11.3.3.2 Starting Stage 282 11.3.3.3 Cluster Evolution 282 11.3.3.4 Sensed Information Stage 283 11.3.3.5 Choice of Forwarder Stage 283 11.3.3.6 Energy Consumption as Well as Routing Stage 285 11.4 CNN-Based Image Segmentation (UNet Model) 287 11.5 Emerging Trends in IoT Healthcare 290 11.6 Tier Health IoT Model 294 11.7 Role of IoT in Big Data Analytics 294 11.8 Tier Wireless Body Area Network Architecture 296 11.9 Conclusion 303 References 303 12 Study on Green Cloud Computing—A Review 307Meenal Agrawal and Ankita Jain 12.1 Introduction 307 12.2 Cloud Computing 308 12.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 308 12.3 Features of Cloud Computing 309 12.4 Green Computing 309 12.5 Green Cloud Computing 309 12.6 Models of Cloud Computing 310 12.7 Models of Cloud Services 310 12.8 Cloud Deployment Models 311 12.9 Green Cloud Architecture 312 12.10 Cloud Service Providers 312 12.11 Features of Green Cloud Computing 313 12.12 Advantages of Green Cloud Computing 313 12.13 Limitations of Green Cloud Computing 314 12.14 Cloud and Sustainability Environmental 315 12.15 Statistics Related to Cloud Data Centers 315 12.16 The Impact of Data Centers on Environment 315 12.17 Virtualization Technologies 316 12.18 Literature Review 316 12.19 The Main Objective 318 12.20 Research Gap 319 12.21 Research Methodology 319 12.22 Conclusion and Suggestions 320 12.23 Scope for Further Research 320 References 321 13 Intelligent Reclamation of Plantae Affliction Disease 323Reshma Banu, G.F Ali Ahammed and Ayesha Taranum 13.1 Introduction 324 13.2 Existing System 327 13.3 Proposed System 327 13.4 Objectives of the Concept 328 13.5 Operational Requirements 328 13.6 Non-Operational Requirements 329 13.7 Depiction Design Description 330 13.8 System Architecture 330 13.8.1 Module Characteristics 331 13.8.2 Convolutional Neural System 332 13.8.3 User Application 332 13.9 Design Diagrams 333 13.9.1 High-Level Design 333 13.9.2 Low-Level Design 333 13.9.3 Test Cases 335 13.10 Comparison and Screenshot 335 13.11 Conclusion 342 References 342 14 Prediction of Stock Market Using Machine Learning–Based Data Analytics 347Maheswari P. and Jaya A. 14.1 Introduction of Stock Market 348 14.1.1 Impact of Stock Prices 349 14.2 Related Works 350 14.3 Financial Prediction Systems Framework 352 14.3.1 Conceptual Financial Prediction Systems 352 14.3.2 Framework of Financial Prediction Systems Using Machine Learning 353 14.3.2.1 Algorithm to Predicting the Closing Price of the Given Stock Data Using Linear Regression 355 14.3.3 Framework of Financial Prediction Systems Using Deep Learning 355 14.3.3.1 Algorithm to Predict the Closing Price of the Given Stock Using Long Short-Term Memory 356 14.4 Implementation and Discussion of Result 357 14.4.1 Pharmaceutical Sector 357 14.4.1.1 Cipla Limited 357 14.4.1.2 Torrent Pharmaceuticals Limited 359 14.4.2 Banking Sector 359 14.4.2.1 ICICI Bank 359 14.4.2.2 State Bank of India 359 14.4.3 Fast-Moving Consumer Goods Sector 362 14.4.3.1 ITC 363 14.4.3.2 Hindustan Unilever Limited 363 14.4.4 Power Sector 363 14.4.4.1 Adani Power Limited 363 14.4.4.2 Power Grid Corporation of India Limited 364 14.4.5 Automobiles Sector 368 14.4.5.1 Mahindra & Mahindra Limited 368 14.4.5.2 Maruti Suzuki India Limited 368 14.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 368 14.5 Conclusion 371 14.5.1 Future Enhancement 372 References 372 Web Citations 373 15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR 375Soumyadev Mukherjee, Harshit Anand, Nishan Acharya, Subham Char, Pritam Ghosh and MinakhiRout 15.1 Introduction 376 15.2 Basic Concepts 377 15.3 Study of Literature Survey and Technology 380 15.4 Proposed Model 381 15.5 Implementation and Results 383 15.6 Conclusion 389 References 389 16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions 391Upinder Kaur and Shalu 16.1 Introduction 392 16.1.1 Aim 393 16.1.2 Research Contribution 395 16.1.3 Organization 396 16.2 Background 396 16.2.1 Blockchain 397 16.2.2 Internet of Things (IoT) 398 16.2.3 5G Future Generation Cellular Networks 398 16.2.4 Machine Learning and Deep Learning Techniques 399 16.2.5 Deep Reinforcement Learning 399 16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 401 16.3.1 Resource Management in Blockchain for 5G Cellular Networks 402 16.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 402 16.4 Future Research Challenges 413 16.4.1 Blockchain Technology 413 16.4.1.1 Scalability 414 16.4.1.2 Efficient Consensus Protocols 415 16.4.1.3 Lack of Skills and Experts 415 16.4.2 IoT Networks 416 16.4.2.1 Heterogeneity of IoT and 5G Data 416 16.4.2.2 Scalability Issues 416 16.4.2.3 Security and Privacy Issues 416 16.4.3 5G Future Generation Networks 416 16.4.3.1 Heterogeneity 416 16.4.3.2 Security and Privacy 417 16.4.3.3 Resource Utilization 417 16.4.4 Machine Learning and Deep Learning 417 16.4.4.1 Interpretability 418 16.4.4.2 Training Cost for ML and DRL Techniques 418 16.4.4.3 Lack of Availability of Data Sets 418 16.4.4.4 Avalanche Effect for DRL Approach 419 16.4.5 General Issues 419 16.4.5.1 Security and Privacy Issues 419 16.4.5.2 Storage 419 16.4.5.3 Reliability 420 16.4.5.4 Multitasking Approach 420 16.5 Conclusion and Discussion 420 References 422 17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence 429Riya Sharma, Komal Saxena and Ajay Rana 17.1 Introduction 430 17.2 Applications of Machine Learning in Data Management Possibilities 431 17.2.1 Terminology of Basic Machine Learning 432 17.2.2 Rules Based on Machine Learning 434 17.2.3 Unsupervised vs. Supervised Methodology 434 17.3 Solutions to Improve Unsupervised Learning Using Machine Learning 436 17.3.1 Insufficiency of Labeled Data 436 17.3.2 Overfitting 437 17.3.3 A Closer Look Into Unsupervised Algorithms 437 17.3.3.1 Reducing Dimensionally 437 17.3.3.2 Principal Component Analysis 438 17.3.4 Singular Value Decomposition (SVD) 439 17.3.4.1 Random Projection 439 17.3.4.2 Isomax 439 17.3.5 Dictionary Learning 439 17.3.6 The Latent Dirichlet Allocation 440 17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 440 17.4.1 TensorFlow 441 17.4.2 Keras 441 17.4.3 Scikit-Learn 441 17.4.4 Microsoft Cognitive Toolkit 442 17.4.5 Theano 442 17.4.6 Caffe 442 17.4.7 Torch 442 17.5 Applications of Unsupervised Learning 443 17.5.1 Regulation of Digital Data 443 17.5.2 Machine Learning in Voice Assistance 443 17.5.3 For Effective Marketing 444 17.5.4 Advancement of Cyber Security 444 17.5.5 Faster Computing Power 444 17.5.6 The Endnote 445 17.6 Applications Using Machine Learning Algos 445 17.6.1 Linear Regression 445 17.6.2 Logistic Regression 446 17.6.3 Decision Tree 446 17.6.4 Support Vector Machine (SVM) 446 17.6.5 Naive Bayes 446 17.6.6 K-Nearest Neighbors 447 17.6.7 K-Means 447 17.6.8 Random Forest 447 17.6.9 Dimensionality Reduction Algorithms 448 17.6.10 Gradient Boosting Algorithms 448 References 449 18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System 461Deepa Kumari, B.S.A.S. Rajita, Medindrao Raja Sekhar, Ritika Garg and Subhrakanta Panda 18.1 Introduction 462 18.1.1 Transitional Healthcare Services and Their Challenges 462 18.2 Gamification in Transitional Healthcare: A New Model 463 18.2.1 Anthropomorphic Interface With Gamification 464 18.2.2 Gamification in Blockchain 465 18.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 466 18.3 Existing Related Work 468 18.4 The Framework 478 18.4.1 Health Player 479 18.4.2 Data Collection 480 18.4.3 Anthropomorphic Gamification Layers 480 18.4.4 Ethereum 480 18.4.4.1 Ethereum-Based Smart Contracts for Healthcare 481 18.4.4.2 Installation of Ethereum Smart Contract 481 18.4.5 Reward Model 482 18.4.6 Predictive Models 482 18.5 Implementation 483 18.5.1 Methodology 483 18.5.2 Result Analysis 484 18.5.3 Threats to the Validity 486 18.6 Conclusion 487 References 487 Index 491

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Book SynopsisDIGITAL CITIES ROADMAP This book details applications of technology to efficient digital city infrastructure and its planning, including smart buildings.Rapid urbanization, demographic changes, environmental changes, and new technologies are changing the views of urban leaders on sustainability, as well as creating and providing public services to tackle these new dynamics. Sustainable development is an objective by which the processes of planning, implementing projects, and development is aimed at meeting the needs of modern communities without compromising the potential of future generations. The advent of Smart Cities is the answer to these problems.Digital Cities Roadmap provides an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. The book also highlights smart automation technologies that help save energy, as well as various performance indicators needed to make construction easier. The book aims to creatTable of ContentsPreface xix 1 The Use of Machine Learning for Sustainable and Resilient Buildings 1Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal 1.1 Introduction of ML Sustainable Resilient Building 2 1.2 Related Works 2 1.3 Machine Learning 5 1.4 What is Resilience? 6 1.4.1 Sustainability and Resiliency Conditions 7 1.4.2 Paradigm and Challenges of Sustainability and Resilience 7 1.4.3 Perspectives of Local Community 9 1.5 Sustainability and Resilience of Engineered System 12 1.5.1 Resilience and Sustainable Development Framework for Decision-Making 13 1.5.2 Exposures and Disturbance Events 15 1.5.3 Quantification of Resilience 15 1.5.4 Quantification of Sustainability 16 1.6 Community and Quantification Metrics, Resilience and Sustainability Objectives 17 1.6.1 Definition of Quantification Metric 18 1.6.2 Considering and Community 19 1.7 Structure Engineering Dilemmas and Resilient Epcot 21 1.7.1 Dilation of Resilience Essence 21 1.7.2 Quality of Life 22 1.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building 27 1.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard 28 1.10 Machine Learning With Smart Building 29 1.10.1 Smart Building Appliances 29 1.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB) 29 1.10.3 Level if Clouds are the IoT Institute Level With SBs 31 1.10.4 Component of Smart Buildings (SB) 33 1.10.5 Machine Learning Tasks in Smart Building Environment 46 1.10.6 ML Tools and Services for Smart Building 47 1.10.7 Big Data Research Applications for SBs in Real-Time 51 1.10.8 Implementation of the ML Concept in the SB Context 51 1.11 Conclusion and Future Research 53 References 58 2 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs) 63Sandhya Tarar and Namisha Bhasin 2.1 Introduction 64 2.1.1 Bluetooth 65 2.1.2 Unmanned Aerial Vehicle 65 2.1.3 Sensors 65 2.1.4 Problem Description 67 2.2 Literature Review 68 2.3 Experimental Methods 71 2.3.1 Univariate Time-Series 73 2.3.1.1 Naïve Bayes 74 2.3.1.2 Simple Average 74 2.3.1.3 Moving Average 75 2.3.1.4 Simple Exponential Smoothing (SES) 76 2.3.1.5 Holt’s Linear Trend 76 2.3.1.6 Holt–Winters Method 76 2.3.1.7 Autoregressive Integrated Moving Average Model (ARIMA) 77 2.3.2 Multivariate Time-Series Prediction 80 2.3.2.1 Vector Autoregressive (VAR) 80 2.3.3 Hidden Markov Model (HMM) 81 2.3.4 Fuzzy Logic 85 2.4 Results 89 2.5 Conclusion and Future Work 89 References 90 3 Sustainable Infrastructure Theories and Models 97Saurabh Jain, Keshav Kaushik, Deepak Kumar Sharma, Rajalakshmi Krishnamurthi and Adarsh Kumar 3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure 98 3.1.1 The Need for Sustainable Infrastructure 98 3.1.2 Data Fusion 99 3.1.3 Different Types of Data Fusion Architecture 100 3.1.3.1 Centralized Architecture 100 3.1.3.2 Decentralized Architecture 101 3.1.3.3 Distributed Architecture 101 3.1.3.4 Hierarchical Architecture 102 3.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques 102 3.2 Smart City Infrastructure Approaches 104 3.2.1 Smart City Infrastructure 104 3.2.2 Smart City IoT Deployments 105 3.2.3 Smart City Control and Monitoring Centers 106 3.2.4 Theory of Unified City Modeling for Smart Infrastructure 108 3.2.5 Smart City Operational Modeling 109 3.3 Theories and Models 110 3.3.1 Sustainable Infrastructure Theories 110 3.3.2 Sustainable Infrastructure Models 112 3.4 Case Studies 113 3.4.1 Case Studies-1: Web Browsing History Analysis 113 3.4.1.1 Objective 115 3.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement 117 3.5 Conclusion and Future Scope 121 References 122 4 Blockchain for Sustainable Smart Cities 127Iftikhar Ahmad, Syeda Warda Ashar, Umamma Khalid, Anmol Irfan and Wajeeha Khalil 4.1 Introduction 128 4.2 Smart City 130 4.2.1 Overview of Smart City 130 4.2.2 Evolution 130 4.2.3 Smart City’s Sub Systems 130 4.2.4 Domains of Smart City 132 4.2.5 Challenges 134 4.3 Blockchain 136 4.3.1 Motivation 137 4.3.2 The Birth of Blockchain 137 4.3.3 System of Blockchain 137 4.4 Use Cases of Smart City Implementing Blockchain 138 4.4.1 Blockchain-Based Smart Economy 138 4.4.1.1 Facilitating Faster and Cheaper International Payment 139 4.4.1.2 Distributed Innovations in Financial Transactions 139 4.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains 140 4.4.1.4 Equity Crowd Funding 141 4.4.2 Blockchain for Smart People 141 4.4.2.1 Elections through Blockchain Technology 141 4.4.2.2 Smart Contract 143 4.4.2.3 Protecting Personal Data 144 4.4.2.4 E-Health: Storing Health Records on Blockchain 145 4.4.2.5 Intellectual Property Rights 145 4.4.2.6 Digital Payments 146 4.4.2.7 Other Use Cases 146 4.4.3 Blockchain-Based Smart Governance 147 4.4.3.1 Transparent Record Keeping and Tracking of Records 147 4.4.3.2 Fraud Free Voting 148 4.4.3.3 Decision Making 150 4.4.4 Blockchain-Based Smart Transport 150 4.4.4.1 Digitizing Driving License 150 4.4.4.2 Smart Ride Sharing 150 4.4.5 Blockchain-Based Smart Environment 151 4.4.5.1 Social Plastic 151 4.4.5.2 Energy 152 4.4.5.3 Environmental Treaties 152 4.4.5.4 Carbon Tax 153 4.4.6 Blockchain-Based Smart Living 153 4.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices 154 4.4.6.2 Managing Change in Ownership 154 4.4.6.3 Sustainable Buildings 154 4.4.6.4 Other Use Cases 155 4.5 Conclusion 156 References 156 5 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework 163Nitin K. Tyagi and Mukta Goyal 5.1 Introduction 164 5.2 Related Works 166 5.2.1 Research Questions 166 5.3 Related E-Governance Frameworks 178 5.3.1 Smart City Features in India 181 5.4 Proposed Smart Governance Framework 181 5.5 Results Discussion 185 5.5.1 Initial Stage 185 5.5.2 Design, Development and Delivery Stage 186 5.6 Conclusion 186 References 188 6 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly 193Shubhi Sonal and Anupadma R. 6.1 Introduction to Geriatric Design 194 6.1.1 Aim, Objectives, and Methodology 196 6.1.2 Organization of Chapter 197 6.2 Background 197 6.2.1 Development of Smart Homes 197 6.2.2 Development of Smart Homes for Elderly 198 6.2.3 Indian Scenario 200 6.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping 201 6.3.1 Geriatric Smart Home Design: The Indian Context 202 6.3.2 Elderly Activity Mapping 202 6.3.3 Framework for Smart Homes for Elderly People 206 6.3.4 Architectural Interventions: Spatial Requirements for Daily Activities 207 6.3.5 Architectural Interventions to Address Issues Faced by Elderly People 208 6.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People 208 6.4.1 IoT-Based Real Time Automation for Nesting Homes 208 6.4.2 Technological Components of Elderly Smart Homes 212 6.4.2.1 Sensors for Smart Home 212 6.4.2.2 Health Monitoring System 213 6.4.2.3 Network Devices 213 6.4.2.4 Alerts 214 6.5 Worldwide Elderly Smart Homes 214 6.5.1 Challenges in Smart Elderly Homes 215 6.6 Conclusion and Future Scope 216 References 216 7 Sustainable E-Infrastructure for Blockchain-Based Voting System 221Mukta Goyal and Adarsh Kumar 7.1 Introduction 222 7.1.1 E-Voting Challenge 224 7.2 Related Works 224 7.3 System Design 227 7.4 Experimentation 230 7.4.1 Software Requirements 230 7.4.2 Function Requirements 230 7.4.2.1 Election Organizer 231 7.4.2.2 Candidate Registration 231 7.4.2.3 Voter Registration Process 232 7.4.3 Common Functional Requirement for All Users 233 7.4.3.1 Result Display 233 7.4.4 Non-Function Requirements 233 7.4.4.1 Performance Requirement 233 7.4.4.2 Security Requirement 233 7.4.4.3 Usability Requirement 233 7.4.4.4 Availability Requirement 234 7.4.5 Implementation Details 234 7.5 Findings & Results 237 7.5.1 Smart Contract Deployment 241 7.6 Conclusion and Future Scope 242 Acknowledgement 246 References 246 8 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges 253K. Rajkumar and U. Hariharan 8.1 Introduction 254 8.2 Recent Development in IoT Application for Modern City 256 8.2.1 IoT Potential Smart City Approach 257 8.2.2 Problems and Related Solutions in Modern Smart Cities Application 259 8.3 Classification of IoT-Based Smart Cities 262 8.3.1 Program Developers 263 8.3.2 Network Type 263 8.3.3 Activities of Standardization Bodies of Smart City 263 8.3.4 Available Services 269 8.3.5 Specification 269 8.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing 270 8.4.1 IoT Five-Layer Architecture for Smart City Applications 270 8.4.1.1 Sensing Layer (Get Information from Sensor) 272 8.4.1.2 Network Layer (Access and Also Transmit Information) 272 8.4.1.3 Data Storage and Analyzing 273 8.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model) 273 8.4.1.5 Application Layer (Dedicated Apps and Services) 273 8.4.2 IoT Computing Paradigm for Smart City Application 274 8.5 Research Advancement and Drawback on Smart Cities 280 8.5.1 Integration of Cloud Computing in Smart Cities 280 8.5.2 Integration of Applications 281 8.5.3 System Security 281 8.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines 282 8.7 Conclusion and Future Direction 287 References 288 9 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being 293Ankita Banerjee, N.P. Melkania and Ayushi Nain 9.1 Introduction 294 9.2 Pollutants Responsible for Poor IAQ 296 9.2.1 Volatile Organic Compounds (VOCs) 296 9.2.2 Particulate Matter (PM) 298 9.2.3 Asbestos 299 9.2.4 Carbon Monoxide (CO) 299 9.2.5 Environmental Tobacco Smoke (ETS) 300 9.2.6 Biological Pollutants 301 9.2.7 Lead (Pb) 303 9.2.8 Nitrogen Dioxide (NO2) 304 9.2.9 Ozone (O3) 305 9.3 Health Impacts of Poor IAQ 306 9.3.1 Sick Building Syndrome (SBS) 306 9.3.2 Acute Impacts 307 9.3.3 Chronic Impacts 308 9.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings 308 9.5 Conclusion and Future Scope 313 References 314 10 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization 319Pooja Choudhary, Lava Bhargava, Ashok Kumar Suhag, Manju Choudhary and Satendra Singh 10.1 Introduction: Emergence of a Smart City Concept 320 10.2 Components of Smart City 321 10.2.1 Smart Infrastructure 323 10.2.2 Smart Building 323 10.2.3 Smart Transportation 325 10.2.4 Smart Energy 326 10.2.5 Smart Health Care 327 10.2.6 Smart Technology 328 10.2.7 Smart Citizen 329 10.2.8 Smart Governance 330 10.2.9 Smart Education 330 10.3 Role of IoT in Smart Cities 331 10.3.1 Intent of IoT Adoption in Smart Cities 333 10.3.2 IoT-Supported Communication Technologies 333 10.4 Sectors, Services Related and Principal Issues for IoT Technologies 336 10.5 Impact of Smart Cities 336 10.5.1 Smart City Impact on Science and Technology 336 10.5.2 Smart City Impact on Competitiveness 339 10.5.3 Smart City Impact on Society 339 10.5.4 Smart City Impact on Optimization and Management 339 10.5.5 Smart City for Sustainable Development 340 10.6 Key Applications of IoT in Smart Cities 340 10.7 Challenges 343 10.7.1 Smart City Design Challenges 343 10.7.2 Challenges Raised by Smart Cities 344 10.7.3 Challenges of IoT Technologies in Smart Cities 344 10.8 Conclusion 346 Acknowledgements 346 References 346 11 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure 351Rajalakshmi Krishnamurthi, Dhanalekshmi Gopinathan and Adarsh Kumar 11.1 Introduction 352 11.2 Smart City and IoT 354 11.3 Mobile Computing for Smart City 357 11.4 Smart City and its Applications 360 11.4.1 Traffic Monitoring 360 11.4.2 Smart Lighting 361 11.4.3 Air Quality Monitoring 362 11.5 Smart Tourism in Smart City 363 11.6 Mobile Computing-Based Smart Tourism 366 11.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure 368 11.7.1 System Interfaces and User Interfaces 371 11.8 Experimentation and Results Discussion 371 11.9 Conclusion and Future Scope 373 References 374 12 Smart Health Monitoring for Elderly Care in Indoor Environments 379Sonia and Tushar Semwal 12.1 Introduction 380 12.2 Sensors 382 12.2.1 Human Traits 383 12.2.2 Sensors Description 384 12.2.2.1 Passive Sensors 385 12.2.2.2 Active Sensors 386 12.2.3 Sensing Challenges 387 12.3 Internet of Things and Connected Systems 387 12.4 Applications 389 12.5 Case Study 392 12.5.1 Case 1 392 12.5.2 Case 2 393 12.5.3 Challenges Involved 393 12.5.4 Possible Solution 393 12.6 Conclusion 395 12.7 Discussion 395 References 395 13 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City 401Akansha Bhargava, Gauri Salunkhe, Sushant Bhargava and Prerna Goswami 13.1 Introduction 402 13.1.1 Organization of the Chapter 404 13.2 Related Works 405 13.3 Overview of IoT System in Smart Cities 407 13.3.1 Physical Devices 409 13.3.2 Connectivity 409 13.3.3 Middleware 410 13.3.4 Human Interaction 410 13.4 IoT Security Prerequisite 411 13.5 IoT Security Areas 413 13.5.1 Anomaly Detection 413 13.5.2 Host-Based IDS (HIDS) 414 13.5.3 Network-Based IDS (NIDS) 414 13.5.4 Malware Detection 414 13.5.5 Ransomware Detection 415 13.5.6 Intruder Detection 415 13.5.7 Botnet Detection 415 13.6 IoT Security Threats 416 13.6.1 Passive Threats 416 13.6.2 Active Threats 417 13.7 Review of ML/DL Application in IoT Security 418 13.7.1 Machine Learning Methods 421 13.7.1.1 Decision Trees (DTs) 421 13.7.1.2 K-Nearest Neighbor (KNN) 423 13.7.1.3 Random Forest 424 13.7.1.4 Principal Component Analysis (PCA) 425 13.7.1.5 Naïve Bayes 425 13.7.1.6 Support Vector Machines (SVM) 425 13.7.2 Deep Learning Methods 426 13.7.2.1 Convolutional Neural Networks (CNNs) 427 13.7.2.2 Auto Encoder (AE) 429 13.7.2.3 Recurrent Neural Networks (RNNs) 429 13.7.2.4 Restricted Boltzmann Machines (RBMs) 432 13.7.2.5 Deep Belief Networks (DBNs) 433 13.7.2.6 Generative Adversarial Networks (GANs) 433 13.8 Challenges 434 13.8.1 IoT Dataset Unavailability 434 13.8.2 Computational Complications 434 13.8.3 Forensics Challenges 435 13.9 Future Prospects 436 13.9.1 Implementation of ML/DL With Edge Computing 437 13.9.2 Integration of ML/DL With Blockchain 438 13.9.3 Integration of ML/DL With Fog Computing 439 13.10 Conclusion 439 References 440 14 Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities 449Tarana Singh, Arun Solanki and Sanjay Kumar Sharma 14.1 Introduction 449 14.1.1 Chapter Organization 453 14.2 Literature Review 453 14.3 Components of Smart Cities 455 14.3.1 Smart Infrastructure 455 14.3.2 Smart Parking Management 456 14.3.3 Connected Charging Stations 457 14.3.4 Smart Buildings and Properties 457 14.3.5 Smart Garden and Sprinkler Systems 457 14.3.6 Smart Heating and Ventilation 457 14.3.7 Smart Industrial Environment 458 14.3.8 Smart City Services 458 14.3.9 Smart Energy Management 458 14.3.10 Smart Water Management 459 14.3.11 Smart Waste Management 459 14.4 Characteristics of Smart Buildings 459 14.4.1 Minimal Human Control 459 14.4.2 Optimization 460 14.4.3 Qualities 460 14.4.4 Connected Systems 460 14.4.5 Use of Sensors 460 14.4.6 Automation 461 14.4.7 Data 461 14.5 Supporting Technology 461 14.5.1 Big Data and IoT in Smart Cities 461 14.5.2 Sensors 462 14.5.3 5G Connectivity 462 14.5.4 Geospatial Technology 462 14.5.5 Robotics 463 14.6 Key Performance Indicators of Smart City 463 14.6.1 Smart Economy 463 14.6.2 Smart Governance 464 14.6.3 Smart Mobility 464 14.6.4 Smart Environment 464 14.6.5 Smart People 464 14.6.6 Smart Living 465 14.7 Challenges While Working for Smart City 465 14.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart 465 14.7.2 Financing Smart Cities 466 14.7.3 Availability of Master Plan or City Development Plan 466 14.7.4 Financial Sustainability of ULBs 466 14.7.5 Technical Constraints ULBs 466 14.7.6 Three-Tier Governance 467 14.7.7 Providing Clearances in a Timely Manner 467 14.7.8 Dealing With a Multivendor Environment 467 14.7.9 Capacity Building Program 467 14.7.10 Reliability of Utility Services 468 14.8 Future Research Opportunities in Smart City 468 14.8.1 IoT Management 468 14.8.2 Data Management 469 14.8.3 Smart City Assessment Framework 469 14.8.4 VANET Security 469 14.8.5 Improving Photovoltaic Cells 469 14.8.6 Smart City Enablers 470 14.8.7 Information System Risks 470 14.9 Conclusion 470 References 471 15 Effects of Green Buildings on the Environment 477Ayushi Nain, Ankita Banerjee and N.P. Melkania 15.1 Introduction 478 15.2 Sustainability and the Building Industry 480 15.2.1 Environmental Benefits 481 15.2.2 Social Benefits 483 15.2.3 Economic Benefits 483 15.3 Goals of Green Buildings 484 15.3.1 Green Design 485 15.3.2 Energy Efficiency 485 15.3.3 Water Efficiency 487 15.3.4 Material Efficiency 489 15.3.5 Improved Internal Environment and Air Quality 490 15.3.6 Minimization of Wastes 492 15.3.7 Operations and Maintenance Optimization 492 15.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify 493 15.4.1 Energy Use in Buildings 494 15.4.2 Green House Gas (GHG) Emissions 494 15.4.3 Indoor Air Quality 494 15.4.4 Building Water Use 496 15.4.5 Use of Land and Consumption 496 15.4.6 Construction Materials 497 15.4.7 Construction and Demolition (C&D) Wastes 498 15.5 Green Buildings in India 498 15.6 Conclusion 503 Acknowledgement 504 Acronyms 504 References 505 Index 509

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Book SynopsisTable of ContentsPreface xvii Part 1: Introduction to Intelligent Healthcare Systems 1 1 Innovation on Machine Learning in Healthcare Services—An Introduction 3Parthasarathi Pattnayak and Om Prakash Jena 1.1 Introduction 3 1.2 Need for Change in Healthcare 5 1.3 Opportunities of Machine Learning in Healthcare 6 1.4 Healthcare Fraud 7 1.4.1 Sorts of Fraud in Healthcare 7 1.4.2 Clinical Service Providers 8 1.4.3 Clinical Resource Providers 8 1.4.4 Protection Policy Holders 8 1.4.5 Protection Policy Providers 9 1.5 Fraud Detection and Data Mining in Healthcare 9 1.5.1 Data Mining Supervised Methods 10 1.5.2 Data Mining Unsupervised Methods 10 1.6 Common Machine Learning Applications in Healthcare 10 1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11 1.6.2 Machine Learning in Patient Risk Stratification 11 1.6.3 Machine Learning in Telemedicine 11 1.6.4 AI (ML) Application in Sedate Revelation 12 1.6.5 Neuroscience and Image Computing 12 1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12 1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12 1.6.8 Machine Learning in Outbreak Prediction 13 1.7 Conclusion 13 References 14 Part 2: Machine Learning/Deep Learning-Based Model Development 17 2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy 2.1 Introduction 19 2.1.1 Health Status of an Individual 19 2.1.2 Activities and Measures of an Individual 20 2.1.3 Traditional Approach to Predict Health Status 20 2.2 Background 20 2.3 Problem Statement 21 2.4 Proposed Architecture 22 2.4.1 Pre-Processing 22 2.4.2 Phase-I 23 2.4.3 Phase-II 23 2.4.4 Dataset Generation 23 2.4.4.1 Rules Collection 23 2.4.4.2 Feature Selection 24 2.4.4.3 Feature Reduction 24 2.4.4.4 Dataset Generation From Rules 24 2.4.4.5 Example 24 2.4.5 Pre-Processing 26 2.5 Experimental Results 27 2.5.1 Performance Metrics 27 2.5.1.1 Accuracy 27 2.5.1.2 Precision 28 2.5.1.3 Recall 28 2.5.1.4 F1-Score 30 2.6 Conclusion 31 References 31 3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33S. Pal, P. Das, R. Sahu and S.R. Dash 3.1 Introduction 34 3.1.1 Why BCI 34 3.1.2 Human–Computer Interfaces 34 3.1.3 What is EEG 35 3.1.4 History of EEG 35 3.1.5 About Neuromarketing 35 3.1.6 About Machine Learning 36 3.2 Literature Survey 36 3.3 Methodology 45 3.3.1 Bagging Decision Tree Classifier 45 3.3.2 Gaussian Naïve Bayes Classifier 45 3.3.3 Kernel Support Vector Machine (Sigmoid) 45 3.3.4 Random Decision Forest Classifier 46 3.4 System Setup & Design 46 3.4.1 Pre-Processing & Feature Extraction 47 3.4.1.1 Savitzky–Golay Filter 47 3.4.1.2 Discrete Wavelet Transform 48 3.4.2 Dataset Description 49 3.5 Result 49 3.5.1 Individual Result Analysis 49 3.5.2 Comparative Results Analysis 52 3.6 Conclusion 53 References 54 4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis 57Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena 4.1 Introduction 57 4.2 Outline of Clinical DSS 59 4.2.1 Preliminaries 59 4.2.2 Types of Clinical DSS 60 4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 60 4.2.4 Knowledge-Based Decision Support System (K-DSS) 62 4.2.5 Hybrid Decision Support System (H-DSS) 64 4.2.6 DSS Architecture 64 4.3 Background 65 4.4 Proposed Expert System-Based CDSS 65 4.4.1 Problem Description 65 4.4.2 Rules Set & Knowledge Base 66 4.4.3 Inference Engine 66 4.5 Implementation & Testing 66 4.6 Conclusion 73 References 73 5 Deep Learning on Symptoms in Disease Prediction 77Sheikh Raul Islam, Rohit Sinha, Santi P. Maity and Ajoy Kumar Ray 5.1 Introduction 77 5.2 Literature Review 78 5.3 Mathematical Models 79 5.3.1 Graphs and Related Terms 80 5.3.2 Deep Learning in Graph 80 5.3.3 Network Embedding 80 5.3.4 Graph Neural Network 81 5.3.5 Graph Convolution Network 82 5.4 Learning Representation From DSN 82 5.4.1 Description of the Proposed Model 83 5.4.2 Objective Function 84 5.5 Results and Discussion 84 5.5.1 Description of the Dataset 85 5.5.2 Training Progress 85 5.5.3 Performance Comparisons 86 5.6 Conclusions and Future Scope 86 References 87 6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 89Rajitha B. 6.1 Introduction 89 6.1.1 Problems Intended in Video Surveillance Systems 90 6.1.2 Current Developments in This Area 91 6.1.3 Role of AI in Video Surveillance Systems 91 6.2 Public Safety and Video Surveillance Systems 92 6.2.1 Offline Crime Prevention 92 6.2.2 Crime Prevention and Identification via Apps 92 6.2.3 Crime Prevention and Identification via CCTV 92 6.3 Machine Learning for Public Safety 94 6.3.1 Abnormality Behavior Detection via Deep Learning 95 6.3.2 Video Analytics Methods for Accident Classification/Detection 97 6.3.3 Feature Selection and Fusion Methods 98 6.4 Securing the CCTV Data 99 6.4.1 Image/Video Security Challenges 99 6.4.2 Blockchain for Image/Video Security 99 6.5 Conclusion 99 References 100 7 Semantic Framework in Healthcare 103Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj 7.1 Introduction 103 7.2 Semantic Web Ontology 104 7.3 Multi-Agent System in a Semantic Framework 106 7.3.1 Existing Healthcare Semantic Frameworks 107 7.3.1.1 AOIS 107 7.3.1.2 SCKE 108 7.3.1.3 MASE 109 7.3.1.4 MET4 110 7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 111 7.3.2.1 Data Dictionary 111 7.3.2.2 Mapping Database 112 7.3.2.3 Decision Making Ontology 113 7.3.2.4 STTL and SPARQL-Based RDF Transformation 115 7.3.2.5 Query Optimizer Agent 116 7.3.2.6 Semantic Web Services Ontology 116 7.3.2.7 Web Application User Interface and Customer Agent 116 7.3.2.8 Translation Agent 117 7.3.2.9 RDF Translator 117 7.4 Conclusion 118 References 119 8 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 121Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia 8.1 Introduction 121 8.2 Materials & Methods 123 8.2.1 Subjects and Experimental Design 123 8.2.2 Data Pre-Processing & Statistical Analysis 125 8.2.3 Extracting Singularity Spectrum from EEG 126 8.3 Results & Discussion 126 8.4 Conclusion 132 Acknowledgement 133 References 133 9 Detection of Onset and Progression of Osteoporosis Using Machine Learning 137Shilpi Ruchi Kerketta and Debalina Ghosh 9.1 Introduction 137 9.1.1 Measurement Techniques of BMD 138 9.1.2 Machine Learning Algorithms in Healthcare 138 9.1.3 Organization of Chapter 139 9.2 Microwave Characterization of Human Osseous Tissue 139 9.2.1 Frequency-Domain Analysis of Human Wrist Sample 140 9.2.2 Data Collection and Analysis 141 9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 144 9.3.1 K-Nearest Neighbor (KNN) 144 9.3.2 Decision Tree 145 9.3.3 Random Forest 145 9.4 Conclusion 148 Acknowledgment 148 References 148 10 Applications of Machine Learning in Biomedical Text Processing and Food Industry 151K. Paramesha, Gururaj H.L. and Om Prakash Jena 10.1 Introduction 152 10.2 Use Cases of AI and ML in Healthcare 153 10.2.1 Speech Recognition (SR) 153 10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 153 10.2.3 Clinical Imaging and Diagnostics 153 10.2.4 Conversational AI in Healthcare 154 10.3 Use Cases of AI and ML in Food Technology 154 10.3.1 Assortment of Vegetables and Fruits 154 10.3.2 Personal Hygiene 154 10.3.3 Developing New Products 155 10.3.4 Plant Leaf Disease Detection 156 10.3.5 Face Recognition Systems for Domestic Cattle 156 10.3.6 Cleaning Processing Equipment 157 10.4 A Case Study: Sentiment Analysis of Drug Reviews 158 10.4.1 Dataset 159 10.4.2 Approaches for Sentiment Analysis on Drug Reviews 159 10.4.3 BoW and TF-IDF Model 160 10.4.4 Bi-LSTM Model 160 10.4.4.1 Word Embedding 160 10.4.5 Deep Learning Model 161 10.5 Results and Analysis 164 10.6 Conclusion 165 References 166 11 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model 169Subasish Mohapatra, N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty 11.1 Introduction 169 11.2 Our Skin Cancer Classifier Model 171 11.3 Skin Cancer Classifier Model Results 172 11.4 Hyperparameter Tuning and Performance 174 11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 175 11.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model 175 11.4.3 Table Summary of Hyperparameter Tuning Results 176 11.5 Comparative Analysis and Results 176 11.5.1 Training and Validation Loss 177 11.5.1.1 MobileNet 177 11.5.1.2 ResNet50 177 11.5.1.3 Inferences 177 11.5.2 Training and Validation Categorical Accuracy 178 11.5.2.1 MobileNet 178 11.5.2.2 ResNet50 178 11.5.2.3 Inferences 178 11.5.3 Training and Validation Top 2 Accuracy 179 11.5.3.1 MobileNet 179 11.5.3.2 ResNet50 179 11.5.3.3 Inferences 180 11.5.4 Training and Validation Top 3 Accuracy 180 11.5.4.1 MobileNet 180 11.5.4.2 ResNet50 180 11.5.4.3 Inferences 181 11.5.5 Confusion Matrix 181 11.5.5.1 MobileNet 181 11.5.5.2 ResNet50 181 11.5.5.3 Inferences 182 11.5.6 Classification Report 182 11.5.6.1 MobileNet 182 11.5.6.2 ResNet50 182 11.5.6.3 Inferences 183 11.5.7 Last Epoch Results 183 11.5.7.1 MobileNet 183 11.5.7.2 ResNet50 183 11.5.7.3 Inferences 184 11.5.8 Best Epoch Results 184 11.5.8.1 MobileNet 184 11.5.8.2 ResNet50 184 11.5.8.3 Inferences 184 11.5.9 Overall Comparative Analysis 184 11.6 Conclusion 185 References 185 12 Deep Learning-Based Image Classifier for Malaria Cell Detection 187Alok Negi, Krishan Kumar and Prachi Chauhan 12.1 Introduction 187 12.2 Related Work 189 12.3 Proposed Work 190 12.3.1 Dataset Description 191 12.3.2 Data Pre-Processing and Augmentation 191 12.3.3 CNN Architecture and Implementation 192 12.4 Results and Evaluation 194 12.5 Conclusion 196 References 197 13 Prediction of Chest Diseases Using Transfer Learning 199S. Baghavathi Priya, M. Rajamanogaran and S. Subha 13.1 Introduction 199 13.2 Types of Diseases 200 13.2.1 Pneumothorax 200 13.2.2 Pneumonia 200 13.2.3 Effusion 200 13.2.4 Atelectasis 201 13.2.5 Nodule and Mass 202 13.2.6 Cardiomegaly 202 13.2.7 Edema 202 13.2.8 Lung Consolidation 202 13.2.9 Pleural Thickening 202 13.2.10 Infiltration 202 13.2.11 Fibrosis 203 13.2.12 Emphysema 203 13.3 Diagnosis of Lung Diseases 204 13.4 Materials and Methods 204 13.4.1 Data Augmentation 206 13.4.2 CNN Architecture 206 13.4.3 Lung Disease Prediction Model 207 13.5 Results and Discussions 208 13.5.1 Implementation Results Using ROC Curve 209 13.6 Conclusion 210 References 212 14 Early Stage Detection of Leukemia Using Artificial Intelligence 215Neha Agarwal and Piyush Agrawal 14.1 Introduction 215 14.1.1 Classification of Leukemia 216 14.1.1.1 Acute Lymphocytic Leukemia 216 14.1.1.2 Acute Myeloid Leukemia 216 14.1.1.3 Chronic Lymphocytic Leukemia 216 14.1.1.4 Chronic Myeloid Leukemia 216 14.1.2 Diagnosis of Leukemia 216 14.1.3 Acute and Chronic Stages of Leukemia 217 14.1.4 The Role of AI in Leukemia Detection 217 14.2 Literature Review 219 14.3 Proposed Work 220 14.3.1 Modules Involved in Proposed Methodology 221 14.3.2 Flowchart 222 14.3.3 Proposed Algorithm 223 14.4 Conclusion and Future Aspects 223 References 223 Part 3: Internet of Medical Things (IoMT) for Healthcare 225 15 IoT Application in Interconnected Hospitals 227Subhra Debdas, Chinmoy Kumar Panigrahi, Priyasmita Kundu, Sayantan Kundu and Ramanand Jha 15.1 Introduction 228 15.2 Networking Systems Using IoT 229 15.3 What are Smart Hospitals? 233 15.3.1 Environment of a Smart Hospital 234 15.4 Assets 236 15.4.1 Overview of Smart Hospital Assets 236 15.4.2 Exigency of Automated Healthcare Center Assets 239 15.5 Threats 241 15.5.1 Emerging Vulnerabilities 241 15.5.2 Threat Analysis 244 15.6 Conclusion 246 References 246 16 Real Time Health Monitoring Using IoT With Integration of Machine Learning Approach 249K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh Raj 16.1 Introduction 250 16.2 Related Work 250 16.3 Existing Healthcare Monitoring System 251 16.4 Methodology and Data Analysis 251 16.5 Proposed System Architecture 252 16.6 Machine Learning Approach 252 16.6.1 Multiple Linear Regression Algorithm 253 16.6.2 Random Forest Algorithm 253 16.6.3 Support Vector Machine 253 16.7 Work Flow of the Proposed System 253 16.8 System Design of Health Monitoring System 256 16.9 Use Case Diagram 257 16.10 Conclusion 258 References 259 Part 4: Machine Learning Applications for COVID-19 261 17 Semantic and NLP-Based Retrieval From Covid-19 Ontology 263Ramar Kaladevi and Appavoo Revathi 17.1 Introduction 263 17.2 Related Work 264 17.3 Proposed Retrieval System 266 17.3.1 Why Ontology? 266 17.3.2 Covid Ontology 266 17.3.3 Information Retrieval From Ontology 269 17.3.4 Query Formulation 272 17.3.5 Retrieval From Knowledgebase 272 17.4 Conclusion 273 References 273 18 Semantic Behavior Analysis of COVID-19 Patients: A Collaborative Framework 277Amlan Mohanty, Debasish Kumar Mallick, Shantipriya Parida and Satya Ranjan Dash 18.1 Introduction 278 18.2 Related Work 280 18.2.1 Semantic Analysis and Topic Discovery of Alcoholic Patients From Social Media Platforms 280 18.2.2 Sentiment Analysis of Tweets From Twitter Handles of the People of Nepal in Response to the COVID-19 Pandemic 280 18.2.3 Study of Sentiment Analysis and Analyzing Scientific Papers 280 18.2.4 Informatics and COVID-19 Research 281 18.2.5 COVID-19 Outbreak in the World and Twitter Sentiment Analysis 281 18.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic 281 18.2.7 The First Decade of Research on Sentiment Analysis 282 18.2.8 Detailed Survey on the Semantic Analysis Techniques for NLP 282 18.2.9 Understanding Text Semantics With LSA 282 18.2.10 Analyzing Suicidal Tendencies With Semantic Analysis Using Social Media 283 18.2.11 Analyzing Public Opinion on BREXIT Using Sentiment Analysis 283 18.2.12 Prediction of Indian Elections Using NLP and Decision Tree 283 18.3 Methodology 283 18.4 Conclusion 286 References 287 19 Comparative Study of Various Data Mining Techniques Towards Analysis and Prediction of Global COVID-19 Dataset 289Sachin Kamley 19.1 Introduction 289 19.2 Literature Review 290 19.3 Materials and Methods 292 19.3.1 Dataset Collection 292 19.3.2 Support Vector Machine (SVM) 292 19.3.3 Decision Tree (DT) 294 19.3.4 K-Means Clustering 294 19.3.5 Back Propagation Neural Network (BPNN) 295 19.4 Experimental Results 296 19.5 Conclusion and Future Scopes 305 References 306 20 Automated Diagnosis of COVID-19 Using Reinforced Lung Segmentation and Classification Model 309J. Shiny Duela and T. Illakiya 20.1 Introduction 309 20.2 Diagnosis of COVID-19 310 20.2.1 Pre-Processing of Lung CT Image 310 20.2.2 Lung CT Image Segmentation 311 20.2.3 ROI Extraction 311 20.2.4 Feature Extraction 311 20.2.5 Classification 311 20.3 Genetic Algorithm (GA) 311 20.3.1 Operators of GA 312 20.3.2 Applications of GA 312 20.4 Related Works 313 20.5 Challenges in GA 314 20.6 Challenges in Lung CT Segmentation 314 20.7 Proposed Diagnosis Framework 314 20.7.1 Image Pre-Processing 315 20.7.2 Proposed Image Segmentation Technique 315 20.7.3 ROI Segmentation 318 20.7.4 Feature Extraction 318 20.7.5 Modified GA Classifier 318 20.7.5.1 Gaussian Type—II Fuzzy in Classification 318 20.7.5.2 Classifier Algorithm 319 20.8 Result Discussion 319 20.9 Conclusion 321 References 321 Part 5: Case Studies of Application Areas of Machine Learning in Healthcare System 323 21 Future of Telemedicine with ML: Building a Telemedicine Framework for Lung Sound Detection 325Sudhansu Shekhar Patra, Nitin S. Goje, Kamakhya Narain Singh, Kaish Q. Khan, Deepak Kumar, Madhavi and Kumar Ashutosh Sharma 21.1 Introduction 325 21.1.1 Monitoring the Remote Patient 326 21.1.2 Intelligent Assistance for Patient Diagnosis 326 21.1.3 Fasten Electronic Health Record Retrieval Process 326 21.1.4 Collaboration Increases Among Healthcare Practitioners 326 21.2 Related Work 327 21.3 Strategic Model for Telemedicine 328 21.4 Framework for Lung Sound Detection in Telemedicine 330 21.4.1 Data Collection 330 21.4.2 Pre-Processing of Data 331 21.4.3 Feature Extraction 331 21.4.3.1 MFCC 331 21.4.3.2 Lung Sounds Using Multi Resolution DWT 332 21.4.4 Classification 334 21.4.4.1 Correlation Coefficient for Feature Selection (CFS) 334 21.4.4.2 Symmetrical Uncertainty 334 21.4.4.3 Gain Ratio 335 21.4.4.4 Modified RF Classification Architecture 335 21.5 Experimental Analysis 335 21.6 Conclusion 340 References 340 22 A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images 343Rani Oomman Panicker, S.J. Pawan, Jeny Rajan and M.K. Sabu 22.1 Introduction 343 22.2 Literature Review 345 22.3 Proposed Work 346 22.4 Experimental Results and Discussion 349 22.5 Conclusion 350 References 350 23 Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer 353Vibhav Prakash Singh and Ashish Kumar Maurya 23.1 Introduction 353 23.2 Clinically Correlated Texture Features 358 23.2.1 Texture-Based LBP Descriptors 358 23.2.2 GLCM Features 358 23.2.3 Statistical Features 359 23.3 Machine Learning Techniques 359 23.3.1 Support Vector Machine (SVM) 359 23.3.2 k-NN (k-Nearest Neighbors) 360 23.3.3 Random Forest (RF) 361 23.3.4 Naïve Bayes 361 23.4 Result Analysis and Discussions 361 23.5 Conclusions 366 References 366 24 Analysis of Machine Learning Technologies for the Detection of Diabetic Retinopathy 369Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra 24.1 Introduction 369 24.2 Related Work 370 24.2.1 Pre-Processing of Image 371 24.2.2 Diabetic Retinopathy Detection 372 24.2.3 Grading of DR 374 24.3 Dataset Used 374 24.3.1 DIARETDB1 374 24.3.2 Diabetic-Retinopathy-Detection Dataset 376 24.4 Methodology Used 377 24.4.1 Pre-Processing 377 24.4.2 Segmentation 377 24.4.3 Feature Extraction 378 24.4.4 Classification 378 24.5 Analysis of Results and Discussion 379 24.6 Conclusion 380 References 381 Index 383

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Book SynopsisTable of ContentsPreface xv 1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge 1.1 Introduction 2 1.1.1 Scope and Motivation 3 1.2 Literature Review 4 1.2.1 Comparative Analysis 5 1.2.2 Survey Analysis 5 1.3 Tools and Techniques 10 1.3.1 Description of Dataset 11 1.3.2 Machine Learning Algorithm 12 1.3.3 Decision Tree 14 1.3.4 Random Forest 15 1.3.5 Naive Bayes Algorithm 16 1.3.6 K Means Algorithm 18 1.3.7 Ensemble Method 18 1.3.7.1 Bagging 19 1.3.7.2 Boosting 19 1.3.7.3 Stacking 19 1.3.7.4 Majority Vote 19 1.4 Proposed Method 20 1.4.1 Experiment and Analysis 20 1.4.2 Method 22 1.5 Conclusion 25 References 26 2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi 2.1 Introduction 30 2.1.1 Motivation to the Study 30 2.1.1.1 Problem Statements 31 2.1.1.2 Authors’ Contributions 31 2.1.1.3 Research Manuscript Organization 31 2.1.1.4 Definitions 32 2.1.2 Computer-Aided Diagnosis System (CADe or CADx) 32 2.1.3 Sensors for the Internet of Things 32 2.1.4 Wireless and Wearable Sensors for Health Informatics 33 2.1.5 Remote Human’s Health and Activity Monitoring 33 2.1.6 Decision-Making Systems for Sensor Data 33 2.1.7 Artificial Intelligence and Machine Learning for Health Informatics 34 2.1.8 Health Sensor Data Management 34 2.1.9 Multimodal Data Fusion for Healthcare 35 2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 35 2.2 Literature Review 35 2.3 Proposed Systems 37 2.3.1 Framework or Architecture of the Work 38 2.3.2 Model Steps and Parameters 38 2.3.3 Discussions 39 2.4 Experimental Results and Analysis 39 2.4.1 Tissue Characterization and Risk Stratification 39 2.4.2 Samples of Cancer Data and Analysis 40 2.5 Novelties 42 2.6 Future Scope, Limitations, and Possible Applications 42 2.7 Recommendations and Consideration 43 2.8 Conclusions 43 References 43 3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón 3.1 Introduction 48 3.2 Human Coronavirus Types 49 3.3 The SARS-CoV-2 Pandemic Impact 50 3.3.1 RNA Virus vs DNA Virus 51 3.3.2 The Coronaviridae Family 51 3.3.3 The SARS-CoV-2 Structural Proteins 52 3.3.4 Protein Representations 52 3.4 Computational Predictors 53 3.4.1 Supervised Algorithms 53 3.4.2 Non-Supervised Algorithms 54 3.5 Polarity Index Method® 54 3.5.1 The PIM® Profile 54 3.5.2 Advantages 55 3.5.3 Disadvantages 55 3.5.4 SARS-CoV-2 Recognition Using PIM® Profile 55 3.6 Future Implications 59 3.7 Acknowledgments 60 References 60 4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy 4.1 Introduction 63 4.2 Background 65 4.2.1 LSTM 65 4.2.1.1 Vanilla LSTM 65 4.2.1.2 Bidirectional LSTM 66 4.3 Related Works 67 4.4 Methods 68 4.4.1 Data Collection and Analysis 68 4.4.2 Results and Discussion 69 4.5 Conclusion and Future Work 71 4.6 Acknowledgments 71 References 71 5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai 5.1 Introduction 74 5.2 Types of Biological Networks 76 5.3 Methodologies in Network Embedding 76 5.4 Attributed and Non-Attributed Network Embedding 82 5.5 Applications of Network Embedding in Computational Biology 83 5.5.1 Understanding Genomic and Protein Interaction via Network Alignment 83 5.5.2 Pharmacogenomics 84 5.5.2.1 Drug-Target Interaction Prediction 84 5.5.2.2 Drug-Drug Interaction 84 5.5.2.3 Drug-Disease Interaction Prediction 85 5.5.2.4 Analysis of Adverse Drug Reaction 85 5.5.3 Function Prediction 86 5.5.4 Community Detection 86 5.5.5 Network Denoising 87 5.5.6 Analysis of Multi-Omics Data 87 5.6 Limitations of Network Embedding in Biology 87 5.7 Conclusion and Outlook 89 References 89 6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R. 6.1 Introduction 100 6.2 Related Study 101 6.3 Methodology 103 6.3.1 Pre-Processing 103 6.3.2 Region of Interest Extraction 104 6.3.3 Segmentation 105 6.3.4 Feature Extraction 106 6.3.5 Disease Classification 107 6.4 Implementation and Result Analysis 108 6.4.1 Dataset Description 108 6.4.2 Testbed 108 6.4.3 Discussion 108 6.4.3.1 K-Fold Cross-Validation 110 6.4.3.2 Confusion Matrix 110 6.5 Conclusion 115 References 115 7 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N. 7.1 Introduction 118 7.2 Deep Learning Techniques in Medical Informatics and Public Health 121 7.2.1 Autoencoders 122 7.2.2 Recurrent Neural Network 123 7.2.3 Convolutional Neural Network (CNN) 124 7.2.4 Deep Boltzmann Machine 126 7.2.5 Deep Belief Network 127 7.3 Applications of Deep Learning in Medical Informatics and Public Health 128 7.3.1 The Use of DL for Cancer Diagnosis 128 7.3.2 DL in Disease Prediction and Treatment 129 7.3.3 Future Applications 133 7.4 Open Issues Concerning DL in Medical Informatics and Public Health 135 7.5 Conclusion 139 References 140 8 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop 8.1 Foundations of Human Pose Estimation 147 8.2 Challenges to Human Pose Estimation 149 8.2.1 Motion Blur 150 8.2.2 Indistinct Background 151 8.2.3 Occlusion or Self-Occlusion 151 8.2.4 Lighting Conditions 151 8.3 Analyzing the Dimensions 152 8.3.1 2D Human Pose Estimation 152 8.3.1.1 Single-Person Pose Estimation 153 8.3.1.2 Multi-Person Pose Estimation 153 8.3.2 3D Human Pose Estimation 153 8.4 Standard Datasets for Human Pose Estimation 154 8.4.1 Pascal VOC (Visual Object Classes) Dataset 156 8.4.2 KTH Multi-View Football Dataset I 156 8.4.3 KTH Multi-View Football Dataset II 156 8.4.4 MPII Human Pose Dataset 157 8.4.5 BBC Pose 157 8.4.6 COCO Dataset 157 8.4.7 J-HMDB Dataset 158 8.4.8 Human3.6M Dataset 158 8.4.9 DensePose 158 8.4.10 AMASS Dataset 159 8.5 Deep Learning Revolutionizing Pose Estimation 159 8.5.1 Approaches in 2D Human Pose Estimation 159 8.5.2 Approaches in 3D Human Pose Estimation 163 8.6 Application of Human Pose Estimation in Medical Domains 165 8.7 Conclusion 166 References 167 9 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi 9.1 Introduction 172 9.1.1 Brain Tumor 172 9.1.2 Big Data Analytics in Health Informatics 172 9.1.3 Machine Learning in Healthcare 173 9.1.4 Sensors for Internet of Things 173 9.1.5 Challenges and Critical Issues of IoT in Healthcare 174 9.1.6 Machine Learning and Artificial Intelligence for Health Informatics 174 9.1.7 Health Sensor Data Management 175 9.1.8 Multimodal Data Fusion for Healthcare 175 9.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 176 9.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 176 9.2 Literature Survey 177 9.3 System Design and Methodology 179 9.3.1 System Design 179 9.3.2 CNN Architecture 180 9.3.3 Block Diagram 181 9.3.4 Algorithm(s) 181 9.3.5 Our Experimental Results, Interpretation, and Discussion 183 9.3.6 Implementation Details 183 9.3.7 Snapshots of Interfaces 184 9.3.8 Performance Evaluation 186 9.3.9 Comparison with Other Algorithms 186 9.4 Novelty in Our Work 186 9.5 Future Scope, Possible Applications, and Limitations 188 9.6 Recommendations and Consideration 188 9.7 Conclusions 188 References 189 10 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma 10.1 Introduction 192 10.1.1 Scenario of Pollution and Need to Connect with Indian Culture 192 10.1.2 Global Pollution Scenario 192 10.1.3 Indian Crisis on Pollution and Worrying Stats 193 10.1.4 Efforts Made to Curb Pollution World Wide 194 10.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 196 10.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 196 10.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 197 10.1.8 Effect of Different Woods and Cow Dung Used in Yajna 197 10.1.9 Use of Sensors and IoT to Record Experimental Data 198 10.1.10 Analysis and Pattern Recognition by ML and AI 199 10.2 Literature Survey 200 10.3 The Methodology and Protocols Followed 201 10.4 Experimental Setup of an Experiment 202 10.5 Results and Discussions 202 10.5.1 Mango 202 10.5.2 Bargad 203 10.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 207 10.7 Future Research Perspectives 207 10.8 Novelty of Our Research 208 10.9 Recommendations 208 10.10 Conclusions 209 References 209 11 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N. 11.1 Introduction 215 11.2 Literature Survey 218 11.3 Proposed Work 229 11.4 Analysis of the Work 230 11.5 Conclusion 231 References 231 12 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg 12.1 Introduction 236 12.1.1 Different Types of Diseases 236 12.1.1.1 Diabetes (Madhumeha) and Its Types 236 12.1.1.2 TTH and Stress 237 12.1.1.3 Anxiety 237 12.1.1.4 Hypertension 237 12.1.2 Machine Vision 237 12.1.2.1 Medical Images and Analysis 238 12.1.2.2 Machine Learning in Healthcare 238 12.1.2.3 Artificial Intelligence in Healthcare 239 12.1.3 Big Data and Internet of Things (IoT) 239 12.1.4 Machine Learning in Association with Data Science and Analytics 239 12.1.5 Yajna Science 240 12.1.6 Mantra Science 240 12.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 241 12.1.6.2 Significance of Mantra on Indian Culture and Mythology 241 12.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 241 12.1.8 Effects of Yajna and Mantra on Human Health 242 12.1.9 Impact of Yajna in Reducing the Atmospheric Solution 242 12.1.10 Scientific Study on Impact of Yajna on Air Purification 243 12.1.11 Scientific Meaning of Religious and Manglik Signs 244 12.2 Literature Survey 244 12.3 Methodology 246 12.4 Results and Discussion 249 12.5 Interpretations and Analysis 250 12.6 Novelty in Our Work 258 12.7 Recommendations 259 12.8 Future Scope and Possible Applications 260 12.9 Limitations 261 12.10 Conclusions 261 12.11 Acknowledgments 262 References 262 13 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J. 13.1 Introduction 269 13.2 Data Collection 271 13.2.1 Emerging Technologies in Healthcare and Its Applications 271 13.2.1.1 RFID 272 13.2.1.2 WSN 273 13.2.1.3 IoT 274 13.2.2 Issues and Challenges in Data Collection 277 13.2.2.1 Data Quality 277 13.2.2.2 Data Quantity 277 13.2.2.3 Data Access 278 13.2.2.4 Data Provenance 278 13.2.2.5 Security 278 13.2.2.6 Other Challenges 279 13.3 Data Analysis 280 13.3.1 Data Analysis Approaches 280 13.3.1.1 Machine Learning 280 13.3.1.2 Deep Learning 281 13.3.1.3 Natural Language Processing 281 13.3.1.4 High-Performance Computing 281 13.3.1.5 Edge-Fog Computing 282 13.3.1.6 Real-Time Analytics 282 13.3.1.7 End-User Driven Analytics 282 13.3.1.8 Knowledge-Based Analytics 283 13.3.2 Issues and Challenges in Data Analysis 283 13.3.2.1 Multi-Modal Data 283 13.3.2.2 Complex Domain Knowledge 283 13.3.2.3 Highly Competent End-Users 283 13.3.2.4 Supporting Complex Decisions 283 13.3.2.5 Privacy 284 13.3.2.6 Other Challenges 284 13.4 Research Trends 284 13.5 Conclusion 286 References 286 14 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra 14.1 Introduction 289 14.2 Sign Language Recognition 290 14.2.1 Fundamentals of Sign Language Recognition 290 14.2.2 Requirements for the Sign Language Recognition 292 14.3 Dataset Creation 293 14.3.1 American Sign Language 293 14.3.2 German Sign Language 296 14.3.3 Arabic Sign Language 297 14.3.4 Indian Sign Language 298 14.4 Hardware Employed for Sign Language Recognition 299 14.4.1 Glove/Sensor-Based Systems 299 14.4.2 Microsoft Kinect–Based Systems 300 14.5 Computer Vision–Based Sign Language Recognition and Translation Systems 302 14.5.1 Image Processing Techniques for Sign Language Recognition 302 14.5.2 Deep Learning Methods for Sign Language Recognition 304 14.5.3 Pose Estimation Application to Sign Language Recognition 305 14.5.4 Temporal Information in Sign Language Recognition and Translation 306 14.6 Sign Language Translation System—A Brief Overview 307 14.7 Conclusion 309 References 310 Index 315

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Book SynopsisArtificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original researTable of ContentsList of Contributors xv Preface xix Profile of Editors xxvii Acknowledgments xxx 1 Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 4.0 1Subhash Mondal, Swapnoj Banerjee, Soumodipto Halder, and Diganta Sengupta 1.1 Introduction 1 1.2 Literature Review 2 1.3 Proposed Framework 5 1.3.1 Enrolment Phase 5 1.3.2 Authentication Phase 7 1.3.2.1 Pre-processing 7 1.3.2.2 Minutiae Extraction and False Minutiae Removal 12 1.3.2.3 Key Generation from extracted Minutiae points 13 1.3.2.4 Encrypting the Biometric Fingerprint Image Using AES 14 1.4 Comparative Analysis 18 1.5 Conclusion 19 References 19 2 Decision Support Methodology for Scheduling Orders in Additive Manufacturing 25Juan Jesús Tello Rodríguez and Lopez-I Fernando 2.1 Introduction 25 2.2 The Additive Manufacturing Process 26 2.3 Some Background 28 2.4 Proposed Approach 30 2.4.1 A Mathematical Model for the Initial Printing Scheduling 32 2.4.1.1 Considerations 32 2.4.1.2 Sets 32 2.4.2 Parameters 33 2.4.2.1 Orders 33 2.4.2.2 Parts 33 2.4.2.3 Printing Machines 33 2.4.2.4 Process 33 2.4.3 Decision Variables 33 2.4.4 Optimization Criteria 33 2.4.5 Constrains 34 2.5 Results 35 2.5.1 Orders 35 2.6 Conclusions 39 References 39 3 Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 43Y. Bevish Jinila, Cinthia Joy, J. Joshua Thomas, and S. Prayla Shyry 3.1 Introduction 43 3.2 Background and RelatedWork 47 3.3 Challenges in Smart Cities 49 3.3.1 Data Acquisition 49 3.3.2 Data Analysis 50 3.3.3 Data Security and Privacy 50 3.3.4 Data Dissemination 50 3.4 Need for AI and Data Analytics 50 3.5 Applications of AI in Smart Cities 51 3.5.1 Road Condition Monitoring 51 3.5.2 Driver Behavior Monitoring 52 3.5.3 AI-Enabled Automatic Parking 53 3.5.4 Waste Management 53 3.5.5 Smart Governance 53 3.5.6 Smart Healthcare 54 3.5.7 Smart Grid 54 3.5.8 Smart Agriculture 55 3.6 AI-based Modeling for Smart Cities 55 3.6.1 Smart Cities Deployment Model 55 3.6.2 AI-Based Predictive Analytics 57 3.6.3 Pre-processing 58 3.6.4 Feature Selection 58 3.6.5 Artificial Intelligence Model 58 3.7 Conclusion 60 References 60 4 Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 63Vladimir A. Kozub, Alexander B. Murynin, Igor S. Litvinchev, Ivan A. Matveev, and Pandian Vasant 4.1 Introduction 63 4.2 Methodology for Constructing a Digital Terrain Model 64 4.3 Image Segmentation Problem 65 4.4 Segmentation Quality Assessment 67 4.5 Existing Segmentation Methods and Algorithms 68 4.6 Classical Methods 69 4.7 Neural Network Methods 72 4.7.1 Semantic Segmentation of Objects in Satellite Images 74 4.8 Segmentation with Neural Networks 76 4.9 Convolutional Neural Networks 79 4.10 Batch Normalization 83 4.11 Residual Blocks 84 4.12 Training of Neural Networks 85 4.13 Loss Functions 85 4.14 Optimization 86 4.15 Numerical Experiments 88 4.16 Description of the Training Set 88 4.17 Class Analysis 90 4.18 Augmentation 90 4.19 NN Architecture 92 4.20 Training and Results 93 4.21 Conclusion 97 Acknowledgments 97 References 97 5 The Impact of Data Security on the Internet of Things 101Joshua E. Chukwuere and Boitumelo Molefe 5.1 Introduction 101 5.2 Background of the Study 102 5.3 Problem Statement 103 5.4 Research Questions 103 5.5 Literature Review 103 5.5.1 The Data Security on IoT 103 5.5.2 The Security Threats and Awareness of Data Security on IoT 105 5.5.3 The DifferentWays to Assist with Keeping Your IoT Device Safer from Security Threats 105 5.6 Research Methodology 106 5.6.1 Population and Sampling 106 5.6.2 Data Collection 107 5.6.3 Reliability and Validity 108 5.7 Chapter Results and Discussions 108 5.7.1 The Demographic Information 109 5.7.1.1 Age, Ethnic Group, and Ownership of a Smart Device 109 5.7.2 Awareness of Users About Data Security of the Internet of Things 109 5.7.3 The Security Threats that are Affecting the Internet of Things Devices 111 5.7.3.1 The Architecture of IoT Devices 112 5.7.3.2 The botnets Attack 112 5.7.4 The Effects of Security Threats on IoT Devices that are Affecting Users 112 5.7.4.1 The Slowness or Malfunctioning of the IoT Device 112 5.7.4.2 The Trust of Users on IoT 113 5.7.4.3 The Safety of Users 113 5.7.4.4 The Guaranteed Duration of IoT Devices 114 5.7.5 DifferentWays to Assist with Keeping IoT Smart Devices Safer from Security Threats 114 5.7.5.1 The Change Default Passwords 114 5.7.5.2 The Easy or Common Passwords 114 5.7.5.3 On the Importance of Reading Privacy Policies 114 5.7.5.4 The Bluetooth and Wi-Fi of IoT Devices 115 5.7.5.5 The VPN on IoT 115 5.7.5.6 The Physical Restriction 115 5.7.5.7 Two-Factor Authentication 116 5.7.5.8 The Biometric Authentication 116 5.8 Answers to the Chapter Questions 116 5.8.1 Objective 1: Awareness on Users About Data Security of Internet of Things (IoT) 116 5.8.2 Objective 2: Determine the Security Threats that are Involved in the Internet of Things (IoT) 117 5.8.3 Objective 3: The Effects of Security Threats on IoT Devices that are Affecting Users 117 5.8.4 Objective 4: DifferentWays to Assist with Keeping IoT Devices Safer from Security Threats 117 5.8.5 Other Descriptive Analysis (Mean) 118 5.8.5.1 Mean 1 – Awareness on Users About Data Security on IoT 118 5.8.5.2 The Effects of Security Threats on IoT Devices that are Affecting Users 118 5.8.5.3 DifferentWays to Assist with Keeping an IoT Device Safer 122 5.9 Chapter Recommendations 122 5.10 Conclusion 122 References 124 6 Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 129Choo K. Chin and Deng H. Xiang 6.1 Introduction 129 6.2 Literature Review 131 6.2.1 Energy Efficiency 135 6.2.2 Waste Minimization 136 6.2.3 Water Consumption 137 6.2.4 Eco-Procurement 137 6.2.5 Communication 138 6.2.6 Awareness 138 6.2.7 Sustainable and Renewable Energy Development 138 6.3 Conceptual Framework 139 6.4 Conclusion 139 6.4.1 Energy Efficiency 140 6.4.2 Waste Minimization 140 6.4.3 Water Consumption 140 6.4.4 Eco-Procurement 141 6.4.5 Communication 141 6.4.6 Sustainable and Renewable Energy Development 141 Acknowledgment 142 References 142 7 Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 147Denis Y. Nartsev, Alexander N. Gneushev, and Ivan A. Matveev 7.1 Introduction 147 7.2 Problem Statement 149 7.3 Modifications of the Adam Optimization Method for Training a Regression Model 151 7.4 Computational Experiments 155 7.4.1 Model for Evaluating the Eye Image Blurring Degree 155 7.4.2 Facial Rotation Angle Estimation Model 158 7.5 Conclusion 160 Acknowledgments 161 References 161 8 Application of Integer Programming in Allocating Energy Resources in Rural Africa 165Elias Munapo 8.1 Introduction 165 8.1.1 Applications of the QAP 165 8.2 Quadratic Assignment Problem Formulation 166 8.2.1 Koopmans–Beckmann Formulation 166 8.3 Current Linearization Technique 167 8.3.1 The General Quadratic Binary Problem 167 8.3.2 Linearizing the Quadratic Binary Problem 169 8.3.2.1 Variable Substitution 169 8.3.2.2 Justification 169 8.3.3 Number of Variables and Constraints in the Linearized Model 170 8.3.4 Linearized Quadratic Binary Problem 171 8.3.5 Reducing the Number of Extra Constraints in the Linear Model 171 8.3.6 The General Binary Linear (BLP) Model 171 8.3.6.1 Convex Quadratic Programming Model 172 8.3.6.2 Transforming Binary Linear Programming (BLP) Into a Convex/Concave Quadratic Programming Problem 172 8.3.6.3 Equivalence 173 8.4 Algorithm 174 8.4.1 Making the Model Linear 175 8.5 Conclusions 176 References 176 9 Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 179Sadia S. Ali, Rajbir Kaur, and Haidar Abbas 9.1 Introduction 179 9.1.1 Technology and Business 181 9.1.2 Technological Revolution of the Twenty-first Century 181 9.2 An Overview of Drone Technology and Its Future Prospects in Indian Market 182 9.2.1 Utilities 183 9.2.1.1 Delivery 183 9.2.1.2 Media/Photography 183 9.2.1.3 Agriculture 184 9.2.1.4 Contingency and Disaster Management Scenarios 184 9.2.1.5 Civil and Military Services: Search and Rescue, Surveillance,Weather, and Traffic Monitoring, Firefighting 185 9.2.2 Complexities Involved 185 9.2.3 Drones in Indian Business Scenario 186 9.3 Literature Review 187 9.3.1 Absorption and Diffusion of New Technology 188 9.3.2 Leadership for Innovation 188 9.3.3 Social and Economic Environment 189 9.3.4 Customer Perceptions 190 9.3.5 Alliances with Other National and International Organizations 190 9.3.6 Other Influencers 191 9.4 Methodology 191 9.5 Discussion 193 9.5.1 Market Module 195 9.5.2 Technology Module 196 9.5.3 Commercial Module 198 9.6 Conclusions 199 References 200 10 Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 209Isidro Soria-Arguello, Rafael Torres-Esobar, and Pandian Vasant 10.1 Introduction 209 10.2 New Distribution System 211 10.3 The Mathematical Model to Design the Distribution Network 214 10.4 Solution Technique 216 10.4.1 Lagrangian Relaxation 216 10.4.2 Methods for Finding the Value of Lagrange Multipliers 216 10.4.3 Selecting the Solution Method 216 10.4.4 Used Notation 217 10.4.5 Proposed Relaxations of the Distribution Model 218 10.4.5.1 Relaxation 1 218 10.4.5.2 Relaxation 2 219 10.4.6 Selection of the Best Lagrangian Relaxation 219 10.5 Heuristic Algorithm to Restore Feasibility 220 10.6 Numerical Analysis 222 10.6.1 Scenario 2020 223 10.6.2 Scenario 2021 224 10.6.3 Scenario 2022 225 10.6.4 Scenario 2023 226 10.7 Conclusions 228 References 228 11 Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 231Arrieta-M Luisa F and Lopez-I Fernando 11.1 Introduction 231 11.1.1 Background 232 11.2 Proposed Approach 233 11.3 Case Study 234 11.3.1 Intelligent Phase 234 11.3.2 Design Phase 235 11.3.3 Choice Phase 236 11.4 Results 238 11.5 Conclusion 240 References 240 12 A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 243Timothy Ganesan, Irraivan Elamvazuthi, Pandian Vasant, and J. Joshua Thomas 12.1 Background 243 12.2 Computational Optimization Frameworks 246 12.3 Internet of Things (IoT) Systems 248 12.4 Smart Grids Data Systems 250 12.5 Supply Chain Management 252 12.6 Healthcare Data Management Systems 255 12.7 Outlook 257 References 258 13 Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 267Paulina A. Avila-Torres and Nancy M. Arratia-Martinez 13.1 Introduction 267 13.2 Problem Description 270 13.3 Mathematical Formulation 273 13.4 Computational Experiments 275 13.4.1 Numerical Example 276 13.4.1.1 The Inventory Routing Problem Under Certainty 279 13.4.1.2 The Inventory Routing Problem Under Uncertainty in the Consumption Rate of Product 279 13.5 Conclusions and FutureWork 280 References 281 14 Comparison of Defuzzification Methods for Project Selection 283Nancy M. Arratia-Martinez, Paulina A. Avila-Torres, and Lopez-I Fernando 14.1 Introduction 283 14.2 Problem Description 286 14.3 Mathematical Model 286 14.3.1 Sets and Parameters 287 14.3.2 Decision Variables 287 14.3.3 Objective Functions 287 14.4 Constraints 288 14.5 Methods of Defuzzification and Solution Algorithm 289 14.5.1 k-Preference Method 289 14.5.2 Integral Value 291 14.5.3 SAUGMECON Algorithm 291 14.6 Results 292 14.6.1 Results of k-Preference Method 292 14.6.2 Results of Integral Value Method 295 14.7 Conclusions 299 References 300 15 Re-Identification-Based Models for Multiple Object Tracking 303Alexey D. Grigorev, Alexander N. Gneushev, and Igor S. Litvinchev 15.1 Introduction 303 15.2 Multiple Object Tracking Problem 305 15.3 Decomposition of Tracking into Filtering and Assignment Tasks 306 15.4 Cost Matrix Adjustment in Assignment Problem Based on Re-Identification with Pre-Filtering of Descriptors by Quality 310 15.5 Computational Experiments 313 15.6 Conclusion 315 Acknowledgments 315 References 316 Index 319

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Book SynopsisTable of ContentsNotes on Contributors Preface About the Editors Chapter 1 Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks Adnen El Amraoui Associate Professor, Univ. Artois, U.R. 3926 Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Béthune, France Chapter 2 A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks Elsaid M. Abdelrahim Department of Mathematics Computer Science Division, Faculty of Science, Tanta University, Tanta, Egypt. Chapter 3 Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks 1,2José Escorcia-Gutierrez, 3Melitsa Torres-Torres, 4Kelvin Beleño, 5Carlos Soto 1Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia 2Research Center - CIENS, Escuela Naval de Suboficiales A.R.C. "Barranquilla", Barranquilla, Colombia 3Research group IET-UAC,Universidad Autónoma del Caribe, 08001 Barranquilla, Colombia 4Mechatronics Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia 5Mechanical Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia Chapter 4 Crow Search Algorithm based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment Maha M. Althobaiti ‏Department of Computer Science, College of Computing and Information technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia. Chapter 5 Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks 1,2Pooja Singh, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque 1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil. 2Associate Professor, Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Knowledge Park-3, Greater Noida (U.P.) India- 201306 3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. 4Meteora, Fortaleza/CE, Brazil 5Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza/CE, Brazil Chapter 6 Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks 1,2Vinita Malik, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque 1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil. 2Information Scientist, Central University of Haryana, Haryana, India- 201306 3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. 4Meteora, Fortaleza/CE, Brazil 5Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza/CE, Brazil Chapter 7 Efficient Deer Hunting Optimization Algorithm based Spectrum Sensing Approach for 6G Communication Networks R. Pandi Selvam, Kanagaraj Narayanasamy, M. Ilayaraja PG Department of Computer Science, Vidhyaa Giri College of Arts & Science, Karaikudi- 630 108, India Department of Computer Applications, J.J. College of Arts and Science (Autonomous), Pudukkottai- 622 422, India School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India Chapter 8 Elite Oppositional Hunger Games Search Optimization based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks Emad A-B Abdel-Salam, Ayman M. Mahmoud, Romany F. Mansour Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt

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Book SynopsisTable of ContentsPreface xv Part I: Introduction 1 1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3Nahid Sami and Asfia Aziz 1.1 Introduction 3 1.2 Machine Learning in Healthcare 4 1.3 Machine Learning Algorithms 6 1.3.1 Supervised Learning 6 1.3.2 Unsupervised Learning 7 1.3.3 Semi-Supervised Learning 7 1.3.4 Reinforcement Learning 8 1.3.5 Deep Learning 8 1.4 Big Data in Healthcare 8 1.5 Application of Big Data in Healthcare 9 1.5.1 Electronic Health Records 9 1.5.2 Helping in Diagnostics 9 1.5.3 Preventive Medicine 10 1.5.4 Precision Medicine 10 1.5.5 Medical Research 10 1.5.6 Cost Reduction 10 1.5.7 Population Health 10 1.5.8 Telemedicine 10 1.5.9 Equipment Maintenance 11 1.5.10 Improved Operational Efficiency 11 1.5.11 Outbreak Prediction 11 1.6 Challenges for Big Data 11 1.7 Conclusion 11 References 12 Part II: Medical Data Processing and Analysis 15 2 Thoracic Image Analysis Using Deep Learning 17Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi 2.1 Introduction 18 2.2 Broad Overview of Research 19 2.2.1 Challenges 19 2.2.2 Performance Measuring Parameters 21 2.2.3 Availability of Datasets 21 2.3 Existing Models 23 2.4 Comparison of Existing Models 30 2.5 Summary 38 2.6 Conclusion and Future Scope 38 References 39 3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43G. Manikandan and S. Abirami 3.1 Introduction 43 3.1.1 Motivation of the Dimensionality Reduction 45 3.1.2 Feature Selection and Feature Extraction 46 3.1.3 Objectives of the Feature Selection 47 3.1.4 Feature Selection Process 47 3.2 Types of Feature Selection 48 3.2.1 Filter Methods 49 3.2.1.1 Correlation-Based Feature Selection 49 3.2.1.2 The Fast Correlation-Based Filter 50 3.2.1.3 The INTERACT Algorithm 51 3.2.1.4 ReliefF 51 3.2.1.5 Minimum Redundancy Maximum Relevance 52 3.2.2 Wrapper Methods 52 3.2.3 Embedded Methods 53 3.2.4 Hybrid Methods 54 3.3 Machine Learning and Deep Learning Models 55 3.3.1 Restricted Boltzmann Machine 55 3.3.2 Autoencoder 56 3.3.3 Convolutional Neural Networks 57 3.3.4 Recurrent Neural Network 58 3.4 Real-World Applications and Scenario of Feature Selection 58 3.4.1 Microarray 58 3.4.2 Intrusion Detection 59 3.4.3 Text Categorization 59 3.5 Conclusion 59 References 60 4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65Parvej Reja Saleh and Eeshankur Saikia 4.1 Introduction 65 4.2 Literature Review 68 4.3 Dataset, EDA, and Data Processing 69 4.4 Machine Learning Algorithms 72 4.4.1 Multinomial Naïve Bayes Classifier 72 4.4.2 Support Vector Machine Classifier 72 4.4.3 Random Forest Classifier 73 4.4.4 K-Nearest Neighbor Classifier 74 4.4.5 Decision Tree Classifier 74 4.4.6 Logistic Regression Classifier 75 4.4.7 Multilayer Perceptron Classifier 76 4.5 Work Architecture 77 4.6 Conclusion 78 References 79 5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features 81Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar 5.1 Introduction 81 5.1.1 Motivation 82 5.2 Related Work 83 5.3 Theoretical Background 84 5.3.1 Pre-Processing Techniques 84 5.3.2 Spectrogram Generation 85 5.3.2 Feature Extraction 88 5.3.4 Feature Selection 90 5.3.5 Support Vector Machine 91 5.4 Proposed Algorithm 91 5.5 Experimental Results 92 5.5.1 Database 92 5.5.2 Evaluation Metrics 94 5.5.3 Confusion Matrix 94 5.5.4 Results and Discussions 94 5.6 Conclusion 96 References 99 6 Improving Multi-Label Classification in Prototype Selection Scenario 103Himanshu Suyal and Avtar Singh 6.1 Introduction 103 6.2 Related Work 105 6.3 Methodology 106 6.3.1 Experiments and Evaluation 108 6.4 Performance Evaluation 108 6.5 Experiment Data Set 109 6.6 Experiment Results 110 6.7 Conclusion 117 References 117 7 A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121Maqsood Hayat, Yar Muhammad and Muhammad Tahir 7.1 Introduction 121 7.2 Materials and Methods 123 7.2.1 Dataset 123 7.2.2 Proposed Framework for Diabetes System 124 7.2.3 Pre-Processing of Data 124 7.3 Machine Learning Classification Hypotheses 124 7.3.1 K-Nearest Neighbor 124 7.3.2 Decision Tree 125 7.3.3 Random Forest 126 7.3.4 Logistic Regression 126 7.3.5 Naïve Bayes 126 7.3.6 Support Vector Machine 126 7.3.7 Adaptive Boosting 126 7.3.8 Extra-Tree Classifier 127 7.4 Classifier Validation Method 127 7.4.1 K-Fold Cross-Validation Technique 127 7.5 Performance Evaluation Metrics 127 7.6 Results and Discussion 129 7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129 7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131 7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133 7.7 Conclusion 137 References 137 8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139Dhilsath Fathima M. and S. Justin Samuel 8.1 Introduction 140 8.2 Related Work 140 8.3 Proposed Method 142 8.3.1 Dataset Description 143 8.3.2 Ensemble Learners for Classification Modeling 144 8.3.2.1 Bagging Ensemble Learners 145 8.3.2.2 Boosting Ensemble Learner 147 8.3.3 Hyperparameter Tuning of Ensemble Learners 151 8.3.3.1 Grid Search Algorithm 151 8.3.3.2 Random Search Algorithm 152 8.4 Experimental Outcomes and Analyses 153 8.4.1 Characteristics of UCI Heart Disease Dataset 153 8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154 8.4.3 Analysis of Experimental Result 154 8.5 Conclusion 157 References 157 9 Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies 159Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj 9.1 Introduction: Simulation in Healthcare 160 9.2 Need for a Healthcare Simulation Process 160 9.3 Types of Healthcare Simulations 161 9.4 AI in Healthcare Simulation 163 9.4.1 Machine Learning Models in Healthcare Simulation 163 9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163 9.4.2 Deep Learning Models in Healthcare Simulation 169 9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 170 9.5 Conclusion 174 References 174 10 Wolfram’s Cellular Automata Model in Health Informatics 179Sutapa Sarkar and Mousumi Saha 10.1 Introduction 179 10.2 Cellular Automata 181 10.3 Application of Cellular Automata in Health Science 183 10.4 Cellular Automata in Health Informatics 184 10.5 Health Informatics–Deep Learning–Cellular Automata 190 10.6 Conclusion 191 References 191 Part III: Machine Learning and COVID Prospective 193 11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques 195Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena 11.1 Introduction 195 11.2 Literature Review 196 11.3 Data Pre-Processing 197 11.4 Proposed Methodologies 198 11.4.1 Simple Linear Regression 198 11.4.2 Association Rule Mining 202 11.4.3 Back Propagation Neural Network 203 11.5 Experimental Results 204 11.6 Conclusion and Future Scopes 211 References 212 12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach 215Sivanantham Kalimuthu 12.1 Introduction 215 12.2 Literature Review 218 12.3 System Design 222 12.3.1 Extracting Feature With WMAR 224 12.4 Result and Discussion 229 12.5 Conclusion 232 References 232 13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network 235Sayan Das and Jaya Sil 13.1 Introduction 236 13.2 Background Details and Literature Review 239 13.2.1 Fuzzy Set 239 13.2.2 Self-Organizing Mapping 239 13.3 Methodology 240 13.3.1 Severity_Factor of Patient 244 13.3.2 Clustering by Self-Organizing Mapping 249 13.4 Results and Discussion 250 13.5 Conclusion 252 References 252 14 Face Mask Detection in Real-Time Video Stream Using Deep Learning 255Alok Negi and Krishan Kumar 14.1 Introduction 256 14.2 Related Work 257 14.3 Proposed Work 258 14.3.1 Dataset Description 258 14.3.2 Data Pre-Processing and Augmentation 258 14.3.3 VGG19 Architecture and Implementation 259 14.3.4 Face Mask Detection From Real-Time Video Stream 261 14.4 Results and Evaluation 262 14.5 Conclusion 267 References 267 15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms 269Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar 15.1 Introduction 270 15.2 Research Problem Statements 274 15.3 Dataset Description 274 15.4 Machine Learning Technique Used for Skin Disease Identification 276 15.4.1 Logistic Regression 277 15.4.1.1 Logistic Regression Assumption 277 15.4.1.2 Logistic Sigmoid Function 277 15.4.1.3 Cost Function and Gradient Descent 278 15.4.2 SVM 279 15.4.3 Recurrent Neural Networks 281 15.4.4 Decision Tree Classification Algorithm 283 15.4.5 CNN 286 15.4.6 Random Forest 288 15.5 Result and Analysis 290 15.6 Conclusion 291 References 291 16 Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario 297Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari 16.1 Introduction 298 16.1.1 Motivation 298 16.1.2 Contributions 299 16.1.3 Paper Organization 299 16.1.4 System Model Problem Formulation 299 16.1.5 Proposed Methodology 300 16.2 Material Properties and Design Specifications 301 16.2.1 Hardware Components 301 16.2.1.1 Microcontroller 301 16.2.1.2 ESP8266 Wi-Fi Shield 301 16.2.2 Sensors 301 16.2.2.1 Temperature Sensor (LM 35) 301 16.2.2.2 ECG Sensor (AD8232) 301 16.2.2.3 Pulse Sensor 301 16.2.2.4 GPS Module (NEO 6M V2) 302 16.2.2.5 Gyroscope (GY-521) 302 16.2.3 Software Components 302 16.2.3.1 Arduino Software 302 16.2.3.2 MySQL Database 302 16.2.3.3 Wireless Communication 302 16.3 Experimental Methods and Materials 303 16.3.1 Simulation Environment 303 16.3.1.1 System Hardware 303 16.3.1.2 Connection and Circuitry 304 16.3.1.3 Protocols Used 306 16.3.1.4 Libraries Used 307 16.4 Simulation Results 307 16.5 Conclusion 310 16.6 Abbreviations and Acronyms 310 References 311 17 COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques 313Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik 17.1 Introduction 313 17.2 Literature Survey 314 17.2.1 Cellular Automata 315 17.2.2 Image Segmentation 316 17.2.3 Deep Learning Techniques 316 17.3 Proposed Methodology 317 17.4 Results and Discussion 320 17.5 Conclusion 322 References 322 18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures 325Abhilash C. B. and Kavi Mahesh 18.1 Introduction 326 18.2 Methods 326 18.2.1 Data 326 18.3 GSA Model: Graph-Based Statistical Analysis 327 18.4 Graph-Based Analysis 329 18.4.1 Modeling Your Data as a Graph 329 18.4.2 RDF for Knowledge Graph 331 18.4.3 Knowledge Graph Representation 331 18.4.4 RDF Triple for KaTrace 333 18.4.5 Cipher Query Operation on Knowledge Graph 335 18.4.5.1 Inter-District Travel 335 18.4.5.2 Patient 653 Spread Analysis 336 18.4.5.3 Spread Analysis Using Parent-Child Relationships 337 18.4.5.4 Delhi Congregation Attended the Patient’s Analysis 339 18.5 Machine Learning Techniques 339 18.5.1 Apriori Algorithm 339 18.5.2 Decision Tree Classifier 341 18.5.3 System Generated Facts on Pandas 343 18.5.4 Time Series Model 345 18.6 Exploratory Data Analysis 346 18.6.1 Statistical Inference 347 18.7 Conclusion 356 18.8 Limitations 356 Acknowledgments 356 Abbreviations 357 References 357 Part IV: Prospective of Computational Intelligence in Healthcare 359 19 Conceptualizing Tomorrow’s Healthcare Through Digitization 361Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena 19.1 Introduction 361 19.2 Importance of IoMT in Healthcare 362 19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 363 19.3.1 Introduction to the Case Study 363 19.3.2 Merits 363 19.3.3 Proposed Design 363 19.3.3.1 Homecare 363 19.3.3.2 Healthcare Provider 365 19.3.3.3 Community 367 19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 371 19.4.1 Introduction to the Case Study 371 19.4.2 Proposed Design 373 19.5 Future of Smart Healthcare 375 19.6 Conclusion 375 References 375 20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 377Pitambar Behera and Om Prakash Jena 20.1 Introduction 377 20.1.1 COVID-19 Pandemic Situation 378 20.1.2 Salient Characteristics of Biomedical Corpus 378 20.2 Review of Related Literature 379 20.2.1 Biomedical NLP Research 379 20.2.2 Domain Adaptation 379 20.2.3 POS Tagging in Hindi 380 20.3 Scope and Objectives 380 20.3.1 Research Questions 380 20.3.2 Research Problem 380 20.3.3 Objectives 381 20.4 Methodological Design 381 20.4.1 Method of Data Collection 381 20.4.2 Method of Data Annotation 381 20.4.2.1 The BIS Tagset 381 20.4.2.2 ILCI Semi-Automated Annotation Tool 382 20.4.2.3 IA Agreement 383 20.4.3 Method of Data Analysis 383 20.4.3.1 The Theory of Support Vector Machines 384 20.4.3.2 Experimental Setup 384 20.5 Evaluation 385 20.5.1 Error Analysis 386 20.5.2 Fleiss’ Kappa 388 20.6 Issues 388 20.7 Conclusion and Future Work 388 Acknowledgements 389 References 389 21 Application of Natural Language Processing in Healthcare 393Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas 21.1 Introduction 393 21.2 Evolution of Natural Language Processing 395 21.3 Outline of NLP in Medical Management 396 21.4 Levels of Natural Language Processing in Healthcare 397 21.5 Opportunities and Challenges From a Clinical Perspective 399 21.5.1 Application of Natural Language Processing in the Field of Medical Health Records 399 21.5.2 Using Natural Language Processing for Large-Sample Clinical Research 400 21.6 Openings and Difficulties From a Natural Language Processing Point of View 401 21.6.1 Methods for Developing Shareable Data 401 21.6.2 Intrinsic Evaluation and Representation Levels 402 21.6.3 Beyond Electronic Health Record Data 403 21.7 Actionable Guidance and Directions for the Future 403 21.8 Conclusion 406 References 406 Index 409

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Book SynopsisAI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials A cohesive and insightful compilation of resources explaining the latest discoveries and methods in the field of nanoporous materials In Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction a team of distinguished researchers delivers a robust compilation of the latest knowledge and most recent developments in computational chemistry, synthetic chemistry, and artificial intelligence as it applies to zeolites, porous molecular materials, covalent organic frameworks and metal-organic frameworks. The book presents a common language that unifies these fields of research and advances the discovery of new nanoporous materials. The editors have included resources that describe strategies to synthesize new nanoporous materials, construct databases of materials, structure directing agents, and synthesis conditions, and Table of ContentsList of Contributors xiii Preface xvii About the Cover xxiii Acknowledgments xxv 1 The Confluence of Organo-Cations, Inorganic Species, and Molecular Modeling on the Discovery of New Zeolite Structures and Compositions 1 Christopher M. Lew, Dan Xie, Joel E. Schmidt, Saleh Elomari, Tracy M. Davis, and Stacey I. Zones 1.1 Introduction 1 1.2 Inorganic Studies 3 1.3 Organic Structure-Directing Agents (OSDAs) 9 1.3.1 Purpose and Important Properties 9 1.3.2 Classes of Ammonium-based OSDAs 10 1.3.3 Methods of Making 12 1.4 OSDA–Zeolite Energetics and Rational Synthesis 15 1.5 Role of High Throughput and Automation 22 1.6 Cataloguing, Archiving, Harvesting, and Mining Years of Historical Data 24 1.7 Concluding Remarks 25 References 25 2 De Novo Design of Organic Structure Directing Agents for the Synthesis of Zeolites 33 Frits Daeyaert and Michael Deem 2.1 Introduction 33 2.2 De Novo Design 34 2.2.1 Molecular Structure Generator 35 2.2.2 Scoring Function 36 2.2.3 Optimization Algorithm 37 2.2.4 Practical Implementation 42 2.3 Scoring Functions for OSDAs 43 2.3.1 Stabilization Energy 43 2.3.2 Other Constraints 44 2.3.3 Multiple Objectives 45 2.4 Applications 48 2.4.1 From Drug Design to the Design of OSDAs for Zeolites 48 2.4.2 Experimental Confirmation: Pure Silica STW 49 2.4.3 Experimental Confirmation: Zeolite AEI 49 2.4.4 Practical Application: SSZ-52 (SFW) 49 2.4.5 Design of Chiral OSDAs to Direct the Synthesis of Chiral STW 49 2.4.6 Design of Selective OSDAs Directed Toward BEA vs. BEB 51 2.4.7 Design of OSDAs for Chiral Zeolite BEA 52 2.4.8 Application of a Machine-Learning Scoring Function in the De Novo Design of OSDAs for Zeolite Beta 52 2.4.9 Design of OSDAs for Zeolites for Gas Adsorption and Separation 52 2.4.9.1 Carbon Capture and Storage: WEI, JBW, GIS, SIV, DAC, 8124767, 8277563 52 2.4.9.2 Carbon Dioxide/Methane Separation: GIS, ABW, 8186909, 8198030 53 2.4.9.3 Separation of Ethylene-Ethane: DFT, ACO, NAT, JRY 53 2.4.10 Design of MOFs for Methane Storage and Delivery 54 2.4.11 Multi-Objective De Novo Design of OSDAs for Zeolites Using an Ant Colony Optimization Algorithm 55 2.5 Conclusions and Outlook 55 References 56 3 Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta (BEA) Zeolite Using Molecular Topology and Monte Carlo Techniques 61 María Gálvez-Llompart and German Sastre 3.1 Introduction 61 3.2 Artificial Neural Networks for Modeling Zeolite-SDA van der Waals Energy Applied to BEA Zeolite 64 3.3 Virtual Screening: Identifying Novel SDA with Favorable E ZEO-SDA for the Synthesis of BEA Zeolite 69 3.4 Zeo-SDA Energy Calculation Using Atomic Models 71 3.5 Comparing Zeo-SDA Energy Calculation Using MLR, ANN, and Atomic Models 73 3.6 Conclusions 74 Acknowledgments 77 References 77 4 Generating, Managing, and Mining Big Data in Zeolite Simulations 81 Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli 4.1 Introduction 81 4.1.1 Computational Materials Databases 82 4.1.2 Zeolite Databases 83 4.2 Database of OSDAs for Zeolites 85 4.2.1 Developing a Docking Algorithm 86 4.2.2 Calibrating Binding Energy Predictions 88 4.2.3 Performing and Analyzing High-Throughput Screening Calculations 91 4.2.4 Recalling Synthesis Outcomes from the Literature 94 4.2.5 Proposing OSDA Descriptors 96 4.2.6 Designing with Interactivity 99 4.3 Outlook 102 References 103 5 Co-templating in the Designed Synthesis of Small-pore Zeolite Catalysts 113 Ruxandra G. Chitac, Mervyn D. Shannon, Paul A. Cox, James Mattock, Paul A. Wright, and Alessandro Turrina 5.1 Introduction 113 5.1.1 Definitions: Templates and Structure Directing Agents; Co-templating; Dual Templating; Mixed Templating 114 5.2 SAPO Zeotypes: “Model” Systems for Co-templating 116 5.2.1 The CHA-AEI-SAV-KFI System 116 5.2.2 Development of a Retrosynthetic Co-templating Approach for ABC-6 Structure Types 118 5.3 Co-templating Aluminosilicate Zeolites 120 5.3.1 Inorganic/Organic Co-templates 121 5.3.1.1 Targeting new phases in the RHO family using divalent cations 121 5.3.1.2 Designed synthesis of the aluminosilicate SWY, STA-30 123 5.3.1.3 Co-templating and the charge density mismatch approach 124 5.3.2 Two Organic Templates in Zeolite Synthesis 125 5.3.2.1 Applications of Dual/Mixed Organic Templating 125 5.4 Intergrowth Zeolite Structures as Co-templated Materials 127 5.5 Discussion 134 5.6 Conclusions 138 Acknowledgments 138 References 138 6 Computer Generation of Hypothetical Zeolites 145 Estefania Argente, Soledad Valero, Alechania Misturini, Michael M.J. Treacy, Laurent Baumes, and German Sastre 6.1 Introduction 145 6.2 Genetic Algorithms 146 6.2.1 Codification of Genetic Algorithms 147 6.2.2 Selection Operators for Genetic Algorithms 147 6.2.3 Crossover Operators for Genetic Algorithms 149 6.2.4 Mutation Operators for Genetic Algorithms 150 6.3 Algorithms for Zeolite Structure Determination and Prediction 151 6.3.1 Zefsaii 152 6.3.2 FraGen (Framework Generator) 152 6.3.3 SCIBS (Symmetry-Constrained Intersite Bonding Search) 153 6.3.4 TTL GRINSP (Geometrically Restrained Inorganic Structure Prediction) 154 6.3.5 EZs (Exclusive Zones) 155 6.3.6 P-GHAZ (Parallel Genetic Hybrid Algorithm for Zeolites) 155 6.3.7 zeoGAsolver 156 6.4 zeoGAsolver: A Specific Example of Genetic Algorithm for ZSD 156 6.4.1 Setting Up and Coding Scheme 157 6.4.2 Initialization 157 6.4.3 Fitness Evaluation 157 6.4.4 Crossover 159 6.4.5 Population Reduction and Termination Criterion 160 6.5 Graphics Processing Units in Zeolite Structure Determination and Prediction 160 6.5.1 Quick Presentation of GPU Cards 160 6.5.2 Efficient Parallelization of Evolutionary Algorithms on GPUs 161 6.5.3 Genetic Algorithms on GPUs for Zeolite Structures Problem 162 6.5.4 GPUs in Island Model for Interrupted Zeolitic Frameworks 167 6.6 Conclusions 168 Acknowledgments 169 References 169 7 Numerical Representations of Chemical Data for Structure-Based Machine Learning 173 Gyoung S. Na 7.1 Machine Readable Data Formats 173 7.1.1 Feature Vectors 173 7.1.2 Matrices 174 7.1.3 Mathematical Graphs 175 7.2 Graph-based Molecular Representations 175 7.2.1 Chemical Representations of Molecular Structures 175 7.2.2 Molecular Graphs 176 7.2.3 XYZ File to Molecular Graph 177 7.2.4 SMILES to Molecular Graph 178 7.2.5 Multiple Molecular Graph 178 7.3 Machine Learning with Molecular Graphs 179 7.3.1 General Architecture of Graph Neural Networks 179 7.3.2 Graph Convolutional Network 181 7.3.3 Graph Attention Network 182 7.3.4 Continuous Kernel-based Convolutional Network 182 7.3.5 Crystal Graph Convolutional Neural Network 183 7.4 Graph-based Machine Learning for Molecular Interactions 183 7.4.1 Vector Concatenation Approach to Prediction Molecule-to-Molecule Interactions 184 7.4.2 Attention Map Approach for Interpretable Prediction of Molecule-to-Molecule Interactions 185 7.5 Representation Learning from Molecular Graphs 186 7.5.1 Unsupervised Representation Learning 187 7.5.2 Supervised Representation Learning 187 7.6 Python Implementations 189 7.6.1 Data Conversion: Molecular Structures to Molecular Graphs 190 7.6.2 Machine Learning: Deep Learning Frameworks for Graph Neural Networks 190 7.6.3 Pymatgen for Crystal Structures 192 7.7 Graph-based Machine Learning for Chemical Applications 193 7.7.1 Message Passing Neural Network to Predict Physical Properties of Molecules 193 7.7.2 Scale-Aware Prediction of Molecular Properties 193 7.7.3 Prediction of Optimal Properties From Chromophore-Solvent Interactions 194 7.7.4 Drug Discovery with Reinforcement Learning 195 7.7.5 Graph Neural Networks for Crystal Structures 195 7.8 Conclusion 196 References 196 8 Extracting Metal-Organic Frameworks Data from the Cambridge Structural Database 201 Aurelia Li, Rocio Bueno-Perez, and David Fairen-Jimenez 8.1 Introduction 201 8.2 Building the CSD MOF Subset 203 8.2.1 What Is a MOF? 203 8.2.2 ConQuest 204 8.3 The CSD MOF Subset 208 8.3.1 Removing Solvents With the CSD Python API 209 8.3.2 Adding Missing Hydrogens 209 8.4 Textural Properties of MOFs and Their Evolution 210 8.5 Classification of MOFs 211 8.5.1 Identification of Target MOF Families 212 8.5.2 Identification of Surface Functionalities in MOFs 217 8.5.3 Identification of Chiral MOFs 217 8.5.4 Porous Network Connectivity and Framework Dimensionality 218 8.5.5 An Insight into Crystal Quality of Different MOF Families 220 8.6 The CSD MOF Subset Among All the MOF Databases 223 8.7 Conclusions 225 Acknowledgments 226 References 226 9 Data-Driven Approach for Rational Synthesis of Zeolites and Other Nanoporous Materials 233 Watcharop Chaikittisilp 9.1 Introduction 233 9.2 Rationalization of the Synthesis–Structure Relationship in Zeolite Synthesis: Application Machine Learning and Graph Theory to Zeolite Synthesis 234 9.3 Extraction of the Structure–Property Relationship in Nanoporous Nitrogen-Doped Carbons: Dealing with the Missing Values in Literature Data 239 9.4 Acceleration of Experimental Exploration of Nanoporous Metal Alloys: An Active Learning Approach 243 9.5 Summary 247 Acknowledgments 248 References 248 10 Porous Molecular Materials: Exploring Structure and Property Space with Software and Artificial Intelligence 251 Steven Bennett and Kim E. Jelfs 10.1 Introduction 251 10.2 Computational Modeling of Porous Molecular Materials 255 10.2.1 Structure Prediction 256 10.2.2 Modeling Porosity 257 10.2.3 Amorphous and Liquid Phase Simulations 259 10.3 Data-Driven Discovery: Applying Artificial Intelligence Methods to Materials Discovery 260 10.3.1 Training Data Generation 262 10.3.1.1 Hypothetical Structure Datasets 262 10.3.1.2 Experimental Structure Datasets 263 10.3.1.3 Extraction of Data From Scientific Literature 263 10.3.1.4 Data Augmentation and Transfer Learning 263 10.3.2 Descriptor Construction and Selection 264 10.3.2.1 Local Environment Descriptors 264 10.3.2.2 Global Environment Descriptors 265 10.4 Efficient Traversal of the Chemical Space of Porous Materials 266 10.4.1 Evolutionary Algorithms 266 10.4.2 Reducing the Number of Experiments: Bayesian Optimization and Active Learning 267 10.4.3 Chemical Space Exploration with Deep Learning 268 10.5 Considering Synthetic Accessibility 269 10.6 Closing the Loop: How Can High-Throughput Experimentation Feed Back into Computation? 270 10.6.1 High-Throughput and Autonomous Experimentation 271 10.7 Conclusions 272 References 272 11 Machine Learning-Aided Discovery of Nanoporous Materials for Energy- and Environmental-Related Applications 283 Archit Datar, Qiang Lyu, and Li-Chiang Lin 11.1 Introduction 283 11.1.1 Nanoporous Materials 283 11.1.2 History and Development 283 11.1.3 Gas Separation and Storage Applications 284 11.1.4 Large-Scale Computational Screening for Gas Separation and Storage 284 11.2 Concepts and Background for Data-Driven Approaches 286 11.2.1 Dimensionality Reduction 286 11.2.2 Machine Learning Models 287 11.2.2.1 Linear Models 287 11.2.2.2 Decision Trees and Random Forests 288 11.2.2.3 Support Vector Machine 289 11.2.2.4 Neural Networks 289 11.2.2.5 Unsupervised Learning 290 11.3 Data-Driven Approaches 290 11.3.1 Nanoporous Structure Datasets 291 11.3.2 Identifying Feature Space of Materials to Screen 292 11.3.3 Methods to Search for Optimal Structures 295 11.3.4 Modeling Interatomic and Intermolecular Interactions 297 11.4 Case Studies 300 11.4.1 Post-Combustion CO2 Capture 300 11.4.2 Methane Storage 303 11.4.3 Hydrogen Storage 305 11.5 Summary and Outlook 309 References 311 12 Big Data Science in Nanoporous Materials: Datasets and Descriptors 319 Maciej Haranczyk and Giulia Lo Dico 12.1 Introduction 319 12.2 Repositories of Nanoporous Material Structures 321 12.2.1 Experimental Crystal Structures 321 12.2.2 Predicted Crystal Structures 322 12.3 Descriptors 325 12.3.1 Handcrafted Descriptors 325 12.3.2 Toward Automatically Generated and Multi-Scale Descriptors 328 12.4 Properties 329 12.5 Data Analysis 330 12.5.1 Material Similarity and Distance Measures 330 12.5.1.1 Diversity Selection 331 12.5.1.2 Cluster Analysis 332 12.6 Machine Learning Models of Structure–Property Relationships 333 12.7 Current and Future Applications 335 References 336 13 Efficient Data Utilization in Training Machine Learning Models for Nanoporous Materials Screening 343 Diego A. Gómez-Gualdrón, Cory M. Simon, and Yamil J. Colón 13.1 Descriptor Selection 344 13.1.1 Engineering of Advanced Features 344 13.1.2 Engineering of Simpler Features 347 13.2 Material Selection 349 13.3 Model Selection 351 13.3.1 Linear Regression 353 13.3.2 Supported Vector Regressors 354 13.3.3 Decision Tree-based Regressors 355 13.3.4 Artificial Neural Networks 357 13.4 Data Usage Strategies 360 13.4.1 Transfer Learning 361 13.4.2 Multipurpose Models 365 13.4.3 Material Recommendation Systems 368 13.4.4 Active Learning 370 13.4.5 Machine Learning to Speed Up Data Generation 371 13.5 Summary and Outlook 374 References 375 14 Machine Learning and Digital Manufacturing Approaches for Solid-State Materials Development 377 Lawson T. Glasby, Emily H. Whaites, and Peyman Z. Moghadam 14.1 Introduction 377 14.2 The Development of MOF Databases 379 14.3 Natural Language Processing 380 14.4 An Overview of Machine Learning Models 383 14.5 Machine Learning for Synthesis and Investigation of Solid State Materials 386 14.6 Machine Learning in Design and Discovery of MOFs 388 14.7 Current Limitations of Machine Learning for MOFs 392 14.8 Automated Synthesis and Digital Manufacturing 394 14.9 Digital Manufacturing of MOFs 401 14.10 The Future of Digital Manufacturing 403 References 404 15 Overview of AI in the Understanding and Design of Nanoporous Materials 411 Seyed Mohamad Moosavi, Frits Daeyaert, Michael W. Deem, and German Sastre 15.1 Introduction 411 15.2 Databases 411 15.2.1 Structural Databases 412 15.2.2 Databases of Material Properties 412 15.2.3 Databases of Synthesis Protocols 413 15.3 Big-Data Science for Nanoporous Materials Design and Discovery 413 15.3.1 Representations of Chemical Data 413 15.3.2 Learning Algorithms 414 15.4 Applications 415 15.5 Zeolite Synthesis and OSDAs 417 15.6 Conclusion 420 References 420 Index 425

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Book SynopsisTable of ContentsPreface xvii 1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning 1P. Anbalagan, S. Saravanan and R. Saminathan 1.1 Introduction 2 1.2 Comprehensive Study 3 1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 7 1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 9 1.5 Conclusion 12 2 Impact of AI in 5G Wireless Technologies and Communication Systems 15A. Sivasundari and K. Ananthajothi 2.1 Introduction 16 2.2 Integrated Services of AI in 5G and 5G in AI 18 2.3 Artificial Intelligence and 5G in the Industrial Space 23 2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 25 2.5 Conclusion 28 3 Artificial Intelligence Revolution in Logistics and Supply Chain Management 31P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy 3.1 Introduction 32 3.2 Theory--AI in Logistics and Supply Chain Market 35 3.3 Factors to Propel Business Into the Future Harnessing Automation 40 3.4 Conclusion 43 4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning 47M. P. Vaishnnave and R. Manivannan 4.1 Introduction 47 4.2 An Overview of Reinforcement Learning in Agriculture 49 4.3 Reinforcement Learning Startups for Crop Prediction 52 4.4 Conclusion 57 5 Cost Optimization for Inventory Management in Blockchain and Cloud 59C. Govindasamy, A. Antonidoss and A. Pandiaraj 5.1 Introduction 60 5.2 Blockchain: The Future of Inventory Management 62 5.3 Cost Optimization for Blockchain Inventory Management in Cloud 66 5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 71 5.5 Conclusion 72 6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases 75G. Gangadevi and C. Jayakumar 6.1 Introduction 75 6.2 Literature Review 76 6.3 Proposed Idea 82 6.4 Reference Gap 86 6.5 Conclusion 87 7 Generating Art and Music Using Deep Neural Networks 91A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna 7.1 Introduction 91 7.2 Related Works 92 7.3 System Architecture 94 7.4 System Development 96 7.5 Algorithm-LSTM 100 7.6 Result 100 7.7 Conclusions 101 8 Deep Learning Era for Future 6G Wireless Communications--Theory, Applications, and Challenges 105S.K.B. Sangeetha and R. Dhaya 8.1 Introduction 106 8.2 Study of Wireless Technology 108 8.3 Deep Learning Enabled 6G Wireless Communication 113 8.4 Applications and Future Research Directions 117 9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks 121J. Banumathi, S.K.B. Sangeetha and R. Dhaya 9.1 Introduction 122 9.2 Spectrum Sensing in Cognitive Radio Networks 122 9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 124 9.4 Cooperative Sensing Among Cognitive Radios 125 9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 128 9.6 Spectrum Agile Radios: Utilization and Sensing Architectures 128 9.7 Some Fundamental Limits on Cognitive Radio 130 9.8 Cooperative Strategies and Capacity Theorems for Relay Networks 131 9.9 Research Challenges in Cooperative Communication 133 9.10 Conclusion 135 10 Natural Language Processing 139S. Meera and S. Geerthik 10.1 Introduction 139 10.2 Conclusions 152 References 152 11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval 155D. Sujatha, M. Subramaniam and A. Kathirvel 11.1 Introduction 156 11.2 Literature Review 158 11.3 Class Level Semantic Similarity-Based Retrieval 159 11.4 Results and Discussion 164 12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes 175J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya 12.1 Introduction 176 12.2 Literature Survey 176 12.3 Proposed Work 177 12.4 Results 180 12.5 Conclusion and Future Work 190 13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation 193Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna 13.1 Introduction 194 13.2 Background 196 13.3 Issues and Gap Identified 197 13.4 Main Focus of the Chapter 198 13.5 Mobility 199 13.6 Routing Protocol 201 13.7 High Altitude Platforms (HAPs) 202 13.8 Connectivity Graph Metrics 204 13.9 Aerial Vehicle Network Simulator (AVENs) 206 13.10 Conclusion 207 14 Artificial Intelligence in Logistics and Supply Chain 211Jeyaraju Jayaprakash 14.1 Introduction to Logistics and Supply Chain 212 14.2 Recent Research Avenues in Supply Chain 217 14.3 Importance and Impact of AI 222 14.4 Research Gap of AI-Based Supply Chain 224 15 Hereditary Factor-Based Multi-Featured Algorithm for Early Diabetes Detection Using Machine Learning 235S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan 15.1 Introduction 236 15.2 Literature Review 237 15.3 Objectives of the Proposed System 244 15.4 Proposed System 245 15.5 HIVE and R as Evaluation Tools 246 15.6 Decision Trees 247 15.7 Results and Discussions 250 15.8 Conclusion 252 16 Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism 255V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai 16.1 Introduction 255 16.2 Related Study 258 16.3 System Model 259 16.4 Experiments and Results 264 16.5 Conclusion 267 17 Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing 269R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumar and R. Mahaveerakannan 17.1 Introduction 270 17.2 New Development of Artificial Intelligence 271 17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 271 17.4 Current Status and Problems of Green Manufacturing 272 17.5 Artificial Intelligence for Green Manufacturing 276 17.6 Detailed Description of Common Encryption Algorithms 280 17.7 Current and Future Works 282 17.8 Conclusion 283 18 Deep Learning in 5G Networks 287G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani 18.1 5G Networks 287 18.2 Artificial Intelligence and 5G Networks 291 18.3 Deep Learning in 5G Networks 293 19 EIDR Umpiring Security Models for Wireless Sensor Networks 299A. Kathirvel, S. Navaneethan and M. Subramaniam 19.1 Introduction 299 19.2 A Review of Various Routing Protocols 302 19.3 Scope of Chapter 307 19.4 Conclusions and Future Work 311 20 Artificial Intelligence in Wireless Communication 317Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta 20.1 Introduction 318 20.2 Artificial Intelligence: A Grand Jewel Mine 318 20.3 Wireless Communication: An Overview 320 20.4 Wireless Revolution 320 20.5 The Present Times 321 20.6 Artificial Intelligence in Wireless Communication 321 20.7 Artificial Neural Network 324 20.8 The Deployment of 5G 326 20.9 Looking Into the Features of 5G 327 20.10 AI and the Internet of Things (IoT) 328 20.11 Artificial Intelligence in Software-Defined Networks (SDN) 329 20.12 Artificial Intelligence in Network Function Virtualization 331 20.13 Conclusion 332 References 332 Index 335

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Book SynopsisTHE NEW ADVANCED SOCIETY Included in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial Internet of Things, featuring their working principles and application in different sectors. A 360-degree view of the different dimensions of the digital revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead, and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart transTable of ContentsPreface xvii Acknowledgments xxiii 1 Post Pandemic: The New Advanced Society 1Sujata Priyambada Dash 1.1 Introduction 1 1.1.1 Themes 2 1.1.1.1 Theme: Areas of Management 2 1.1.1.2 Theme: Financial Institutions Cyber Crime 3 1.1.1.3 Theme: Economic Notion 4 1.1.1.4 Theme: Human Depression 6 1.1.1.5 Theme: Migrant Labor 7 1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions 9 1.1.1.7 School and Colleges Closures 11 1.2 Conclusions 12 References 12 2 Distributed Ledger Technology in the Construction Industry Using Corda 15Sandeep Kumar Panda, Shanmukhi Priya Daliyet, Shagun S. Lokre and Vihas Naman 2.1 Introduction 16 2.2 Prerequisites 16 2.2.1 DLT vs Blockchain 17 2.3 Key Points of Corda 18 2.3.1 Some Salient Features of Corda 20 2.3.2 States 20 2.3.3 Contract 22 2.3.3.1 Create and Assign Task (CAT) Contract 22 2.3.3.2 Request for Cash (RT) Contract 23 2.3.3.3 Transfer of Cash (TT) Contract 24 2.3.3.4 Updation of the Task (UOT) Contract 24 2.3.4 Flows 25 2.3.4.1 Flow Associated With CAT Contract 25 2.3.4.2 Flow Associated With RT Contract 26 2.3.4.3 Flow Associated With TT Contract 26 2.3.4.4 Flow Associated With UOT Contract 26 2.4 Implementation 26 2.4.1 System Overview 27 2.4.2 Working Flowchart 28 2.4.3 Experimental Demonstration 29 2.5 Future Work 35 2.6 Conclusion 36 References 37 3 Identity and Access Management for Internet of Things Cloud 43Soumya Prakash Otta and Subhrakanta Panda 3.1 Introduction 44 3.2 Internet of Things (IoT) Security 45 3.2.1 IoT Security Overview 45 3.2.2 IoT Security Requirements 46 3.2.3 Securing the IoT Infrastructure 49 3.3 IoT Cloud 49 3.3.1 Cloudification of IoT 50 3.3.2 Commercial IoT Clouds 52 3.3.3 IAM of IoT Clouds 54 3.4 IoT Cloud Related Developments 55 3.5 Proposed Method for IoT Cloud IAM 58 3.5.1 Distributed Ledger Approach for IoT Security 59 3.5.2 Blockchain for IoT Security Solution 60 3.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM 62 3.6 Conclusion 64 References 65 4 Automated TSR Using DNN Approach for Intelligent Vehicles 67Banhi Sanyal, Piyush R. Biswal, R.K. Mohapatra, Ratnakar Dash and Ankush Agarwalla 4.1 Introduction 68 4.2 Literature Survey 69 4.3 Neural Network (NN) 70 4.4 Methodology 71 4.4.1 System Architecture 71 4.4.2 Database 71 4.5 Experiments and Results 71 4.5.1 FFNN 74 4.5.2 RNN 76 4.5.3 CNN 76 4.5.4 CNN 76 4.5.5 Pre-Trained Models 79 4.6 Discussion 79 4.7 Conclusion 80 References 88 5 Honeypot: A Trap for Attackers 91Anjanna Matta, G. Sucharitha, Bandlamudi Greeshmanjali, Manji Prashanth Kumar and Mathi Naga Sarath Kumar 5.1 Introduction 92 5.1.1 Research Honeypots 93 5.1.2 Production Honeypots 93 5.2 Method 94 5.2.1 Low-Interaction Honeypots 94 5.2.2 Medium-Interaction Honeypots 95 5.2.3 High-Interaction Honeypots 95 5.3 Cryptanalysis 96 5.3.1 System Architecture 96 5.3.2 Possible Attacks on Honeypot 97 5.3.3 Advantages of Honeypots 98 5.3.4 Disadvantages of Honeypots 99 5.4 Conclusions 99 References 100 6 Examining Security Aspect in Industrial-Based Internet of Things 103Rohini Jha 6.1 Introduction 104 6.2 Process Frame of IoT Before Security 105 6.2.1 Cyber Attack 107 6.2.2 Security Assessment in IoT 107 6.2.2.1 Security in Perception and Network Frame 108 6.3 Attacks and Security Assessments in IIoT 111 6.3.1 IoT Security Techniques Analysis Based on its Merits 111 6.4 Conclusion 116 References 119 7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm 123D. Chandrasekhar Rao 7.1 Introduction 124 7.2 Related Works 126 7.3 Problem Formulation 130 7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm 134 7.4.1 Basic Jaya Algorithm 134 7.5 Hybrid Jaya-DE 136 7.5.1 Mutation 136 7.5.2 Crossover 136 7.5.3 Selection 137 7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm 139 7.7 Total Navigation Path Deviation (TNPD) 147 7.8 Average Unexplored Goal Distance (AUGD) 148 7.9 Conclusion 159 References 159 8 Categorization Model for Parkinson’s Disease Occurrence and Severity Prediction 163Prashant Kumar Shrivastava, Ashish Chaturvedi, Megha Kamble and Megha Jain 8.1 Introduction 164 8.2 Applications 166 8.2.1 Machine Learning in PD Diagnosis 166 8.2.2 Challenges of PD Detection 169 8.2.3 Structuring of UPDRS Score 170 8.3 Methodology 173 8.3.1 Overview of Data Driven Intelligence 173 8.3.2 Comparison Between Deep Learning and Traditional Machine 175 8.3.3 Deep Learning for PD Diagnosis 176 8.3.4 Convolution Neural Network for PD Diagnosis 176 8.4 Proposed Models 178 8.4.1 Classification of Patient and Healthy Controls 178 8.4.2 Severity Score Classification 181 8.5 Results and Discussion 184 8.5.1 Performance Measures 185 8.5.2 Graphical Results 187 8.6 Conclusion 187 References 187 9 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images 191Shounak Chakraborty, Nikumani Choudhury and Indrajit Kalita 9.1 Introduction 192 9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images 194 9.3 Deep Learning-Based Agriculture Monitoring 196 9.4 Adaptive Approaches for Multi-Modal Classification 197 9.4.1 Unsupervised DA 199 9.4.2 Semi-Supervised DA 200 9.4.3 Active Learning-Based DA 201 9.5 System Model 202 9.6 IEEE 802.15.4 204 9.6.1 802.15.4 MAC 204 9.6.2 DSME MAC 205 9.6.3 TSCH MAC 206 9.7 Analysis of IEEE 802.15.4 for Smart Agriculture 207 9.7.1 Effect of Device Specification 207 9.7.1.1 Low-Power 208 9.7.2 Effect of MAC Protocols 208 9.8 Experimental Results 209 9.9 Conclusion & Future Directions 212 References 212 10 Car Buying Criteria Evaluation Using Machine Learning Approach 223Samdeep Kumar Panda 10.1 Introduction 224 10.2 Literature Survey 225 10.3 Proposed Method 226 10.4 Dataset 227 10.5 Exploratory Data Analysis 227 10.6 Splitting of Data Into Training Data and Test Data 230 10.7 Pre-Processing 232 10.8 Training of Our Models 232 10.8.1 Gaussian Naïve Bayes 233 10.8.2 Decision Tree Classifier 234 10.8.3 Tuning the Model 235 10.8.4 Karnough Nearest Neighbor Classifier 236 10.8.5 Tuning the Model 237 10.8.6 Neural Network 238 10.8.7 Tuning the Model 239 10.9 Result Analysis 240 10.9.1 Confusion Matrix 240 10.9.2 Gaussian Naïve Bayes 241 10.9.3 Decision Tree Classifier 242 10.9.4 Karnough Nearest Neighbor Classifier 242 10.9.5 Neural Network 242 10.9.6 Accuracy Scores 243 10.10 Conclusion and Future Work 244 References 244 11 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns 247Md. Safiullah and Neha Parveen 11.1 Introduction 248 11.2 Big Data Reveals the Voters’ Preference 249 11.2.1 Use of Software Applications in Election Campaigns 251 11.2.1.1 Team Joe App 252 11.2.1.2 Trump 2020 252 11.2.1.3 Modi App 253 11.3 Deep Fakes and Election Campaigns 254 11.3.1 Deep Fake in Delhi Elections 254 11.4 Social Media Bots 256 11.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns 259 References 259 12 Impact of Optimized Segment Routing in Software Defined Network 263Amrutanshu Panigrahi, Bibhuprasad Sahu, Satya Sobhan Panigrahi, Ajay Kumar Jena and Md. Sahil Khan 12.1 Introduction 264 12.2 Software-Defined Network 266 12.3 SDN Architecture 268 12.4 Segment Routing 270 12.5 Segment Routing in SDN 272 12.6 Traffic Engineering in SDN 274 12.7 Segment Routing Protocol 275 12.8 Simulation and Result 277 12.9 Conclusion and Future Work 278 References 283 13 An Investigation into COVID-19 Pandemic in India 289Shubhangi V. Urkude, Vijaykumar R. Urkude, S. Vairachilai and Sandeep Kumar Panda 13.1 Introduction 289 13.1.1 Symptoms of COVID-19 292 13.1.2 Precautionary Measures 292 13.1.3 Ways of Spreading the Coronavirus 294 13.2 Literature Survey 295 13.3 Technologies Used to Fight COVID-19 296 13.3.1 Robots 296 13.3.2 Drone Technology 297 13.3.3 Crowd Surveillance 297 13.3.4 Spraying the Disinfectant 298 13.3.5 Sanitizing the Contaminated Areas 298 13.3.6 Monitoring Temperature Using Thermal Camera 298 13.3.7 Delivering the Essential Things 298 13.3.8 Public Announcement in the Infected Areas 298 13.4 Impact of COVID-19 on Business 299 13.4.1 Impact on Financial Markets 299 13.4.2 Impact on Supply Side 299 13.4.3 Impact on Demand Side 300 13.4.4 Impact on International Trade 300 13.5 Impact of COVID-19 on Indian Economy 300 13.6 Data and Result Analysis 300 13.7 Conclusion and Future Scope 304 References 304 14 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy 307Poonam Biswal, Monali Saha, Nishtha Jaiswal and Minakhi Rout 14.1 Introduction 307 14.2 Literature Survey 308 14.3 Methodology 310 14.3.1 Dataset Preparation 310 14.3.2 Dataset Loading and Data Pre-Processing 311 14.3.3 Creating Models 312 14.4 Models Used 312 14.5 Simulation Results 313 14.5.1 Changing Size of MaxPool2D(n,n) 314 14.5.2 Changing Size of AveragePool2D(n,n) 314 14.5.3 Changing Number of con2d(32n–64n) Layers 315 14.5.4 Changing Number of con2d-32*n Layers 315 14.5.5 ROC Curves and MSE Curves 318 14.6 Conclusion 321 References 321 15 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain 323Abhishek Kumar Kashyap, Anish Pandey and Dayal R. Parhi 15.1 Introduction 324 15.2 Design of Proposed Algorithm 328 15.2.1 Mechanism of Artificial Potential Field 328 15.2.1.1 Potential Field Generated by Attractive Force of Goal 329 15.2.1.2 Potential Field Generated by Repulsive Force of Obstacle 331 15.2.2 Mechanism of Firefly Algorithm 332 15.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm 335 15.2.3 Dining Philosopher Controller 337 15.3 Hybridization Process of Proposed Algorithm 339 15.4 Execution of Proposed Algorithm in Multiple Humanoid Robots 339 15.5 Comparison 344 15.6 Conclusion 346 References 346 16 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society 351Vinutha D.C., Kavyashree S., Vijay C.P. and G.T. Raju 16.1 Introduction 352 16.2 Literature Survey 353 16.2.1 AI in Auto-Grading 354 16.2.2 AI in Smart Content 356 16.2.3 AI in Auto Analysis on Student’s Grade 356 16.2.4 AI Extends Free Intelligent Tutoring 357 16.2.5 AI in Predicting Student Admission and Drop-Out Rate 359 16.3 Proposed System 359 16.3.1 Data Collection Module 360 16.3.2 Data Pre-Processing Module 364 16.3.3 Clustering Module 364 16.3.4 Partner Selection Module 366 16.4 Results 368 16.5 Future Enhancements 370 16.6 Conclusion 370 References 371 17 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures 373N. Yamuna, J. Antony Vijay and B. Gomathi 17.1 Introduction 374 17.2 Literature Survey 375 17.2.1 Machine Learning 378 17.3 Proposed System 379 17.3.1 Load Aware Cloud Computing Model 379 17.3.2 Wavelet Neural Network 379 17.3.3 Evaluation Using LOOCV Model 380 17.3.4 k-Nearest Neighbor (k-NN) Algorithm 381 17.3.5 Particle Swarm Optimization (PSO) Algorithm 382 17.3.6 HWkNN Optimization Algorithm Based on PSO 383 17.3.7 PSO-Based HWkNN (PHWkNN) Load Prediction Algorithm 384 17.4 Experimental Results 385 17.5 Conclusion 390 References 391 18 An Extensive Survey on the Prediction of Bankruptcy 395Sasmita Manjari Nayak and Minakhi Rout 18.1 Introduction 395 18.2 Literature Survey 397 18.2.1 Data Pre-Processing 397 18.2.1.1 Balancing of Imbalanced Dataset 397 18.2.1.2 Outlier Data Handling 410 18.2.2 Classifiers 418 18.2.3 Ensemble Models 422 18.3 System Architecture and Simulation Results 438 18.4 Conclusion 438 References 443 19 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques 447Manoj Kumar, Pratibha Maurya and Rinki Verma 19.1 Introduction 448 19.2 Overview of AI and Machine Learning 450 19.3 Review of Literature 452 19.4 Application of AI & Machine Learning in Agriculture 456 19.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector 460 19.6 Opportunities for Agricultural Operations in India 465 19.7 Conclusion 466 References 467 Index 473

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Book SynopsisArtificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and pracTable of ContentsList of Contributors xix Foreward xxi Preface xxiii Acknowledgements xxv 1 Motivations for Automating Process Fault Analysis 1 1.1 Introduction 2 1.2 The Changing Role of the Process Operators in Plant Operations 4 1.3 Traditional Methods for Performing Process Fault Management 7 1.4 Limitations of Human Operators in Performing Process Fault Management 8 1.5 The Role of Automated Process Fault Analysis 12 2 Various Process Fault Diagnostic Methodologies 16 2.1 Introduction 17 2.2 Various Alternative Diagnostic Strategies Overview 18 2.3 Diagnostic Methodology Choice Conclusions 35 2.A Failure Modes and Effects Analysis 40 3 Alarm Management and Fault Detection 45 3.1 Introduction 46 3.2 Applicable Definitions and Guidelines 46 3.3 The Alarm Management Life Cycle 49 3.4 Generation of Diagnostic Information 53 3.5 Presentation of the Diagnostic Information 55 3.6 Information Rates 59 4 Operator Performance: Simulation and Automation 63 4.1 Background 63 4.2 Automation 65 4.3 Simulation 68 4.4 Research 69 4.5 AI Integration 73 4.6 Case Study: Turbo Expanders Over-Speed 77 4.7 Human-Centered AI 80 5 AI and Alarm Analytics for Failure Analysis and Prevention 85 5.1 Introduction 86 5.2 Post-Alarm Assessment and Analysis 87 5.3 Real-Time Alarm Activity Database and Operator Action Journal 89 5.4 Pre-Alarm Assessment and Analysis 91 5.5 Utilizing Alarm Assessment Information 92 5.6 Examining the Alarm System to Resolve Failures on a Wider Scale 93 5.7 Emerging Methods of Alarm Analysis 99 5.8 Deep Reinforcement Learning for Alarming and Failure Assessment 103 5.9 Some Typical AI and Machine Learning Examples for Further Study 103 5.10 Wrap-Up 111 5.A Process State Transition Logic Employed by the Original FMC Falconeer KBS 112 5.B Process State Transition Logic and its Routine Use in Falconeer IV 123 6 Process Fault Detection Based on Time-Explicit Kiviat Diagram 131 6.1 Introduction 132 6.2 Time-Explicit Kiviat Diagram 133 6.3 Fault Detection Based on the Time-Explicit Kiviat Diagram 134 6.4 Continuous Processes 136 6.5 Batch Processes 138 6.6 Periodic Processes 140 6.7 Case Studies 141 6.8 Continuous Processes 141 6.9 Batch Processes 144 6.10 Periodic Processes 147 6.11 Conclusions 149 6.A Virtual Statistical Process Control Analysis 151 7 Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization 160 7.1 Introduction to Process Operational Health Modeling 163 7.2 Diagnostic Localization – Key Concepts 165 7.3 Time 178 7.4 The Workflow of Diagnostic Localization 184 7.5 DL-CLA Use Case Implementation: Nova Chemical Ethylene Splitter 191 7.6 Analyzing Potential Malfunctions Over Time 198 7.7 Analysis of Various Operational Scenarios 201 7.8 DL-CLA Integration with Smart Manufacturing (SM) 208 7.9 AN FR Model Library 210 7.10 Conclusions 216 8 Optimal Quantitative Model-Based Process Fault Diagnosis 221 8.1 Introduction 222 8.2 Process Fault Analysis Concept Terminology 223 8.3 MOME Quantitative Models Overview 226 8.4 MOME Quantitative Model Diagnostic Strategy 234 8.5 MOME SV&PFA Diagnostic Rules’ Logic Compiler Motivations 248 8.6 MOME Fuzzy Logic Algorithm Overview 250 8.7 Summary of the Mome Diagnostic Strategy 265 8.8 Actual Process System KBS Application Performance Results 266 8.9 Conclusions 267 8.A Falconeer IV Fuzzy Logic Algorithm Pseudo-Code 272 8.B Mome Conclusions 281 9 Fault Detection Using Artificial Intelligence and Machine Learning 286 9.1 Introduction 287 9.2 Artificial Intelligence 287 9.3 Machine Learning 288 9.4 Engineered Features 290 9.5 Machine Learning Algorithms 291 10 Knowledge-Based Systems 300 10.1 Introduction 301 10.2 Knowledge 301 10.3 Information Required for Diagnosis 304 10.4 Knowledge Representation 305 10.5 Maintaining, Updating, and Extending Knowledge 309 10.6 Expert Systems 311 10.7 Digitization, Digitalization, Digital Transformation, and Digital Twins 319 10.8 Fault Diagnosis with Knowledge-Based Systems 322 10.9 Graphical Representation of Fault Diagnosis 325 10.10 Conclusions 337 10.A Compressor Trip Prediction 340 11 The Falcon Project 343 11.1 Introduction 344 11.2 The Diagnostic Philosophy Underlying the Falcon System 345 11.3 Target Process System 346 11.4 The Fielded Falcon System 348 11.5 The Derivation of the FALCON Diagnostic Knowledge Base 355 11.6 The Ideal FALCON System 369 11.7 Use of the Knowledge-Based System Paradigm in Problem 12 Fault Diagnostic Application Implementation and Sustainability 374 12.1 Key Principles of Successfully Implementing New Technology 375 12.2 Expectation of Advanced Technology 376 12.3 Defining Success 379 12.4 Learning from History 379 12.5 Example: Regulatory Control Loop Monitoring 380 12.6 What Success Looks Like 385 12.7 Example: Systematic Stewardship 386 12.8 Conclusions 387 13 Process Operators, Advanced Process Control, and Artificial Intelligence-Based Applications in the Control Room 389 13.1 Introduction 391 13.2 History of Sustainable APC 392 13.3 Operators as Ultimate APC Application End Users 394 13.4 APC Application Design Considerations 395 13.5 APC Development – Internal Versus External Experts 398 13.6 APC Technology 398 13.7 APC Support 400 13.8 Conclusions 402 References 402 Index 404

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Book SynopsisDistributed Systems Comprehensive textbook resource on distributed systemsintegrates foundational topics with advanced topics of contemporary importance within the field Distributed Systems: Theory and Applications is organized around three layers of abstractions: networks, middleware tools, and application framework. It presents data consistency models suited for requirements of innovative distributed shared memory applications. The book also focuses on distributed processing of big data, representation of distributed knowledge and management of distributed intelligence via distributed agents. To aid in understanding how these concepts apply to real-world situations, the work presents a case study on building a P2P Integrated E-Learning system. Downloadable lecture slides are included to help professors and instructors convey key concepts to their students. Additional topics discussed in Distributed Systems: Theory and Applications include: Table of ContentsAbout the Authors xv Preface xvii Acknowledgments xxi Acronyms xxiii 1 Introduction 1 1.1 Advantages of Distributed Systems 1 1.2 Defining Distributed Systems 3 1.3 Challenges of a Distributed System 5 1.4 Goals of Distributed System 6 1.4.1 Single System View 7 1.4.2 Hiding Distributions 7 1.4.3 Degrees and Distribution of Hiding 9 1.4.4 Interoperability 10 1.4.5 Dynamic Reconfiguration 10 1.5 Architectural Organization 11 1.6 Organization of the Book 12 Bibliography 13 2 The Internet 15 2.1 Origin and Organization 15 2.1.1 ISPs and the Topology of the Internet 17 2.2 Addressing the Nodes 17 2.3 Network Connection Protocol 20 2.3.1 IP Protocol 22 2.3.2 Transmission Control Protocol 22 2.3.3 User Datagram Protocol 22 2.4 Dynamic Host Control Protocol 23 2.5 Domain Name Service 24 2.5.1 Reverse DNS Lookup 27 2.5.2 Client Server Architecture 30 2.6 Content Distribution Network 32 2.7 Conclusion 34 Exercises 34 Bibliography 35 3 Process to Process Communication 37 3.1 Communication Types and Interfaces 38 3.1.1 Sequential Type 38 3.1.2 Declarative Type 39 3.1.3 Shared States 40 3.1.4 Message Passing 41 3.1.5 Communication Interfaces 41 3.2 Socket Programming 42 3.2.1 Socket Data Structures 43 3.2.2 Socket Calls 44 3.3 Remote Procedure Call 48 3.3.1 Xml RPC 52 3.4 Remote Method Invocation 55 3.5 Conclusion 59 Exercises 59 Additional Web Resources 61 Bibliography 61 4 Microservices, Containerization, and MPI 63 4.1 Microservice Architecture 64 4.2 REST Requests and APIs 66 4.2.1 Weather Data Using REST API 67 4.3 Cross Platform Applications 68 4.4 Message Passing Interface 78 4.4.1 Process Communication Models 78 4.4.2 Programming with MPI 81 4.5 Conclusion 87 Exercises 88 Additional Internet Resources 89 Bibliography 89 5 Clock Synchronization and Event Ordering 91 5.1 The Notion of Clock Time 92 5.2 External Clock Based Mechanisms 93 5.2.1 Cristian’s Algorithm 93 5.2.2 Berkeley Clock Protocol 94 5.2.3 Network Time Protocol 95 5.2.3.1 Symmetric Mode of Operation 96 5.3 Events and Temporal Ordering 97 5.3.1 Causal Dependency 99 5.4 Logical Clock 99 5.5 Causal Ordering of Messages 106 5.6 Multicast Message Ordering 107 5.6.1 Implementing FIFO Multicast 110 5.6.2 Implementing Causal Ordering 112 5.6.3 Implementing Total Ordering 113 5.6.4 Reliable Multicast 114 5.7 Interval Events 115 5.7.1 Conceptual Neighborhood 116 5.7.2 Spatial Events 118 5.8 Conclusion 120 Exercises 121 Bibliography 123 6 Global States and Termination Detection 127 6.1 Cuts and Global States 127 6.1.1 Global States 132 6.1.2 Recording of Global States 134 6.1.3 Problem in Recording Global State 138 6.2 Liveness and Safety 140 6.3 Termination Detection 143 6.3.1 Snapshot Based Termination Detection 144 6.3.2 Ring Method 145 6.3.3 Tree Method 148 6.3.4 Weight Throwing Method 151 6.4 Conclusion 153 Exercises 154 Bibliography 156 7 Leader Election 157 7.1 Impossibility Result 158 7.2 Bully Algorithm 159 7.3 Ring-Based Algorithms 160 7.3.1 Circulate IDs All the Way 161 7.3.2 As Far as an ID Can Go 162 7.4 Hirschberg and Sinclair Algorithm 163 7.5 Distributed Spanning Tree Algorithm 167 7.5.1 Single Initiator Spanning Tree 167 7.5.2 Multiple Initiators Spanning Tree 170 7.5.3 Minimum Spanning Tree 176 7.6 Leader Election in Trees 176 7.6.1 Overview of the Algorithm 176 7.6.2 Activation Stage 177 7.6.3 Saturation Stage 178 7.6.4 Resolution Stage 179 7.6.5 Two Nodes Enter SATURATED State 180 7.7 Leased Leader Election 182 7.8 Conclusion 184 Exercises 185 Bibliography 187 8 Mutual Exclusion 189 8.1 System Model 190 8.2 Coordinator-Based Solution 192 8.3 Assertion-Based Solutions 192 8.3.1 Lamport’s Algorithm 192 8.3.2 Improvement to Lamport’s Algorithm 195 8.3.3 Quorum-Based Algorithms 196 8.4 Token-Based Solutions 203 8.4.1 Suzuki and Kasami’s Algorithm 203 8.4.2 Singhal’s Heuristically Aided Algorithm 206 8.4.3 Raymond’s Tree-Based Algorithm 212 8.5 Conclusion 214 Exercises 215 Bibliography 216 9 Agreements and Consensus 219 9.1 System Model 220 9.1.1 Failures in Distributed System 221 9.1.2 Problem Definition 222 9.1.3 Agreement Problem and Its Equivalence 223 9.2 Byzantine General Problem (BGP) 225 9.2.1 BGP Solution Using Oral Messages 228 9.2.2 Phase King Algorithm 232 9.3 Commit Protocols 233 9.3.1 Two-Phase Commit Protocol 234 9.3.2 Three-Phase Commit 238 9.4 Consensus 239 9.4.1 Consensus in Synchronous Systems 239 9.4.2 Consensus in Asynchronous Systems 241 9.4.3 Paxos Algorithm 242 9.4.4 Raft Algorithm 244 9.4.5 Leader Election 246 9.5 Conclusion 248 Exercises 249 Bibliography 250 10 Gossip Protocols 253 10.1 Direct Mail 254 10.2 Generic Gossip Protocol 255 10.3 Anti-entropy 256 10.3.1 Push-Based Anti-Entropy 257 10.3.2 Pull-Based Anti-Entropy 258 10.3.3 Hybrid Anti-Entropy 260 10.3.4 Control and Propagation in Anti-Entropy 260 10.4 Rumor-mongering Gossip 261 10.4.1 Analysis of Rumor Mongering 262 10.4.2 Fault-Tolerance 265 10.5 Implementation Issues 265 10.5.1 Network-Related Issues 266 10.6 Applications of Gossip 267 10.6.1 Peer Sampling 267 10.6.2 Failure Detectors 270 10.6.3 Distributed Social Networking 271 10.7 Gossip in IoT Communication 273 10.7.1 Context-Aware Gossip 273 10.7.2 Flow-Aware Gossip 274 10.7.2.1 Fire Fly Gossip 274 10.7.2.2 Trickle 275 10.8 Conclusion 278 Exercises 279 Bibliography 280 11 Message Diffusion Using Publish and Subscribe 283 11.1 Publish and Subscribe Paradigm 284 11.1.1 Broker Network 285 11.2 Filters and Notifications 287 11.2.1 Subscription and Advertisement 288 11.2.2 Covering Relation 288 11.2.3 Merging Filters 290 11.2.4 Algorithms 291 11.3 Notification Service 294 11.3.1 Siena 294 11.3.2 Rebeca 295 11.3.3 Routing of Notification 296 11.4 MQTT 297 11.5 Advanced Message Queuing Protocol 299 11.6 Effects of Technology on Performance 301 11.7 Conclusions 303 Exercises 304 Bibliography 305 12 Peer-to-Peer Systems 309 12.1 The Origin and the Definition of P2P 310 12.2 P2P Models 311 12.2.1 Routing in P2P Network 312 12.3 Chord Overlay 313 12.4 Pastry 321 12.5 Can 325 12.6 Kademlia 327 12.7 Conclusion 331 Exercises 332 Bibliography 333 13 Distributed Shared Memory 337 13.1 Multicore and S-DSM 338 13.1.1 Coherency by Delegation to a Central Server 339 13.2 Manycore Systems and S-DSM 340 13.3 Programming Abstractions 341 13.3.1 MapReduce 341 13.3.2 OpenMP 343 13.3.3 Merging Publish and Subscribe with DSM 345 13.4 Memory Consistency Models 347 13.4.1 Sequential Consistency 349 13.4.2 Linearizability or Atomic Consistency 351 13.4.3 Relaxed Consistency Models 352 13.4.3.1 Release Consistency 356 13.4.4 Comparison of Memory Models 357 13.5 DSM Access Algorithms 358 13.5.1 Central Sever Algorithm 359 13.5.2 Migration Algorithm 360 13.5.3 Read Replication Algorithm 361 13.5.4 Full Replication Algorithm 362 13.6 Conclusion 364 Exercises 364 Bibliography 367 14 Distributed Data Management 371 14.1 Distributed Storage Systems 372 14.1.1 Raid 372 14.1.2 Storage Area Networks 372 14.1.3 Cloud Storage 373 14.2 Distributed File Systems 375 14.3 Distributed Index 376 14.4 NoSQL Databases 377 14.4.1 Key-Value and Document Databases 378 14.4.1.1 MapReduce Algorithm 380 14.4.2 Wide Column Databases 381 14.4.3 Graph Databases 382 14.4.3.1 Pregel Algorithm 384 14.5 Distributed Data Analytics 386 14.5.1 Distributed Clustering Algorithms 388 14.5.1.1 Distributed K-Means Clustering Algorithm 388 14.5.2 Stream Clustering 391 14.5.2.1 BIRCH Algorithm 392 14.6 Conclusion 393 Exercises 394 Bibliography 395 15 Distributed Knowledge Management 399 15.1 Distributed Knowledge 400 15.2 Distributed Knowledge Representation 401 15.2.1 Resource Description Framework (RDF) 401 15.2.2 Web Ontology Language (OWL) 406 15.3 Linked Data 407 15.3.1 Friend of a Friend 407 15.3.2 DBpedia 408 15.4 Querying Distributed Knowledge 409 15.4.1 SPARQL Query Language 410 15.4.2 SPARQL Query Semantics 411 15.4.3 SPARQL Query Processing 413 15.4.4 Distributed SPARQL Query Processing 414 15.4.5 Federated and Peer-to-Peer SPARQL Query Processing 416 15.5 Data Integration in Distributed Sensor Networks 421 15.5.1 Semantic Data Integration 422 15.5.2 Data Integration in Constrained Systems 424 15.6 Conclusion 427 Exercises 428 Bibliography 429 16 Distributed Intelligence 433 16.1 Agents and Multi-Agent Systems 434 16.1.1 Agent Embodiment 436 16.1.2 Mobile Agents 436 16.1.3 Multi-Agent Systems 437 16.2 Communication in Agent-Based Systems 438 16.2.1 Agent Communication Protocols 439 16.2.2 Interaction Protocols 440 16.2.2.1 Request Interaction Protocol 441 16.3 Agent Middleware 441 16.3.1 FIPA Reference Model 442 16.3.2 FIPA Compliant Middleware 443 16.3.2.1 JADE: Java Agent Development Environment 443 16.3.2.2 MobileC 443 16.3.3 Agent Migration 444 16.4 Agent Coordination 445 16.4.1 Planning 447 16.4.1.1 Distributed Planning Paradigms 447 16.4.1.2 Distributed Plan Representation and Execution 448 16.4.2 Task Allocation 450 16.4.2.1 Contract-Net Protocol 450 16.4.2.2 Allocation of Multiple Tasks 452 16.4.3 Coordinating Through the Environment 453 16.4.3.1 Construct-Ant-Solution 455 16.4.3.2 Update-Pheromone 456 16.4.4 Coordination Without Communication 456 16.5 Conclusion 456 Exercises 457 Bibliography 459 17 Distributed Ledger 461 17.1 Cryptographic Techniques 462 17.2 Distributed Ledger Systems 464 17.2.1 Properties of Distributed Ledger Systems 465 17.2.2 A Framework for Distributed Ledger Systems 466 17.3 Blockchain 467 17.3.1 Distributed Consensus in Blockchain 468 17.3.2 Forking 470 17.3.3 Distributed Asset Tracking 471 17.3.4 Byzantine Fault Tolerance and Proof of Work 472 17.4 Other Techniques for Distributed Consensus 473 17.4.1 Alternative Proofs 473 17.4.2 Non-linear Data Structures 474 17.4.2.1 Tangle 474 17.4.2.2 Hashgraph 476 17.5 Scripts and Smart Contracts 480 17.6 Distributed Ledgers for Cyber-Physical Systems 483 17.6.1 Layered Architecture 484 17.6.2 Smart Contract in Cyber-Physical Systems 486 17.7 Conclusion 486 Exercises 487 Bibliography 488 18 Case Study 491 18.1 Collaborative E-Learning Systems 492 18.2 P2P E-Learning System 493 18.2.1 Web Conferencing Versus P2P-IPS 495 18.3 P2P Shared Whiteboard 497 18.3.1 Repainting Shared Whiteboard 497 18.3.2 Consistency of Board View at Peers 498 18.4 P2P Live Streaming 500 18.4.1 Peer Joining 500 18.4.2 Peer Leaving 503 18.4.3 Handling “Ask Doubt” 504 18.5 P2P-IPS for Stored Contents 504 18.5.1 De Bruijn Graphs for DHT Implementation 505 18.5.2 Node Information Structure 507 18.5.2.1 Join Example 510 18.5.3 Leaving of Peers 510 18.6 Searching, Sharing, and Indexing 511 18.6.1 Pre-processing of Files 511 18.6.2 File Indexing 512 18.6.3 File Lookup and Download 512 18.7 Annotations and Discussion Forum 513 18.7.1 Annotation Format 513 18.7.2 Storing Annotations 514 18.7.3 Audio and Video Annotation 514 18.7.4 PDF Annotation 514 18.7.5 Posts, Comments, and Announcements 514 18.7.6 Synchronization of Posts and Comments 515 18.7.6.1 Epidemic Dissemination 516 18.7.6.2 Reconciliation 516 18.8 Simulation Results 516 18.8.1 Live Streaming and Shared Whiteboard 517 18.8.2 De Bruijn Overlay 518 18.9 Conclusion 520 Bibliography 521 Index 525

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Book SynopsisAI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation <Table of ContentsAuthor Biographies xiii Preface xv Acknowledgments xvii Acronyms xix 1 Introduction 1 1.1 Introduction 1 1.2 Organization of the Book 3 1.3 Conclusion 6 References 6 2 When Network and Security Management Meets AI and Machine Learning 9 2.1 Introduction 9 2.2 Architecture of Machine Learning-Empowered Network and Security Management 10 2.3 Supervised Learning 12 2.3.1 Classification 12 2.3.2 Regression 15 2.4 Semisupervised and Unsupervised Learning 15 2.4.1 Clustering 17 2.4.2 Dimension Reduction 17 2.4.3 Semisupervised Learning 18 2.5 Reinforcement Learning 18 2.5.1 Policy-Based 21 2.5.2 Value-Based 22 2.6 Industry Products on Network and Security Management 24 2.6.1 Network Management 24 2.6.1.1 Cisco DNA Center 24 2.6.1.2 Sophie 25 2.6.1.3 Juniper EX4400 Switch 25 2.6.1.4 Juniper SRX Series Services Gateway 25 2.6.1.5 H3C SeerAnalyzer 26 2.6.2 Security Management 27 2.6.2.1 SIEM, IBM QRadar Advisor with Watson 27 2.6.2.2 FortiSandbox 27 2.6.2.3 FortiSIEM 28 2.6.2.4 FortiEDR 28 2.6.2.5 FortiClient 29 2.6.2.6 H3C SecCenter CSAP 29 2.7 Standards on Network and Security Management 29 2.7.1 Network Management 29 2.7.1.1 Cognitive Network Management 30 2.7.1.2 End-to-End 5G and Beyond 30 2.7.1.3 Software-Defined Radio Access Network 32 2.7.1.4 Architectural Framework for ML in Future Networks 32 2.7.2 Security Management 33 2.7.2.1 Securing AI 33 2.8 Projects on Network and Security Management 34 2.8.1 Poseidon 34 2.8.2 NetworkML 35 2.8.3 Credential-Digger 36 2.8.4 Adversarial Robustness Toolbox 37 2.9 Proof-of-Concepts on Network and Security Management 38 2.9.1 Classification 38 2.9.1.1 Phishing URL Classification 38 2.9.1.2 Intrusion Detection 39 2.9.2 Active Learning 39 2.9.3 Concept Drift Detection 40 2.10 Conclusion 41 References 42 3 Learning Network Intents for Autonomous Network Management 49 3.1 Introduction 49 3.2 Motivation 52 3.3 The Hierarchical Representation and Learning Framework for Intention Symbols Inference 53 3.3.1 Symbolic Semantic Learning (SSL) 53 3.3.1.1 Connectivity Intention 55 3.3.1.2 Deadlock Free Intention 56 3.3.1.3 Performance Intention 57 3.3.1.4 Discussion 57 3.3.2 Symbolic Structure Inferring (SSI) 57 3.4 Experiments 59 3.4.1 Datasets 59 3.4.2 Experiments on Symbolic Semantic Learning 60 3.4.3 Experiments on Symbolic Structure Inferring 62 3.4.4 Experiments on Symbolic Structure Transferring 64 3.5 Conclusion 66 References 66 4 Virtual Network Embedding via Hierarchical Reinforcement Learning 69 4.1 Introduction 69 4.2 Motivation 70 4.3 Preliminaries and Notations 72 4.3.1 Virtual Network Embedding 72 4.3.1.1 Substrate Network and Virtual Network 72 4.3.1.2 The VNE Problem 72 4.3.1.3 Evaluation Metrics 73 4.3.2 Reinforcement Learning 74 4.3.3 Hierarchical Reinforcement Learning 75 4.4 The Framework of VNE-HRL 75 4.4.1 Overview 75 4.4.2 The High-level Agent 77 4.4.2.1 State Encoder for HEA 77 4.4.2.2 Estimated Long-term Cumulative Reward 78 4.4.2.3 Short-term High-level Reward 78 4.4.3 The Low-level Agent 78 4.4.3.1 State Encoder for LEA 79 4.4.3.2 Estimated Long-term Cumulative Reward 79 4.4.3.3 Short-term Low-level Reward 80 4.4.4 The Training Method 80 4.5 Case Study 80 4.5.1 Experiment Setup 80 4.5.2 Comparison Methods 81 4.5.3 Evaluation Results 81 4.5.3.1 Performance Over Time 81 4.5.3.2 Performance of Various VNRs with Diverse Resource Requirements 82 4.6 Related Work 84 4.6.1 Traditional Methods 84 4.6.2 ML-based Algorithms 84 4.7 Conclusion 85 References 85 5 Concept Drift Detection for Network Traffic Classification 91 5.1 Related Concepts of Machine Learning in Data Stream Processing 91 5.1.1 Assumptions and Limitations 91 5.1.1.1 Availability of Learning Examples 91 5.1.1.2 Availability of the Model 92 5.1.1.3 Concept to be Learned 92 5.1.2 Concept Drift and Its Solution 92 5.2 Using an Active Approach to Solve Concept Drift in the Intrusion Detection Field 94 5.2.1 Application Background 94 5.2.2 System Workflow 95 5.3 Concept Drift Detector Based on CVAE 96 5.3.1 CVAE-based Drift Indicator 96 5.3.2 Drift Analyzer 97 5.3.3 The Performance of CVAE-based Concept Drift Detector 98 5.3.3.1 Comparison Drift Detectors 99 5.3.3.2 Experiment Settings 99 5.4 Deployment and Experiment in Real Networks 101 5.4.1 Data Collection and Feature Extraction 101 5.4.2 Data Analysis and Parameter Setting 103 5.4.3 Result Analysis 103 5.5 Future Research Challenges and Open Issues 105 5.5.1 Adaptive Threshold m 105 5.5.2 Computational Cost of Drift Detectors 105 5.5.3 Active Learning 105 5.6 Conclusion 105 References 106 6 Online Encrypted Traffic Classification Based on Lightweight Neural Networks 109 6.1 Introduction 109 6.2 Motivation 109 6.3 Preliminaries 110 6.3.1 Problem Definition 110 6.3.2 Packet Interaction 111 6.4 The Proposed Lightweight Model 111 6.4.1 Preprocessing 112 6.4.2 Feature Extraction 112 6.4.2.1 Embedding 112 6.4.2.2 Attention Encoder 113 6.4.2.3 Fully Connected Layer 115 6.5 Case Study 115 6.5.1 Evaluation Metrics 115 6.5.2 Baselines 116 6.5.3 Datasets 117 6.5.4 Evaluation on Datasets 118 6.5.4.1 Evaluation on Dataset A 118 6.5.4.2 Evaluation on Dataset B 120 6.6 Related Work 121 6.6.1 Encrypted Traffic Classification 122 6.6.2 Packet-Based Methods 122 6.6.3 Flow-Based Methods 122 6.6.3.1 Traditional Machine Learning-Based Methods 123 6.6.3.2 Deep Learning-Based Methods 124 6.7 Conclusion 124 References 125 7 Context-Aware Learning for Robust Anomaly Detection 129 7.1 Introduction 129 7.2 Pronouns 133 7.3 The Proposed Method – AllRobust 135 7.3.1 Problem Statement 135 7.3.2 Log Parsing 135 7.3.3 Log Vectorization 138 7.3.4 Anomaly Detection 142 7.3.4.1 Implementation of SSL 143 7.4 Experiments 145 7.4.1 Datasets 145 7.4.1.1 HDFS Dataset 145 7.4.1.2 BGL Dataset 146 7.4.1.3 Thunderbird Dataset 146 7.4.2 Model Evaluation Indicators 147 7.4.3 Supervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 148 7.4.3.1 Data Preprocessing 148 7.4.3.2 Hyperparameters and Environmental Settings 149 7.4.3.3 Training on Multiclass Imbalanced Log Data 149 7.4.3.4 Training on Binary Imbalanced Log Data 150 7.4.4 Semisupervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 152 7.4.4.1 The Methods of Enhancing Log Data 152 7.4.4.2 Anomaly Detection with a Single Log 153 7.4.4.3 Anomaly Detection with a Log-based Sequence 156 7.5 Discussion 157 7.6 Conclusion 158 References 159 8 Anomaly Classification with Unknown, Imbalanced and Few Labeled Log Data 165 8.1 Introduction 165 8.2 Examples 167 8.2.1 The Feature Extraction of Log Analysis 167 8.2.1.1 Statistical Feature Extraction 168 8.2.1.2 Semantic Feature Extraction 170 8.2.2 Few-Shot Problem 170 8.3 Methodology 172 8.3.1 Data Preprocessing 172 8.3.1.1 Log Parsing 172 8.3.1.2 Log Enhancement 173 8.3.1.3 Log Vectorization 174 8.3.2 The Architecture of OpenLog 174 8.3.2.1 Encoder Module 174 8.3.2.2 Prototypical Module 177 8.3.2.3 Relation Module 178 8.3.3 Training Procedure 179 8.3.4 Objective Function 180 8.4 Experimental Results and Analysis 180 8.4.1 Experimental Design 181 8.4.1.1 Baseline 181 8.4.1.2 Evaluation Metrics 181 8.4.2 Datasets 183 8.4.2.1 Data Processing 184 8.4.3 Experiments on the Unknown Class Data 185 8.4.4 Experiments on the Imbalanced Data 188 8.4.5 Experiments on the Few-shot Data 188 8.5 Discussion 190 8.6 Conclusion 191 References 192 9 Zero Trust Networks 199 9.1 Introduction to Zero-Trust Networks 199 9.1.1 Background 199 9.1.2 Zero-Trust Networks 200 9.2 Zero-Trust Network Solutions 201 9.2.1 Zero-Trust Networks Based on Access Proxy 201 9.2.2 Zero Trust Networks Based on SDP 203 9.2.3 Zero-Trust Networks Based on Micro-Segmentation 204 9.3 Machine Learning Powered Zero Trust Networks 206 9.3.1 Information Fusion 208 9.3.2 Decision Making 210 9.4 Conclusion 212 References 212 10 Intelligent Network Management and Operation Systems 215 10.1 Introduction 215 10.2 Traditional Operation and Maintenance Systems 215 10.2.1 Development of Operation and Maintenance Systems 215 10.2.1.1 Manual Operation and Maintenance 216 10.2.1.2 Tool-Based Operation and Maintenance 216 10.2.1.3 Platform Operation and Maintenance 217 10.2.1.4 DevOps 217 10.2.1.5 AIOps 218 10.2.2 Open-Source Operation and Maintenance Systems 218 10.2.2.1 Nagios 219 10.2.2.2 Zabbix 221 10.2.2.3 Prometheus 223 10.2.3 Summary 224 10.3 Security Operation and Maintenance 225 10.3.1 Introduction 225 10.3.2 Open-Source Security Tools 226 10.3.2.1 Access Control 226 10.3.2.2 Security Audit and Intrusion Detection 227 10.3.2.3 Penetration Testing 227 10.3.2.4 Vulnerability Scanning 231 10.3.2.5 CI/CD Security 234 10.3.2.6 Deception 234 10.3.2.7 Data Security 234 10.3.3 Summary 237 10.4 AIOps 238 10.4.1 Introduction 238 10.4.2 Open-Source AIOps and Algorithms 239 10.4.2.1 Research Progress of Anomaly Detection 239 10.4.2.2 Metis 242 10.4.2.3 UAVStack 244 10.4.2.4 Skyline 244 10.4.3 Summary 247 10.5 Machine Learning-Based Network Security Monitoring and Management Systems 248 10.5.1 Architecture 248 10.5.2 Physical Facility Layer 248 10.5.3 Virtual Resource Layer 249 10.5.4 Orchestrate Layer 250 10.5.5 Policy Layer 250 10.5.6 Semantic Description Layer 251 10.5.7 Application Layer 251 10.5.8 Center for Intelligent Analytics of Big Data 251 10.5.9 Programmable Measurement and Auditing 252 10.5.10 Overall Process 252 10.5.11 Summary 253 10.6 Conclusion 253 References 254 11 Conclusions, and Research Challenges and Open Issues 257 11.1 Conclusions 257 11.2 Research Challenges and Open Issues 258 11.2.1 Autonomous Networks 258 11.2.2 Reinforcement Learning Powered Solutions 259 11.2.3 Traffic Classification 259 11.2.4 Anomaly Detection 260 11.2.5 Zero-Trust Networks 261 References 262 Index 263

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Book SynopsisTable of ContentsPreface xv 1 Machine Learning–Assisted Remote Patient Monitoring with Data Analytics 1Vinutha D. C., Kavyashree and G. T. Raju 1.1 Introduction 2 1.1.1 Traditional Patient Monitoring System 2 1.1.2 Remote Monitoring System 3 1.1.3 Challenges in RPM 4 1.2 Literature Survey 5 1.2.1 Machine Learning Approaches in Patient Monitoring 7 1.3 Machine Learning in RPM 8 1.3.1 Support Vector Machine 9 1.3.2 Decision Tree 10 1.3.3 Random Forest 11 1.3.4 Logistic Regression 11 1.3.5 Genetic Algorithm 12 1.3.6 Simple Linear Regression 12 1.3.7 KNN Algorithm 13 1.3.8 Naive Bayes Algorithm 14 1.4 System Architecture 15 1.4.1 Data Collection 16 1.4.2 Data Pre-Processing 17 1.4.3 Apply Machine Learning Algorithm and Prediction 18 1.5 Results 21 1.6 Future Enhancement 23 1.7 Conclusion 24 References 24 2 A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy 27Priyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain 2.1 Introduction 28 2.2 Diabetic Retinopathy 28 2.2.1 Features of DR 28 2.2.2 Stages of DR 29 2.3 Overview of DL Models 31 2.3.1 Convolution Neural Network 31 2.3.2 Autoencoders 32 2.3.3 Boltzmann Machine and Deep Belief Network 32 2.4 Data Set 33 2.5 Performance Metrics 34 2.6 Literature Survey 36 2.6.1 Segmentation of Blood Vessels 36 2.6.2 Optic Disc Feature 49 2.6.3 Lesion Detections 50 2.6.3.1 Exudate Detection 50 2.6.3.2 MA and HM 51 2.6.4 DR Classification 51 2.7 Discussion and Future Directions 52 2.8 Conclusion 53 References 53 3 A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment 59Dipesh Kumar, Nirupama Mandal and Yugal Kumar 3.1 Introduction 60 3.1.1 Contribution 61 3.2 Motivation 62 3.3 Related Works 62 3.4 Challenges 64 3.5 Proposed Work 64 3.6 Proposed Algorithm for Encryption 66 3.6.1 Demonstration of Encryption Algorithm 66 3.6.1.1 When the Number of Columns Selected in the Table is Even 66 3.6.1.2 When the Number of Columns Selected in the Table is Odd 69 3.6.2 Flowchart for Encryption 72 3.7 Algorithm for Decryption 73 3.7.1 Demonstration of Decryption Algorithm 73 3.7.1.1 When the Number of Columns Selected in the Table is Even 73 3.7.1.2 When the Number of Columns Selected in the Table is Odd 75 3.7.2 Flowchart of Decryption Algorithm 78 3.8 Experiment and Result 78 3.9 Conclusion 80 References 80 4 Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics 85Supriya M., Murugan K., Shanmugaraja T. and Venkatesh T. 4.1 Introduction 86 4.2 Materials and Methods 87 4.2.1 Clinical Setting and Teledermatology Workflow 87 4.2.2 Study Design, Data Collection, and Analysis 87 4.3 Proposed System 88 4.3.1 Teledermatology in an Underresourced Clinic 88 4.3.2 Teledermatology Consultations from Uninsured Patients 89 4.3.3 Teledermatology for Patients Lacking Access to Dermatologists 90 4.3.4 Teledermatologist Management from Nonspecialists 92 4.3.5 Segment Factors of Referring PCPs and Their Patients 93 4.3.6 Teledermatology Operational Considerations 94 4.3.7 Instruction of PCPs 94 4.4 Challenges 95 4.5 Results and Discussion 95 4.5.1 Challenges of Referring to Teledermatology Services 96 References 98 5 Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology 101Shanmugaraja T., Venkatesh T., Supriya M. and Murugan K. 5.1 Introduction 102 5.2 Materials and Methods 102 5.3 Framework Elements 102 5.3.1 Eye Tracker Camera 102 5.3.2 Test Construction 103 5.3.3 Web Camera 106 5.3.4 Camera for Eye Tracking 106 5.4 Proposed System 106 5.4.1 Camera for Tracking Eye 106 5.4.2 Web Camera 108 5.4.3 Scoring 108 5.4.4 Eye Tracking Camera 108 5.4.5 Web Camera Human-Coded Scoring 108 5.5 Subjects 109 5.5.1 Characteristics of Subject 109 5.6 Methodology 110 5.6.1 Analysis of Data 110 5.7 Results 110 5.8 Discussion 112 5.9 Conclusion 114 References 115 6 Fuzzy-Based Patient Health Monitoring System 117Venkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi 6.1 Introduction 118 6.1.1 General Problem 119 6.1.2 Existing Patient Monitoring and Diagnosis Systems 119 6.1.3 Fuzzy Logic Systems 120 6.2 System Design 122 6.2.1 Hardware Requirements 122 6.2.1.1 Functional Requirements 123 6.2.1.2 Nonfunctional Specifications 125 6.3 Software Architecture 125 6.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API) 126 6.3.2 Flowchart—API 128 6.3.3 Foreign Tag IDs 129 6.3.4 Database Manager 130 6.3.5 Database Designing 130 6.3.6 The Fuzzy Logic System 131 6.3.6.1 Introduction to Fuzzy Logic 131 6.3.6.2 The Modified Prior Alerting Score (MPAS) 132 6.3.6.3 Structure of the Fuzzy Logic System 134 6.3.7 Designing a System in Fuzzy 135 6.3.7.1 Input Variables 135 6.3.7.2 The Output Variable 138 6.4 Results and Discussion 140 6.4.1 Hardware Sensors Validation 140 6.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine 141 6.4.3 Normal Group (NRM) 146 6.4.4 Low Risk Group 146 6.4.5 High Risk Group (HRG) 153 6.5 Conclusions and Future Work 155 6.5.1 Summary and Concluding Remarks 155 6.5.2 Future Directions 155 References 155 7 Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography 159C. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S. 7.1 Introduction 160 7.2 Related Work 162 7.2.1 Traditional Approach 162 7.2.2 Deep Learning–Based Approach 163 7.3 Materials and Methods 163 7.3.1 Data Set and Data Pre-Processing 163 7.3.2 Proposed Model 165 7.4 Experiment and Result 171 7.4.1 Experiment Setup 171 7.4.2 Comparison with Other Models 173 7.5 Results 174 7.6 Conclusion 175 References 176 8 An Efficient IoT Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms 179Shanthi S., Nidhya R., Uma Perumal and Manish Kumar 8.1 Introduction 180 8.2 Literature Survey 182 8.3 Machine Learning Algorithms 183 8.4 Problem Statement 184 8.5 Proposed Work 185 8.5.1 Data Set Description 185 8.5.2 Collection of Values Through Sensor Nodes 186 8.5.3 Storage of Data in Cloud 187 8.5.4 Prediction with Machine Learning Algorithms 188 8.5.4.1 Data Cleaning and Preparation 188 8.5.4.2 Data Splitting 189 8.5.4.3 Training and Testing 189 8.5.5 Machine Learning Algorithms 189 8.5.5.1 Naive Bayes Algorithm 189 8.5.5.2 Decision Tree Algorithm 190 8.5.5.3 K-Neighbors Classifier 191 8.5.5.4 Logistic Regression 192 8.6 Performance Analysis and Evaluation 192 8.7 Conclusion 197 References 197 9 BABW: Biometric-Based Authentication Using DWT and FFNN 201R. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai 9.1 Introduction 202 9.2 Literature Survey 203 9.3 BABW: Biometric Authentication Using Brain Waves 208 9.4 Results and Discussion 211 9.5 Conclusion 215 References 216 10 Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review 221Pavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S. 10.1 Introduction 222 10.2 Autism Screening Methods 223 10.2.1 Autism Screening Instrument for Educational Planning—3rd Version 224 10.2.2 Quantitative Checklist for Autism in Toddlers 224 10.2.3 Autism Behavior Checklist 224 10.2.4 Developmental Behavior Checklist-Early Screen 225 10.2.5 Childhood Autism Rating Scale Version 2 225 10.2.6 Autism Spectrum Screening Questionnaire (ASSQ) 226 10.2.7 Early Screening for Autistic Traits 226 10.2.8 Autism Spectrum Quotient 226 10.2.9 Social Communication Questionnaire 227 10.2.10 Child Behavior Check List 227 10.2.11 Indian Scale for Assessment of Autism 227 10.3 Machine Learning in ASD Screening and Diagnosis 228 10.4 DL in ASD Diagnosis 238 10.5 Conclusion 242 References 242 11 Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering 249R. Gowri and R. Rathipriya 11.1 Introduction 249 11.2 Literature Study 250 11.3 Materials and Methods 253 11.3.1 Biological Terminologies 253 11.3.2 Functional Coherence 256 11.3.3 Biological Significances 257 11.3.4 Existing Approach: MR-CoC 257 11.4 Proposed Approach: MR-CoCmulti 258 11.4.1 Biological Score Measures for DTM 259 11.4.2 Multifunctional Score-Based Co-Clustering Approach 259 11.5 Experimental Analysis 264 11.5.1 Experimental Results 265 11.6 Discussion 280 11.7 Conclusion 280 Acknowledgment 281 References 281 12 The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth 285Jothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar 12.1 Introduction 286 12.1.1 Objective 287 12.1.2 Description and Goals 287 12.1.2.1 Data Exploration 288 12.1.2.2 Data Pre-Processing 288 12.1.2.3 Feature Scaling 288 12.1.2.4 Model Selection and Evaluation 288 12.2 Literature Review 289 12.3 Architecture Design and Implementation 304 12.4 Results and Discussion 310 12.5 Conclusion 312 12.6 Future Work 313 References 314 13 Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning 317Mohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi 13.1 Introduction 318 13.1.1 Patient Monitoring in Healthcare System 318 13.2 Literature Survey 321 13.3 Problem Statement 322 13.4 Machine Learning 322 13.4.1 Introduction 322 13.4.2 Cloud Computing 324 13.4.3 Design and Architecture 325 13.5 Proposed System 326 13.6 Results and Discussions 331 13.7 Privacy and Security Challenges 333 13.8 Conclusions and Future Enhancement 334 References 335 14 Investigations on Machine Learning Models to Envisage Coronavirus in Patients 339R. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik 14.1 Introduction 340 14.2 Categories of ML Algorithms in Healthcare 341 14.3 Why ML to Fight COVID-19? Tools and Techniques 343 14.4 Highlights of ML Algorithms Under Consideration 344 14.5 Experimentation and Investigation 349 14.6 Comparative Analysis of the Algorithms 353 14.7 Scope of Enhancement for Better Investigation 354 References 356 15 Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging 359G. Karthick and N.S. Nithya 15.1 Emerging Trends and Challenges in Healthcare Informatics 360 15.1.1 Advanced Technologies in Healthcare Informatics 360 15.1.2 Intelligent Smart Healthcare Devices Using IoT With DL 361 15.1.3 Cyber Security in Healthcare Informatics 362 15.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics 363 15.2 Performance Analysis of Medical Image Compression Using Wavelet Functions 364 15.2.1 Introduction 364 15.2.2 Materials and Methods 366 15.2.3 Wavelet Basis Functions 367 15.2.3.1 Haar Wavelet 367 15.2.3.2 db Wavelet 368 15.2.3.3 bior Wavelet 368 15.2.3.4 rbio Wavelet 368 15.2.3.5 Symlets Wavelet 369 15.2.3.6 coif Wavelet 369 15.2.3.7 dmey Wavelet 369 15.2.3.8 fk Wavelet 369 15.2.4 Compression Methods 370 15.2.4.1 Embedded Zero-Trees of Wavelet Transform 370 15.2.4.2 Set Partitioning in Hierarchical Trees 370 15.2.4.3 Adaptively Scanned Wavelet Difference Reduction 370 15.2.4.4 Coefficient Thresholding 371 15.3 Results and Discussion 371 15.3.1 Mean Square Error 371 15.3.2 Peak Signal to Noise Ratio 371 15.4 Conclusion 380 15.4.1 Summary 380 References 380 Index 383

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Book SynopsisTable of ContentsAbout the Author xvii Preface xix 1 Data Communications and Networks 1 1.1 Introduction 1 1.2 OSI Model 3 1.2.1 Layer 1 – Physical Layer 5 1.2.2 Layer 2 – Data Link Layer 5 1.2.2.1 Addressing 5 1.2.2.2 Framing 5 1.2.2.3 Error Control 6 1.2.2.4 Flow Control 6 1.2.2.5 Access Control 7 1.2.3 Layer 3 – Network Layer 7 1.2.4 Layer 4 – Transport Layer 7 1.2.4.1 Port Addressing 8 1.2.4.2 End-to-end Error Control 8 1.2.4.3 End-to-end Flow Control 8 1.2.4.4 Connection Control 8 1.2.4.5 Congestion Control 8 1.2.5 Layer 5 – Session Layer 9 1.2.6 Layer 6 – Presentation Layer 9 1.2.7 Layer 7 – Application Layer 9 1.3 Header Encapsulation 9 1.4 Layer 2 – Ethernet 10 1.4.1 Framing 11 1.4.2 Addressing 11 1.4.3 Error Control 11 1.4.4 Flow Control 12 1.4.5 Access Control 12 1.5 Layer 3 – IP 12 1.5.1 IPV4 and IPV6 headers 15 1.5.2 Improving IPV4 Address Assignments 17 1.6 Layer 4 – TCP and UDP 19 1.6.1 TCP Header 20 1.6.2 TCP Functionalities 22 1.6.2.1 Process-to-process Communication 22 1.6.2.2 Connection Control 22 1.6.2.3 Flow Control 22 1.6.2.4 Error Control 23 1.6.2.5 Congestion Control 24 1.6.3 UDP 24 1.7 TCP/IP Networking Model 25 1.8 Internetworking Devices 25 1.8.1 VLAN 27 1.8.2 Quality of Service (QoS) 28 1.9 Summary 29 References 30 Exercises 30 Advanced Exercises 32 2 Introduction to IoT 35 2.1 Introduction 35 2.2 IoT Traffic Model 36 2.3 IoT Connectivity 37 2.4 IoT Verticals, Use Cases, and Applications 39 2.5 IoT Value Chain 41 2.6 Examples of IoT Use Cases and Applications 42 2.6.1 IoT-based Structural Health Monitoring System 42 2.6.2 IoT-based Electric Meter 44 2.6.3 IoT-basedWaste Management System 44 2.6.4 IoT-based Earthquake Detection 45 2.6.5 IoT-based Car Software Update 45 2.6.6 IoT-based Mountain Climbing Information System 46 2.6.7 IoT-based Agriculture – Pest Management 46 2.6.8 IoT-basedWearable in Sports 47 2.6.9 IoT-based Healthcare System 47 2.6.10 IoT-based Augmented Reality (AR) System 48 2.6.11 IoT-based Food Supply Chain 49 2.6.12 Smart Grid System 49 2.7 IoT Project Implementation 51 2.8 IoT Standards 52 2.9 Summary 52 References 53 Exercises 53 Advanced Exercises 54 3 IoT Architecture 57 3.1 Introduction 57 3.2 Factors Affecting an IoT Architectural Model 58 3.3 IoT Architectural Model 59 3.4 IoT WF Architectural Model 59 3.5 Data Center and Cloud 63 3.6 Computing (Cloud, Fog, and Edge) 66 3.6.1 Cloud Computing 66 3.6.2 Fog Computing 67 3.6.3 Edge Computing 68 3.7 Summary 69 References 69 Exercises 69 Advanced Exercises 70 4 IoT Sensors 73 4.1 Introduction 73 4.2 Sensor and Its Performance Metrics 74 4.2.1 Static Performance Metrics 74 4.2.2 Dynamic Performance Metrics 76 4.2.3 Sensor Selection 77 4.3 Smart Sensors 80 4.4 MEMS 81 4.5 Sensor Fusion 83 4.5.1 Improving the Quality and Accuracy of a Sensor 83 4.5.2 Improving the Reliability of a Sensor 83 4.5.3 Improving the Capability of a Sensor 84 4.5.4 Measuring a Different Physical Quantity 84 4.6 Self-calibration 84 4.7 Sensors of the Future 85 4.8 Summary 85 References 86 Exercises 86 Advanced Exercises 87 5 IoT Wired Connectivity 89 5.1 Introduction 89 5.2 Ethernet 90 5.2.1 Power over Ethernet (PoE) 91 5.3 Ethernet TSN 92 5.3.1 Challenges of Connectivity for Industrial IoT 92 5.3.2 Ethernet TSN Features and Key Technologies 93 5.3.2.1 Time Synchronization 93 5.3.2.2 Bandwidth and QoS Reservation 94 5.3.2.3 Redundant Transmission 94 5.3.2.4 Traffic Shaping and Scheduling 94 5.3.2.5 Latency Minimization 95 5.3.3 A Simple Example 96 5.3.4 Ethernet TSN Substandards 97 5.4 Power Line Communications (PLCs) 98 5.4.1 PLC for Smart Grid 100 5.5 Summary 103 References 103 Exercises 104 Advanced Exercises 105 6 Unlicensed-band Wireless IoT 107 6.1 Introduction 107 6.2 Zigbee Wireless Network 108 6.3 BLE Wireless Network 111 6.3.1 Bluetooth 5 114 6.3.2 Bluetooth Mesh 115 6.4 WiFiWireless Network 115 6.4.1 WiFi 6 116 6.4.2 WiFi HaLow 117 6.5 LoRaWAN Wireless Wide Area Network 118 6.6 Summary 121 References 121 Exercises 122 Advanced Exercises 124 7 Cellular IoT Technologies 125 7.1 Introduction 125 7.2 EC-GSM-IoT 125 7.3 LTE-based Cellular IoT Technologies 127 7.3.1 LTE-M 127 7.3.1.1 Channel Bandwidth 127 7.3.1.2 Duplexing 128 7.3.1.3 Data Rate and Latency 129 7.3.1.4 Power Class 131 7.3.1.5 Coverage 132 7.3.1.6 Mobility 133 7.3.2 NB-IoT 133 7.3.2.1 Channel Bandwidth and Duplexing 134 7.3.2.2 Data Rate and Latency 134 7.3.2.3 Power Classes 135 7.3.2.4 Coverage 135 7.3.2.5 Mobility 135 7.4 Practical Use Cases 135 7.5 CIoT Frequency Bands 137 7.6 Certification 140 7.7 CIoT Modules 141 7.8 AT Commands 143 7.9 Summary 144 References 145 Exercises 146 Advanced Exercises 147 8 CIoT Features 151 8.1 Low-power Consumption Schemes 153 8.1.1 Introduction 153 8.1.2 Power Saving Techniques in 3GPP Release 13 153 8.1.3 Power Saving Techniques in 3GPP Release 14 158 8.1.4 Power Saving Techniques in 3GPP Release 15 158 8.1.4.1 Wake Up Signal 158 8.1.5 Power Consumption for Various Use Cases 159 8.1.6 Summary 162 References 163 Exercises 163 Advanced Exercises 164 8.2 Uplink Access 167 8.2.1 Introduction 167 8.2.2 Random Access Process 168 8.2.2.1 Random Access Dependency to the Coverage Level 170 8.2.2.2 Access Barring (AB) 170 8.2.2.3 Preamble Formats 171 8.2.3 RA Advancements 172 8.2.3.1 Early Data Transmission 173 8.2.3.2 Preconfigured Uplink Resources 173 8.2.4 Summary 174 References 175 Exercises 175 Advanced Exercises 176 8.3 Positioning 177 8.3.1 Introduction 177 8.3.2 LTE Positioning 178 8.3.2.1 CID 179 8.3.2.2 ECID 179 8.3.2.3 Observed Time Difference of Arrival (OTDOA) 180 8.3.2.3.1 Basic OTDOA Navigation Equations 181 8.3.2.3.2 Positioning Reference Signals (PRSs) 182 8.3.3 Positioning Architecture for LTE-IoT 183 8.3.4 RSTD Measurement Performance 184 8.3.5 PRS Signals 185 8.3.5.1 LTE PRS Signals 185 8.3.5.2 LTE-M PRS Signals 186 8.3.5.3 NB-IoT PRS Signals 186 8.3.6 RSTD Error Sources 187 8.3.7 Summary 188 References 188 Exercises 189 Advanced Exercises 189 8.4 Mobility 191 8.4.1 Introduction 191 8.4.2 Mobility 192 8.4.2.1 Cell Selection 192 8.4.2.2 Cell Reselection 192 8.4.2.3 Signal Measurements Used for Mobility 193 8.4.2.4 Idle Mode Versus Connected Mode Mobility 194 8.4.2.5 Mobility Architecture 195 8.4.2.6 Intra-Frequency vs. Inter-Frequency Mobility 196 8.4.2.7 General Idea about TAU Strategies 197 8.4.2.8 General Idea about Paging Strategies 198 8.4.2.9 TAU and Paging Optimization 198 8.4.2.10 Doppler Effect 198 8.4.3 NB-IoT Mobility 199 8.4.4 LTE-M Mobility 199 8.4.5 Summary 199 References 200 Exercises 201 Advanced Exercises 202 9 IoT Data Communication Protocols 203 9.1 Introduction 203 9.2 HyperText Transfer Protocol (HTTP) 204 9.3 Message Queue Telemetry Transport (MQTT) Protocol 206 9.3.1 MQTT Connections 208 9.3.2 Security of MQTT Protocol 209 9.3.3 MQTT Last Value Queue (LVQ) 210 9.3.4 MQTT LastWill and Testament (LWT) 211 9.4 Constrained Application Protocol (CoAP) 211 9.4.1 CoAP Messages 212 9.4.2 CoAP Observers 213 9.5 Other IoT Protocols 213 9.6 Summary 214 References 215 Exercises 215 Advanced Exercises 217 10 IoT in 5G Era 219 10.1 Introduction 219 10.2 5G Vision 220 10.3 5G’s Main Application Areas 222 10.4 5G Implementations and Features 223 10.4.1 Standalone and non-standalone 5G Network 223 10.4.2 5G Network Slicing 223 10.4.3 Private 5G Network 225 10.4.4 Network Exposure 226 10.4.5 Fixed Wireless Access 226 10.5 Summary 227 References 228 Exercises 228 Advanced Exercises 229 11 IoT and Analytics 231 11.1 Introduction 231 11.2 Data Pipeline 233 11.3 AI 233 11.4 Machine Learning 234 11.5 Supervised Machine Learning Techniques 236 11.5.1 Classification 236 11.5.1.1 Decision Tree 236 11.5.1.2 Random Forest 241 11.5.1.3 K Nearest Neighbor (KNN) 243 11.5.1.4 Support Vector Machine (SVM) 244 11.5.2 Regression 246 11.6 Unsupervised Machine Learning Techniques 251 11.6.1 Clustering 251 11.6.1.1 K-Means 251 11.7 Deep Learning Techniques 253 11.7.1 Recurrent Neural Networks (RNN) 257 11.7.2 Convolutional Neural Network (CNN) 258 11.8 Summary 260 References 261 Exercises 261 Advanced Exercises 263 12 IoT Security and Privacy 267 12.1 Introduction 267 12.2 IoT Threats 267 12.2.1 Confidentiality 268 12.2.2 Integrity 268 12.2.3 Authentication 268 12.2.4 Non-Repudiation 269 12.2.5 Availability 269 12.3 IoT Vulnerabilities 269 12.3.1 Insufficient Authentication 269 12.3.2 Insecure Ports and Interfaces 270 12.3.3 Lack of a Secure Update Mechanism 270 12.3.4 Insufficient Encryption 270 12.3.5 Insecure Network Connectivity 270 12.3.6 Insecure Mobile Connection 271 12.3.7 Not Utilizing Whitelist 271 12.3.8 Insecure IoT Device Chip Manufacturing 271 12.3.9 Configuration Issues 271 12.3.10 Privacy Issues 272 12.4 IoT Threat Modeling and Risk 272 12.4.1 Threat Modeling for Smart Gas Station 272 12.4.1.1 Identifying the Assets 273 12.4.1.2 Identifying the Message Flow 273 12.4.1.3 Identifying the Threat Types 274 12.4.1.4 Rating Threats and Risk Calculations 275 12.5 IoT Security Regulations 276 12.6 IoT Privacy Concerns and Regulations 277 12.7 IoT Security and Privacy Examples 279 12.7.1 Threat Against Availability – Mirai Bonnet 279 12.7.2 Threat Against Integrity – LockState 279 12.7.3 Threat Against Software Update – Jeep 279 12.7.4 Threat Against Confidentiality – TRENDnetWebcam 280 12.7.5 Threat Against Availability and Integrity – St. Jude Medical’s Implantable Cardiac Devices 280 12.7.6 Threat Against Availability – Cyberattack on the Ukrainian Smart Grid 280 12.7.7 Privacy Concern – DJI 280 12.8 Threat Protection Methods 281 12.8.1 Confidentiality Protection 281 12.8.1.1 Methods Based on Symmetric Key 281 12.8.1.2 Methods Based on Asymmetric Key 285 12.8.2 Integrity Protection 286 12.8.3 Authentication Protection 287 12.8.4 Non-Repudiation Protection 288 12.9 IoT and Blockchain 289 12.9.1 Blockchain Technology 290 12.9.2 A Practical Example of IoT and Blockchain for Smart Grid 292 12.10 Summary 293 References 294 Exercises 294 13 IoT Solution Developments 299 13.1 Introduction 299 13.2 IoT Solution Development Methodology 300 13.3 Further Details on IoT Solution Development 302 13.3.1 Business Case Document 302 13.3.2 Implementation Strategy 302 13.3.3 Detailed Design 303 13.3.4 Building, Configuration, and Testing (BCT) 304 13.3.5 Pilot Implementation 306 13.3.6 Regulation Acceptance 307 13.3.7 Deployment 307 13.3.8 Sustainment 307 13.3.9 Continuous Improvements 307 13.4 Change Management 307 13.5 Summary 308 Reference 309 Exercises 309 Advanced Exercises 310 Practical Assignments 313 Assignment #1: Connecting an IoT Device to the Cloud 313 Assignment #2: Building a Battery-Powered Vision-Based System 314 Assignment #3: Configuring an LTE-M module using AT Commands 315 Assignment #4: Connecting an IoT Device to an MQTT Broker 316 Assignment #5: Connecting an IoT Device to an IoT Gateway Using BLE 318 Assignment #6: Building an IoT-Based Home Automation System 319 Assignment #7: Designing a Smart Toy System 320 Assignment #8: Controlling a Smart Tank System Using LoRaWAN Technology 321 Assignment #9: Building IoT Systems Using Cisco Packet Tracer 323 Assignment #10: Building a Digital Twin in the Cloud 325 References 327 Appendix A Internet Protocol Security (IPSec) 329 Appendix B Transport Layer Security (TLS) 333 Appendix C Satellite IoT 337 Solutions 339 Chapter 1 339 Chapter 2 343 Chapter 3 346 Chapter 4 348 Chapter 5 352 Chapter 6 355 Chapter 7 357 Chapter 8 361 Chapter 9 367 Chapter 10 370 Chapter 11 371 Chapter 12 376 Chapter 13 381 Abbreviations 385 Index 395

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Book SynopsisAdvances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapteTable of ContentsAbout the Editors xix List of Contributors xx Preface xxvi Section I Introduction to AI-Based Regression and Classification 1 1 Introduction to Neural Networks 3 Isha Garg and Kaushik Roy 1.1 Taxonomy 3 1.1.1 Supervised Versus Unsupervised Learning 3 1.1.2 Regression Versus Classification 4 1.1.3 Training, Validation, and Test Sets 4 1.2 Linear Regression 5 1.2.1 Objective Functions 6 1.2.2 Stochastic Gradient Descent 7 1.3 Logistic Classification 9 1.4 Regularization 11 1.5 Neural Networks 13 1.6 Convolutional Neural Networks 16 1.6.1 Convolutional Layers 17 1.6.2 Pooling Layers 18 1.6.3 Highway Connections 19 1.6.4 Recurrent Layers 19 1.7 Conclusion 20 References 20 2 Overview of Recent Advancements in Deep Learning and Artificial Intelligence 23 Vijaykrishnan Narayanan, Yu Cao, Priyadarshini Panda, Nagadastagiri Reddy Challapalle, Xiaocong Du, Youngeun Kim, Gokul Krishnan, Chonghan Lee, Yuhang Li, Jingbo Sun, Yeshwanth Venkatesha, Zhenyu Wang, and Yi Zheng 2.1 Deep Learning 24 2.1.1 Supervised Learning 26 2.1.1.1 Conventional Approaches 26 2.1.1.2 Deep Learning Approaches 29 2.1.2 Unsupervised Learning 35 2.1.2.1 Algorithm 35 2.1.3 Toolbox 37 2.2 Continual Learning 38 2.2.1 Background and Motivation 38 2.2.2 Definitions 38 2.2.3 Algorithm 38 2.2.3.1 Regularization 39 2.2.3.2 Dynamic Network 40 2.2.3.3 Parameter Isolation 40 2.2.4 Performance Evaluation Metric 41 2.2.5 Toolbox 41 2.3 Knowledge Graph Reasoning 42 2.3.1 Background 42 2.3.2 Definitions 42 2.3.3 Database 43 2.3.4 Applications 43 2.3.5 Toolbox 44 2.4 Transfer Learning 44 2.4.1 Background and Motivation 44 2.4.2 Definitions 44 2.4.3 Algorithm 45 2.4.4 Toolbox 46 2.5 Physics-Inspired Machine Learning Models 46 2.5.1 Background and Motivation 46 2.5.2 Algorithm 46 2.5.3 Applications 49 2.5.4 Toolbox 50 2.6 Distributed Learning 50 2.6.1 Introduction 50 2.6.2 Definitions 51 2.6.3 Methods 51 2.6.4 Toolbox 54 2.7 Robustness 54 2.7.1 Background and Motivation 54 2.7.2 Definitions 55 2.7.3 Methods 55 2.7.3.1 Training with Noisy Data/Labels 55 2.7.3.2 Adversarial Attacks 55 2.7.3.3 Defense Mechanisms 56 2.7.4 Toolbox 56 2.8 Interpretability 56 2.8.1 Background and Motivation 56 2.8.2 Definitions 57 2.8.3 Algorithm 57 2.8.4 ToolBox 58 2.9 Transformers and Attention Mechanisms for Text and Vision Models 58 2.9.1 Background and Motivation 58 2.9.2 Algorithm 59 2.9.3 Application 60 2.9.4 Toolbox 61 2.10 Hardware for Machine Learning Applications 62 2.10.1 Cpu 62 2.10.2 Gpu 63 2.10.3 ASICs 63 2.10.4 Fpga 64 Acknowledgment 64 References 64 Section II Advancing Electromagnetic Inverse Design with Machine Learning 81 3 Breaking the Curse of Dimensionality in Electromagnetics Design Through Optimization Empowered by Machine Learning 83 N. Anselmi, G. Oliveri, L. Poli, A. Polo, P. Rocca, M. Salucci, and A. Massa 3.1 Introduction 83 3.2 The SbD Pillars and Fundamental Concepts 85 3.3 SbD at Work in EMs Design 88 3.3.1 Design of Elementary Radiators 88 3.3.2 Design of Reflectarrays 92 3.3.3 Design of Metamaterial Lenses 93 3.3.4 Other SbD Customizations 96 3.4 Final Remarks and Envisaged Trends 101 Acknowledgments 101 References 102 4 Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization 105 Feng Feng, Weicong Na, Jing Jin, and Qi-Jun Zhang 4.1 Introduction 105 4.2 ANN Structure and Training for Parametric EM Modeling 106 4.3 Deep Neural Network for Microwave Modeling 107 4.3.1 Structure of the Hybrid DNN 107 4.3.2 Training of the Hybrid DNN 108 4.3.3 Parameter-Extraction Modeling of a Filter Using the Hybrid DNN 108 4.4 Knowledge-Based Parametric Modeling for Microwave Components 111 4.4.1 Unified Knowledge-Based Parametric Model Structure 112 4.4.2 Training with l 1 Optimization of the Unified Knowledge-Based Parametric Model 115 4.4.3 Automated Knowledge-Based Model Generation 117 4.4.4 Knowledge-Based Parametric Modeling of a Two-Section Low-Pass Elliptic Microstrip Filter 117 4.5 Parametric Modeling Using Combined ANN and Transfer Function 121 4.5.1 Neuro-TF Modeling in Rational Form 121 4.5.2 Neuro-TF Modeling in Zero/Pole Form 122 4.5.3 Neuro-TF Modeling in Pole/Residue Form 123 4.5.4 Vector Fitting Technique for Parameter Extraction 123 4.5.5 Two-Phase Training for Neuro-TF Models 123 4.5.6 Neuro-TF Model Based on Sensitivity Analysis 125 4.5.7 A Diplexer Example Using Neuro-TF Model Based on Sensitivity Analysis 126 4.6 Surrogate Optimization of EM Design Based on ANN 129 4.6.1 Surrogate Optimization and Trust Region Update 129 4.6.2 Neural TF Optimization Method Based on Adjoint Sensitivity Analysis 130 4.6.3 Surrogate Model Optimization Based on Feature-Assisted of Neuro-TF 130 4.6.4 EM Optimization of a Microwave Filter Utilizing Feature-Assisted Neuro-TF 131 4.7 Conclusion 133 References 133 5 Advanced Neural Networks for Electromagnetic Modeling and Design 141 Bing-Zhong Wang, Li-Ye Xiao, and Wei Shao 5.1 Introduction 141 5.2 Semi-Supervised Neural Networks for Microwave Passive Component Modeling 141 5.2.1 Semi-Supervised Learning Based on Dynamic Adjustment Kernel Extreme Learning Machine 141 5.2.1.1 Dynamic Adjustment Kernel Extreme Learning Machine 142 5.2.1.2 Semi-Supervised Learning Based on DA-KELM 147 5.2.1.3 Numerical Examples 150 5.2.2 Semi-Supervised Radial Basis Function Neural Network 157 5.2.2.1 Semi-Supervised Radial Basis Function Neural Network 157 5.2.2.2 Sampling Strategy 161 5.2.2.3 SS-RBFNN With Sampling Strategy 162 5.3 Neural Networks for Antenna and Array Modeling 166 5.3.1 Modeling of Multiple Performance Parameters for Antennas 166 5.3.2 Inverse Artificial Neural Network for Multi-objective Antenna Design 175 5.3.2.1 Knowledge-Based Neural Network for Periodic Array Modeling 183 5.4 Autoencoder Neural Network for Wave Propagation in Uncertain Media 188 5.4.1 Two-Dimensional GPR System with the Dispersive and Lossy Soil 188 5.4.2 Surrogate Model for GPR Modeling 190 5.4.3 Modeling Results 191 References 193 Section III Deep Learning for Metasurface Design 197 6 Generative Machine Learning for Photonic Design 199 Dayu Zhu, Zhaocheng Liu, and Wenshan Cai 6.1 Brief Introduction to Generative Models 199 6.1.1 Probabilistic Generative Model 199 6.1.2 Parametrization and Optimization with Generative Models 199 6.1.2.1 Probabilistic Model for Gradient-Based Optimization 200 6.1.2.2 Sampling-Based Optimization 200 6.1.2.3 Generative Design Strategy 201 6.1.2.4 Generative Adversarial Networks in Photonic Design 202 6.1.2.5 Discussion 203 6.2 Generative Model for Inverse Design of Metasurfaces 203 6.2.1 Generative Design Strategy for Metasurfaces 203 6.2.2 Model Validation 204 6.2.3 On-demand Design Results 206 6.3 Gradient-Free Optimization with Generative Model 207 6.3.1 Gradient-Free Optimization Algorithms 207 6.3.2 Evolution Strategy with Generative Parametrization 207 6.3.2.1 Generator from VAE 207 6.3.2.2 Evolution Strategy 208 6.3.2.3 Model Validation 209 6.3.2.4 On-demand Design Results 209 6.3.3 Cooperative Coevolution and Generative Parametrization 210 6.3.3.1 Cooperative Coevolution 210 6.3.3.2 Diatomic Polarizer 211 6.3.3.3 Gradient Metasurface 211 6.4 Design Large-Scale, Weakly Coupled System 213 6.4.1 Weak Coupling Approximation 214 6.4.2 Analog Differentiator 214 6.4.3 Multiplexed Hologram 215 6.5 Auxiliary Methods for Generative Photonic Parametrization 217 6.5.1 Level Set Method 217 6.5.2 Fourier Level Set 218 6.5.3 Implicit Neural Representation 218 6.5.4 Periodic Boundary Conditions 220 6.6 Summary 221 References 221 7 Machine Learning Advances in Computational Electromagnetics 225 Robert Lupoiu and Jonathan A. Fan 7.1 Introduction 225 7.2 Conventional Electromagnetic Simulation Techniques 226 7.2.1 Finite Difference Frequency (FDFD) and Time (FDTD) Domain Solvers 226 7.2.2 The Finite Element Method (FEM) 229 7.2.2.1 Meshing 229 7.2.2.2 Basis Function Expansion 229 7.2.2.3 Residual Formulation 230 7.2.3 Method of Moments (MoM) 230 7.3 Deep Learning Methods for Augmenting Electromagnetic Solvers 231 7.3.1 Time Domain Simulators 231 7.3.1.1 Hardware Acceleration 231 7.3.1.2 Learning Finite Difference Kernels 232 7.3.1.3 Learning Absorbing Boundary Conditions 234 7.3.2 Augmenting Variational CEM Techniques Via Deep Learning 234 7.4 Deep Electromagnetic Surrogate Solvers Trained Purely with Data 235 7.5 Deep Surrogate Solvers Trained with Physical Regularization 240 7.5.1 Physics-Informed Neural Networks (PINNs) 240 7.5.2 Physics-Informed Neural Networks with Hard Constraints (hPINNs) 241 7.5.3 WaveY-Net 243 7.6 Conclusions and Perspectives 249 Acknowledgments 250 References 250 8 Design of Nanofabrication-Robust Metasurfaces Through Deep Learning-Augmented Multiobjective Optimization 253 Ronald P. Jenkins, Sawyer D. Campbell, and Douglas H. Werner 8.1 Introduction 253 8.1.1 Metasurfaces 253 8.1.2 Fabrication State-of-the-Art 253 8.1.3 Fabrication Challenges 254 8.1.3.1 Fabrication Defects 254 8.1.4 Overcoming Fabrication Limitations 255 8.2 Related Work 255 8.2.1 Robustness Topology Optimization 255 8.2.2 Deep Learning in Nanophotonics 256 8.3 DL-Augmented Multiobjective Robustness Optimization 257 8.3.1 Supercells 257 8.3.1.1 Parameterization of Freeform Meta-Atoms 257 8.3.2 Robustness Estimation Method 259 8.3.2.1 Simulating Defects 259 8.3.2.2 Existing Estimation Methods 259 8.3.2.3 Limitations of Existing Methods 259 8.3.2.4 Solver Choice 260 8.3.3 Deep Learning Augmentation 260 8.3.3.1 Challenges 261 8.3.3.2 Method 261 8.3.4 Multiobjective Global Optimization 267 8.3.4.1 Single Objective Cost Functions 267 8.3.4.2 Dominance Relationships 267 8.3.4.3 A Robustness Objective 269 8.3.4.4 Problems with Optimization and DL Models 269 8.3.4.5 Error-Tolerant Cost Functions 269 8.3.5 Robust Supercell Optimization 270 8.3.5.1 Pareto Front Results 270 8.3.5.2 Examples from the Pareto Front 271 8.3.5.3 The Value of Exhaustive Sampling 272 8.3.5.4 Speedup Analysis 273 8.4 Conclusion 275 8.4.1 Future Directions 275 Acknowledgments 276 References 276 9 Machine Learning for Metasurfaces Design and Their Applications 281 Kumar Vijay Mishra, Ahmet M. Elbir, and Amir I. Zaghloul 9.1 Introduction 281 9.1.1 ML/DL for RIS Design 283 9.1.2 ML/DL for RIS Applications 283 9.1.3 Organization 285 9.2 Inverse RIS Design 285 9.2.1 Genetic Algorithm (GA) 286 9.2.2 Particle Swarm Optimization (PSO) 286 9.2.3 Ant Colony Optimization (ACO) 289 9.3 DL-Based Inverse Design and Optimization 289 9.3.1 Artificial Neural Network (ANN) 289 9.3.1.1 Deep Neural Networks (DNN) 290 9.3.2 Convolutional Neural Networks (CNNs) 290 9.3.3 Deep Generative Models (DGMs) 291 9.3.3.1 Generative Adversarial Networks (GANs) 291 9.3.3.2 Conditional Variational Autoencoder (cVAE) 293 9.3.3.3 Global Topology Optimization Networks (GLOnets) 293 9.4 Case Studies 294 9.4.1 MTS Characterization Model 294 9.4.2 Training and Design 296 9.5 Applications 298 9.5.1 DL-Based Signal Detection in RIS 302 9.5.2 DL-Based RIS Channel Estimation 303 9.6 DL-Aided Beamforming for RIS Applications 306 9.6.1 Beamforming at the RIS 306 9.6.2 Secure-Beamforming 308 9.6.3 Energy-Efficient Beamforming 309 9.6.4 Beamforming for Indoor RIS 309 9.7 Challenges and Future Outlook 309 9.7.1 Design 310 9.7.1.1 Hybrid Physics-Based Models 310 9.7.1.2 Other Learning Techniques 310 9.7.1.3 Improved Data Representation 310 9.7.2 Applications 311 9.7.3 Channel Modeling 311 9.7.3.1 Data Collection 311 9.7.3.2 Model Training 311 9.7.3.3 Environment Adaptation and Robustness 312 9.8 Summary 312 Acknowledgments 313 References 313 Section IV Rf, Antenna, Inverse-scattering, and other Em Applications of Deep Learning 319 10 Deep Learning for Metasurfaces and Metasurfaces for Deep Learning 321 Clayton Fowler, Sensong An, Bowen Zheng, and Hualiang Zhang 10.1 Introduction 321 10.2 Forward-Predicting Networks 322 10.2.1 FCNN (Fully Connected Neural Networks) 323 10.2.2 CNN (Convolutional Neural Networks) 324 10.2.2.1 Nearly Free-Form Meta-Atoms 324 10.2.2.2 Mutual Coupling Prediction 327 10.2.3 Sequential Neural Networks and Universal Forward Prediction 330 10.2.3.1 Sequencing Input Data 331 10.2.3.2 Recurrent Neural Networks 332 10.2.3.3 1D Convolutional Neural Networks 332 10.3 Inverse-Design Networks 333 10.3.1 Tandem Network for Inverse Designs 333 10.3.2 Generative Adversarial Nets (GANs) 335 10.4 Neuromorphic Photonics 339 10.5 Summary and Outlook 340 References 341 11 Forward and Inverse Design of Artificial Electromagnetic Materials 345 Jordan M. Malof, Simiao Ren, and Willie J. Padilla 11.1 Introduction 345 11.1.1 Problem Setting 346 11.1.2 Artificial Electromagnetic Materials 347 11.1.2.1 Regime 1: Floquet–Bloch 348 11.1.2.2 Regime 2: Resonant Effective Media 349 11.1.2.3 All-Dielectric Metamaterials 350 11.2 The Design Problem Formulation 351 11.3 Forward Design 352 11.3.1 Search Efficiency 353 11.3.2 Evaluation Time 354 11.3.3 Challenges with the Forward Design of Advanced AEMs 354 11.3.4 Deep Learning the Forward Model 355 11.3.4.1 When Does Deep Learning Make Sense? 355 11.3.4.2 Common Deep Learning Architectures 356 11.3.5 The Forward Design Bottleneck 356 11.4 Inverse Design with Deep Learning 357 11.4.1 Why Inverse Problems Are Often Difficult 359 11.4.2 Deep Inverse Models 360 11.4.2.1 Does the Inverse Model Address Non-uniqueness? 360 11.4.2.2 Multi-solution Versus Single-Solution Models 360 11.4.2.3 Iterative Methods versus Direct Mappings 361 11.4.3 Which Inverse Models Perform Best? 361 11.5 Conclusions and Perspectives 362 11.5.1 Reducing the Need for Training Data 362 11.5.1.1 Transfer Learning 362 11.5.1.2 Active Learning 363 11.5.1.3 Physics-Informed Learning 363 11.5.2 Inverse Modeling for Non-existent Solutions 363 11.5.3 Benchmarking, Replication, and Sharing Resources 364 Acknowledgments 364 References 364 12 Machine Learning-Assisted Optimization and Its Application to Antenna and Array Designs 371 Qi Wu, Haiming Wang, and Wei Hong 12.1 Introduction 371 12.2 Machine Learning-Assisted Optimization Framework 372 12.3 Machine Learning-Assisted Optimization for Antenna and Array Designs 375 12.3.1 Design Space Reduction 375 12.3.2 Variable-Fidelity Evaluation 375 12.3.3 Hybrid Optimization Algorithm 378 12.3.4 Robust Design 379 12.3.5 Antenna Array Synthesis 380 12.4 Conclusion 381 References 381 13 Analysis of Uniform and Non-uniform Antenna Arrays Using Kernel Methods 385 Manel Martínez-Ramón, José Luis Rojo Álvarez, Arjun Gupta, and Christos Christodoulou 13.1 Introduction 385 13.2 Antenna Array Processing 386 13.2.1 Detection of Angle of Arrival 387 13.2.2 Optimum Linear Beamformers 388 13.2.3 Direction of Arrival Detection with Random Arrays 389 13.3 Support Vector Machines in the Complex Plane 390 13.3.1 The Support Vector Criterion for Robust Regression in the Complex Plane 390 13.3.2 The Mercer Theorem and the Nonlinear SVM 393 13.4 Support Vector Antenna Array Processing with Uniform Arrays 394 13.4.1 Kernel Array Processors with Temporal Reference 394 13.4.1.1 Relationship with the Wiener Filter 394 13.4.2 Kernel Array Processor with Spatial Reference 395 13.4.2.1 Eigenanalysis in a Hilbert Space 395 13.4.2.2 Formulation of the Processor 396 13.4.2.3 Relationship with Nonlinear MVDM 397 13.4.3 Examples of Temporal and Spatial Kernel Beamforming 398 13.5 DOA in Random Arrays with Complex Gaussian Processes 400 13.5.1 Snapshot Interpolation from Complex Gaussian Process 400 13.5.2 Examples 402 13.6 Conclusion 403 Acknowledgments 404 References 404 14 Knowledge-Based Globalized Optimization of High-Frequency Structures Using Inverse Surrogates 409 Anna Pietrenko-Dabrowska and Slawomir Koziel 14.1 Introduction 409 14.2 Globalized Optimization by Feature-Based Inverse Surrogates 411 14.2.1 Design Task Formulation 411 14.2.2 Evaluating Design Quality with Response Features 412 14.2.3 Globalized Search by Means of Inverse Regression Surrogates 414 14.2.4 Local Tuning Procedure 418 14.2.5 Global Optimization Algorithm 420 14.3 Results 421 14.3.1 Verification Structures 422 14.3.2 Results 423 14.3.3 Discussion 423 14.4 Conclusion 428 Acknowledgment 428 References 428 15 Deep Learning for High Contrast Inverse Scattering of Electrically Large Structures 435 Qing Liu, Li-Ye Xiao, Rong-Han Hong, and Hao-Jie Hu 15.1 Introduction 435 15.2 General Strategy and Approach 436 15.2.1 Related Works by Others and Corresponding Analyses 436 15.2.2 Motivation 437 15.3 Our Approach for High Contrast Inverse Scattering of Electrically Large Structures 438 15.3.1 The 2-D Inverse Scattering Problem with Electrically Large Structures 438 15.3.1.1 Dual-Module NMM-IEM Machine Learning Model 438 15.3.1.2 Receiver Approximation Machine Learning Method 440 15.3.2 Application for 3-D Inverse Scattering Problem with Electrically Large Structures 441 15.3.2.1 Semi-Join Extreme Learning Machine 441 15.3.2.2 Hybrid Neural Network Electromagnetic Inversion Scheme 445 15.4 Applications of Our Approach 450 15.4.1 Applications for 2-D Inverse Scattering Problem with Electrically Large Structures 450 15.4.1.1 Dual-Module NMM-IEM Machine Learning for Fast Electromagnetic Inversion of Inhomogeneous Scatterers with High Contrasts and Large Electrical Dimensions 450 15.4.1.2 Nonlinear Electromagnetic Inversion of Damaged Experimental Data by a Receiver Approximation Machine Learning Method 454 15.4.2 Applications for 3-D Inverse Scattering Problem with Electrically Large Structures 459 15.4.2.1 Super-Resolution 3-D Microwave Imaging of Objects with High Contrasts by a Semi-Join Extreme Learning Machine 459 15.4.2.2 A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-Dimensional Microwave Human Brain Imaging 473 15.5 Conclusion and Future work 480 15.5.1 Summary of Our Work 480 15.5.1.1 Limitations and Potential Future Works 481 References 482 16 Radar Target Classification Using Deep Learning 487 Youngwook Kim 16.1 Introduction 487 16.2 Micro-Doppler Signature Classification 488 16.2.1 Human Motion Classification 490 16.2.2 Human Hand Gesture Classification 494 16.2.3 Drone Detection 495 16.3 SAR Image Classification 497 16.3.1 Vehicle Detection 497 16.3.2 Ship Detection 499 16.4 Target Classification in Automotive Radar 500 16.5 Advanced Deep Learning Algorithms for Radar Target Classification 503 16.5.1 Transfer Learning 504 16.5.2 Generative Adversarial Networks 506 16.5.3 Continual Learning 508 16.6 Conclusion 511 References 511 17 Koopman Autoencoders for Reduced-Order Modeling of Kinetic Plasmas 515 Indranil Nayak, Mrinal Kumar, and Fernando L. Teixeira 17.1 Introduction 515 17.2 Kinetic Plasma Models: Overview 516 17.3 EMPIC Algorithm 517 17.3.1 Overview 517 17.3.2 Field Update Stage 519 17.3.3 Field Gather Stage 521 17.3.4 Particle Pusher Stage 521 17.3.5 Current and Charge Scatter Stage 522 17.3.6 Computational Challenges 522 17.4 Koopman Autoencoders Applied to EMPIC Simulations 523 17.4.1 Overview and Motivation 523 17.4.2 Koopman Operator Theory 524 17.4.3 Koopman Autoencoder (KAE) 527 17.4.3.1 Case Study I: Oscillating Electron Beam 529 17.4.3.2 Case Study II: Virtual Cathode Formation 532 17.4.4 Computational Gain 534 17.5 Towards A Physics-Informed Approach 535 17.6 Outlook 536 Acknowledgments 537 References 537 Index 543

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Book SynopsisCONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as welTable of ContentsPreface xvii Part I: Various Approaches from Machine Learning to Deep Learning 1 1 Web-Assisted Noninvasive Detection of Oral Submucous Fibrosis Using IoHT 3 Animesh Upadhyaya, Vertika Rai, Debdutta Pal, Surajit Bose and Somnath Ghosh 1.1 Introduction 3 1.2 Literature Survey 6 1.2.1 Oral Cancer 6 1.3 Primary Concepts 7 1.3.1 Transmission Efficiency 7 1.4 Propose Model 9 1.4.1 Platform Configuration 9 1.4.2 Harvard Architectural Microcontroller Base Wireless Communication Board 10 1.4.2.1 NodeMCU ESP8266 Microcontroller 10 1.4.2.2 Gas Sensor 12 1.4.3 Experimental Setup 13 1.4.4 Process to Connect to Sever and Analyzing Data on Cloud 14 1.5 Comparative Study 16 1.6 Conclusion 17 References 17 2 Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction 21 Sajeev Ram Arumugam, Sheela Gowr, Abimala, Balakrishna and Oswalt Manoj 2.1 Introduction 22 2.2 Related Research 23 2.2.1 Literature Review on Comparing the Performance of the ML/DL Algorithms 23 2.2.2 Literature Review on House Price Prediction 25 2.3 Research Methodology 26 2.3.1 Data Collection 27 2.3.2 Data Visualization 27 2.3.3 Data Preparation 28 2.3.4 Regression Models 29 2.3.4.1 Simple Linear Regression 29 2.3.4.2 Random Forest Regression 30 2.3.4.3 Ada Boosting Regression 31 2.3.4.4 Gradient Boosting Regression 32 2.3.4.5 Support Vector Regression 33 2.3.4.6 Artificial Neural Network 34 2.3.4.7 Multioutput Regression 36 2.3.4.8 Regression Using Tensorflow—Keras 37 2.3.5 Classification Models 39 2.3.5.1 Logistic Regression Classifier 39 2.3.5.2 Decision Tree Classifier 39 2.3.5.3 Random Forest Classifier 41 2.3.5.4 Naïve Bayes Classifier 41 2.3.5.5 K-Nearest Neighbors Classifier 42 2.3.5.6 Support Vector Machine Classifier (SVM) 43 2.3.5.7 Feed Forward Neural Network 43 2.3.5.8 Recurrent Neural Networks 44 2.3.5.9 LSTM Recurrent Neural Networks 44 2.3.6 Performance Metrics for Regression Models 45 2.3.7 Performance Metrics for Classification Models 46 2.4 Experimentation 47 2.5 Results and Discussion 48 2.6 Suggestions 60 2.7 Conclusion 60 References 62 3 Cyber Physical Systems, Machine Learning & Deep Learning— Emergence as an Academic Program and Field for Developing Digital Society 67 P. K. Paul 3.1 Introduction 68 3.2 Objective of the Work 69 3.3 Methods 69 3.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality 70 3.5 ml and dl Basics with Educational Potentialities 72 3.5.1 Machine Learning (ML) 72 3.5.2 Deep Learning 73 3.6 Manpower and Developing Scenario in Machine Learning and Deep Learning 74 3.7 dl & ml in Indian Context 79 3.8 Conclusion 81 References 82 4 Detection of Fake News and Rumors in the Social Media Using Machine Learning Techniques With Semantic Attributes 85 Diganta Saha, Arijit Das, Tanmay Chandra Nath, Soumyadip Saha and Ratul Das 4.1 Introduction 86 4.2 Literature Survey 87 4.3 Proposed Work 88 4.3.1 Algorithm 89 4.3.2 Flowchart 90 4.3.3 Explanation of Approach 91 4.4 Results and Analysis 92 4.4.1 Datasets 92 4.4.2 Evaluation 93 4.4.2.1 Result of 1st Dataset 93 4.4.2.2 Result of 2nd Dataset 94 4.4.2.3 Result of 3rd Dataset 94 4.4.3 Relative Comparison of Performance 95 4.5 Conclusion 95 References 96 Part II: Innovative Solutions Based on Deep Learning 99 5 Online Assessment System Using Natural Language Processing Techniques 101 S. Suriya, K. Nagalakshmi and Nivetha S. 5.1 Introduction 102 5.2 Literature Survey 103 5.3 Existing Algorithms 108 5.4 Proposed System Design 111 5.5 System Implementation 115 5.6 Conclusion 120 References 121 6 On a Reference Architecture to Build Deep-Q Learning-Based Intelligent IoT Edge Solutions 123 Amit Chakraborty, Ankit Kumar Shaw and Sucharita Samanta 6.1 Introduction 124 6.1.1 A Brief Primer on Machine Learning 124 6.1.1.1 Types of Machine Learning 124 6.2 Dynamic Programming 128 6.3 Deep Q-Learning 129 6.4 IoT 130 6.4.1 Azure 130 6.4.1.1 IoT on Azure 130 6.5 Conclusion 144 6.6 Future Work 144 References 145 7 Fuzzy Logic-Based Air Conditioner System 147 Suparna Biswas, Sayan Roy Chaudhuri, Ayusha Biswas and Arpan Bhawal 7.1 Introduction 147 7.2 Fuzzy Logic-Based Control System 149 7.3 Proposed System 149 7.3.1 Fuzzy Variables 149 7.3.2 Fuzzy Base Class 154 7.3.3 Fuzzy Rule Base 155 7.3.4 Fuzzy Rule Viewer 156 7.4 Simulated Result 157 7.5 Conclusion and Future Work 163 References 163 8 An Efficient Masked-Face Recognition Technique to Combat with COVID- 19 165 Suparna Biswas 8.1 Introduction 165 8.2 Related Works 167 8.2.1 Review of Face Recognition for Unmasked Faces 167 8.2.2 Review of Face Recognition for Masked Faces 168 8.3 Mathematical Preliminaries 169 8.3.1 Digital Curvelet Transform (DCT) 169 8.3.2 Compressive Sensing–Based Classification 170 8.4 Proposed Method 171 8.5 Experimental Results 173 8.5.1 Database 173 8.5.2 Result 175 8.6 Conclusion 179 References 179 9 Deep Learning: An Approach to Encounter Pandemic Effect of Novel Corona Virus (COVID-19) 183 Santanu Koley, Pinaki Pratim Acharjya, Rajesh Mukherjee, Soumitra Roy and Somdeep Das 9.1 Introduction 184 9.2 Interpretation With Medical Imaging 185 9.3 Corona Virus Variants Tracing 188 9.4 Spreading Capability and Destructiveness of Virus 191 9.5 Deduction of Biological Protein Structure 192 9.6 Pandemic Model Structuring and Recommended Drugs 192 9.7 Selection of Medicine 195 9.8 Result Analysis 197 9.9 Conclusion 201 References 202 10 Question Answering System Using Deep Learning in the Low Resource Language Bengali 207 Arijit Das and Diganta Saha 10.1 Introduction 208 10.2 Related Work 210 10.3 Problem Statement 215 10.4 Proposed Approach 215 10.5 Algorithm 216 10.6 Results and Discussion 219 10.6.1 Result Summary for TDIL Dataset 219 10.6.2 Result Summary for SQuAD Dataset 219 10.6.3 Examples of Retrieved Answers 220 10.6.4 Calculation of TP, TN, FP, FN, Accuracy, Precision, Recall, and F1 score 221 10.6.5 Comparison of Result with other Methods and Dataset 222 10.7 Analysis of Error 223 10.8 Few Close Observations 223 10.9 Applications 224 10.10 Scope for Improvements 224 10.11 Conclusions 224 Acknowledgments 225 References 225 Part III: Security and Safety Aspects with Deep Learning 231 11 Secure Access to Smart Homes Using Biometric Authentication With RFID Reader for IoT Systems 233 K.S. Niraja and Sabbineni Srinivasa Rao 11.1 Introduction 234 11.2 Related Work 235 11.3 Framework for Smart Home Use Case With Biometric 236 11.3.1 RFID-Based Authentication and Its Drawbacks 236 11.4 Control Scheme for Secure Access (CSFSC) 237 11.4.1 Problem Definition 237 11.4.2 Biometric-Based RFID Reader Proposed Scheme 238 11.4.3 Reader-Based Procedures 240 11.4.4 Backend Server-Side Procedures 240 11.4.5 Reader Side Final Compute and Check Operations 240 11.5 Results Observed Based on Various Features With Proposed and Existing Methods 242 11.6 Conclusions and Future Work 245 References 246 12 MQTT-Based Implementation of Home Automation System Prototype With Integrated Cyber-IoT Infrastructure and Deep Learning–Based Security Issues 249 Arnab Chakraborty 12.1 Introduction 250 12.2 Architecture of Implemented Home Automation 252 12.3 Challenges in Home Automation 253 12.3.1 Distributed Denial of Service and Attack 254 12.3.2 Deep Learning–Based Solution Aspects 254 12.4 Implementation 255 12.4.1 Relay 256 12.4.2 DHT 11 257 12.5 Results and Discussions 262 12.6 Conclusion 265 References 266 13 Malware Detection in Deep Learning 269 Sharmila Gaikwad and Jignesh Patil 13.1 Introduction to Malware 270 13.1.1 Computer Security 270 13.1.2 What Is Malware? 271 13.2 Machine Learning and Deep Learning for Malware Detection 274 13.2.1 Introduction to Machine Learning 274 13.2.2 Introduction to Deep Learning 276 13.2.3 Detection Techniques Using Deep Learning 279 13.3 Case Study on Malware Detection 280 13.3.1 Impact of Malware on Systems 280 13.3.2 Effect of Malware in a Pandemic Situation 281 13.4 Conclusion 283 References 283 14 Patron for Women: An Application for Womens Safety 285 Riya Sil, Snatam Kamila, Ayan Mondal, Sufal Paul, Santanu Sinha and Bishes Saha 14.1 Introduction 286 14.2 Background Study 286 14.3 Related Research 287 14.3.1 A Mobile-Based Women Safety Application (I safe App) 287 14.3.2 Lifecraft: An Android-Based Application System for Women Safety 288 14.3.3 Abhaya: An Android App for the Safety of Women 288 14.3.4 Sakhi—The Saviour: An Android Application to Help Women in Times of Social Insecurity 289 14.4 Proposed Methodology 289 14.4.1 Motivation and Objective 290 14.4.2 Proposed System 290 14.4.3 System Flowchart 291 14.4.4 Use-Case Model 291 14.4.5 Novelty of the Work 294 14.4.6 Comparison with Existing System 294 14.5 Results and Analysis 294 14.6 Conclusion and Future Work 298 References 299 15 Concepts and Techniques in Deep Learning Applications in the Field of IoT Systems and Security 303 Santanu Koley and Pinaki Pratim Acharjya 15.1 Introduction 304 15.2 Concepts of Deep Learning 307 15.3 Techniques of Deep Learning 308 15.3.1 Classic Neural Networks 309 15.3.1.1 Linear Function 309 15.3.1.2 Nonlinear Function 309 15.3.1.3 Sigmoid Curve 310 15.3.1.4 Rectified Linear Unit 310 15.3.2 Convolution Neural Networks 310 15.3.2.1 Convolution 311 15.3.2.2 Max-Pooling 311 15.3.2.3 Flattening 311 15.3.2.4 Full Connection 311 15.3.3 Recurrent Neural Networks 312 15.3.3.1 LSTMs 312 15.3.3.2 Gated RNNs 312 15.3.4 Generative Adversarial Networks 313 15.3.5 Self-Organizing Maps 314 15.3.6 Boltzmann Machines 315 15.3.7 Deep Reinforcement Learning 315 15.3.8 Auto Encoders 316 15.3.8.1 Sparse 317 15.3.8.2 Denoising 317 15.3.8.3 Contractive 317 15.3.8.4 Stacked 317 15.3.9 Back Propagation 317 15.3.10 Gradient Descent 318 15.4 Deep Learning Applications 319 15.4.1 Automatic Speech Recognition (ASR) 319 15.4.2 Image Recognition 320 15.4.3 Natural Language Processing 320 15.4.4 Drug Discovery and Toxicology 321 15.4.5 Customer Relationship Management 322 15.4.6 Recommendation Systems 323 15.4.7 Bioinformatics 324 15.5 Concepts of IoT Systems 325 15.6 Techniques of IoT Systems 326 15.6.1 Architecture 326 15.6.2 Programming Model 327 15.6.3 Scheduling Policy 329 15.6.4 Memory Footprint 329 15.6.5 Networking 332 15.6.6 Portability 332 15.6.7 Energy Efficiency 333 15.7 IoT Systems Applications 333 15.7.1 Smart Home 334 15.7.2 Wearables 335 15.7.3 Connected Cars 335 15.7.4 Industrial Internet 336 15.7.5 Smart Cities 337 15.7.6 IoT in Agriculture 337 15.7.7 Smart Retail 338 15.7.8 Energy Engagement 339 15.7.9 IoT in Healthcare 340 15.7.10 IoT in Poultry and Farming 340 15.8 Deep Learning Applications in the Field of IoT Systems 341 15.8.1 Organization of DL Applications for IoT in Healthcare 342 15.8.2 DeepSense as a Solution for Diverse IoT Applications 343 15.8.3 Deep IoT as a Solution for Energy Efficiency 346 15.9 Conclusion 346 References 347 16 Efficient Detection of Bioweapons for Agricultural Sector Using Narrowband Transmitter and Composite Sensing Architecture 349 Arghyadeep Nag, Labani Roy, Shruti, Soumen Santra and Arpan Deyasi 16.1 Introduction 350 16.2 Literature Review 353 16.3 Properties of Insects 355 16.4 Working Methodology 357 16.4.1 Sensing 357 16.4.1.1 Specific Characterization of a Particular Species 357 16.4.2 Alternative Way to Find Those Previously Sensing Parameters 357 16.4.3 Remedy to Overcome These Difficulties 358 16.4.4 Take Necessary Preventive Actions 358 16.5 Proposed Algorithm 359 16.6 Block Diagram and Used Sensors 360 16.6.1 Arduino Uno 361 16.6.2 Infrared Motion Sensor 362 16.6.3 Thermographic Camera 362 16.6.4 Relay Module 362 16.7 Result Analysis 362 16.8 Conclusion 363 References 363 17 A Deep Learning–Based Malware and Intrusion Detection Framework 367 Pavitra Kadiyala and Kakelli Anil Kumar 17.1 Introduction 367 17.2 Literature Survey 368 17.3 Overview of the Proposed Work 371 17.3.1 Problem Description 371 17.3.2 The Working Models 371 17.3.3 About the Dataset 371 17.3.4 About the Algorithms 373 17.4 Implementation 374 17.4.1 Libraries 374 17.4.2 Algorithm 376 17.5 Results 376 17.5.1 Neural Network Models 377 17.5.2 Accuracy 377 17.5.3 Web Frameworks 377 17.6 Conclusion and Future Work 379 References 380 18 Phishing URL Detection Based on Deep Learning Techniques 381 S. Carolin Jeeva and W. Regis Anne 18.1 Introduction 382 18.1.1 Phishing Life Cycle 382 18.1.1.1 Planning 383 18.1.1.2 Collection 384 18.1.1.3 Fraud 384 18.2 Literature Survey 385 18.3 Feature Generation 388 18.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs 388 18.5 Results and Discussion 391 18.6 Conclusion 394 References 394 Web Citation 396 Part IV: Cyber Physical Systems 397 19 Cyber Physical System—The Gen Z 399 Jayanta Aich and Mst Rumana Sultana 19.1 Introduction 399 19.2 Architecture and Design 400 19.2.1 Cyber Family 401 19.2.2 Physical Family 401 19.2.3 Cyber-Physical Interface Family 402 19.3 Distribution and Reliability Management in CPS 403 19.3.1 CPS Components 403 19.3.2 CPS Models 404 19.4 Security Issues in CPS 405 19.4.1 Cyber Threats 405 19.4.2 Physical Threats 407 19.5 Role of Machine Learning in the Field of CPS 408 19.6 Application 411 19.7 Conclusion 411 References 411 20 An Overview of Cyber Physical System (CPS) Security, Threats, and Solutions 415 Krishna Keerthi Chennam, Fahmina Taranum and Maniza Hijab 20.1 Introduction 416 20.1.1 Motivation of Work 417 20.1.2 Organization of Sections 417 20.2 Characteristics of CPS 418 20.3 Types of CPS Security 419 20.4 Cyber Physical System Security Mechanism—Main Aspects 421 20.4.1 CPS Security Threats 423 20.4.2 Information Layer 423 20.4.3 Perceptual Layer 424 20.4.4 Application Threats 424 20.4.5 Infrastructure 425 20.5 Issues and How to Overcome Them 426 20.6 Discussion and Solutions 427 20.7 Conclusion 431 References 431 Index 435

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Book SynopsisUnderstanding Artificial Intelligence Provides students across majors with a clear and accessible overview of new artificial intelligence technologies and applications Artificial intelligence (AI) is broadly defined as computers programmed to simulate the cognitive functions of the human mind. In combination with the Neural Network (NN), Big Data (BD), and the Internet of Things (IoT), artificial intelligence has transformed everyday life: self-driving cars, delivery drones, digital assistants, facial recognition devices, autonomous vacuum cleaners, and mobile navigation apps all rely on AI to perform tasks. With the rise of artificial intelligence, the job market of the near future will be radically different???many jobs will disappear, yet new jobs and opportunities will emerge. Understanding Artificial Intelligence: Fundamentals and Applications covers the fundamental concepts and key technologies of AI while exploring its impact on the fTable of Contents1 Introduction 1 1.1 Overview 1 1.2 Development History 3 1.3 Neural Network Model 6 1.4 Popular Neural Network 7 1.4.1 Convolutional Neural Network 7 1.4.2 Recurrent Neural Network 8 1.4.3 Reinforcement Learning 9 1.5 Neural Network Classification 9 1.5.1 Supervised learning 10 1.5.2 Semi-supervised learning 10 1.5.3 Unsupervised learning 11 1.6 Neural Network Operation 11 1.6.1 Training 11 1.6.2 Inference 12 1.7 Application Development 12 1.7.1 Business Planning 14 1.7.2 Network Design 14 1.7.3 Data Engineering 14 1.7.4 System Integration 15 Exercise 16 2 Neural Network 17 2.1 Convolutional Layer 19 2.2 Activation Layer 20 2.3 Pooling Layer 21 2.4 Batch Normalization 22 2.5 Dropout Layer 22 2.6 Fully Connected Layer 23 Exercise 24 3 Machine Vision 25 3.1 Object Recognition 25 3.2 Feature Matching 27 3.3 Facial Recognition 28 3.4 Gesture Recognition 30 3.5 Machine Vision Applications 31 3.5.1 Medical Diagnosis 31 3.5.2 Retail Applications 32 3.5.3 Airport Security 33 Exercise 34 4 Natural Language Processing 35 4.1 Neural Network Model 36 4.1.1 Convolutional Neural Network 36 4.1.2 Recurrent Neural Network 37 4.1.2.1 Long Short-Term Memory Network 38 4.1.3 Recursive Neural Network 39 4.1.4 Reinforcement Learning 40 4.2 Natural Language Processing Applications 41 4.2.1 Virtual Assistant 41 4.2.2 Language Translation 42 4.2.3 Machine Transcription 43 Exercise 45 5 Autonomous Vehicle 46 5.1 Levels of Driving Automation 46 5.2 Autonomous Technology 48 5.2.1 Computer Vision 48 5.2.2 Sensor Fusion 49 5.2.3 Localization 51 5.2.4 Path Planning 52 5.2.5 Drive Control 52 5.3 Communication Strategies 53 5.3.1 Vehicle-to-Vehicle Communication 54 5.3.2 Vehicle-to-Infrastructure Communication 54 5.3.3 Vehicle-to-Pedestrian Communication 55 5.4 Law Legislation 56 5.4.1 Human Behavior 57 5.4.2 Lability 57 5.4.3 Regulation 58 5.5 Future Challenges 58 5.5.1 Road Rules Variation 58 5.5.2 Unified Communication Protocol 58 5.5.3 Safety Standard and Guideline 59 5.5.4 Weather/Disaster 59 Exercise 60 6 Drone 61 6.1 Drone Design 61 6.2 Drone Structure 62 6.2.1 Camera 63 6.2.2 Gyro Stabilization 63 6.2.3 Collision Avoidance 64 6.2.4 Global Positioning System 64 6.2.5 Sensors 64 6.3 Drone Regulation 65 6.3.1 Recreational Rules 65 6.3.2 Commercial Rules 66 6.4 Applications 66 6.4.1 Infrastructure Inspection 66 6.4.2 Civil Construction 67 6.4.3 Agriculture 68 6.4.4 Emergency Rescue 69 Exercise 70 7 Healthcare 71 7.1 Telemedicine 71 7.2 Medical Diagnosis 72 7.3 Medical Imaging 73 7.4 Smart Medical Device 74 7.5 Electronic Health Record 76 7.6 Medical Billing 77 7.7 Drug Development 78 7.8 Clinical Trial 79 7.9 Medical Robotics 80 7.10 Elderly Care 81 7.11 Future Challenges 82 Exercise 84 8 Finance 85 8.1 Fraud Prevention 85 8.2 Financial Forecast 88 8.3 Stock Trading 89 8.4 Banking 91 8.5 Accounting 94 8.6 Insurance 95 Exercise 96 9 Retail 97 9.1 E-Commerce 98 9.2 Virtual Shopping 100 9.3 Product Promotion 102 9.4 Store Management 103 9.5 Warehouse Management 104 9.6 Inventory Management 106 9.7 Supply Chain 108 Exercise 110 10 Manufacturing 111 10.1 Defect Detection 112 10.2 Quality Assurance 113 10.3 Production Integration 114 10.4 Generative Design 115 10.5 Predictive Maintenance 117 10.6 Environment Sustainability 118 10.7 Manufacturing Optimization 119 Exercise 121 11 Agriculture 122 11.1 Crop and Soil Monitoring 123 11.2 Agricultural Robot 125 11.3 Pest Control 126 11.4 Precision Farming 127 Exercise 129 12 Smart City 130 12.1 Smart Transportation 131 12.2 Smart Parking 132 12.3 Waste Management 133 12.4 Smart Grid 134 12.5 Environmental Conservation 135 Exercise 137 13 Government 138 13.1 Information Technology 140 13.2 Human Service 141 13.3 Law Enforcement 144 13.3.4 Augmenting Human Movement 147 13.4 Homeland Security 147 13.5 Legislation 149 13.6 Ethics 152 13.7 Public Perspective 155 Exercise 159 14 Computing Platform 160 14.1 Central Processing Unit 160 14.1.1 System Architecture 161 14.1.2 Advanced Vector Extension 164 14.1.3 Math Kernel Library for Deep Neural Network 165 14.2 Graphics Processing Unit 165 14.2.1 Tensor Core Architecture 167 14.2.2 NVLink2 Configuration 167 14.2.3 High Bandwidth Memory 169 14.3 Tensor Processing Unit 170 14.3.1 System Architecture 170 14.3.2 Brain Floating Point Format 171 14.3.3 Cloud Configuration 172 14.4 Neural Processing Unit 173 14.4.1 System Architecture 173 14.4.2 Deep Compression 174 14.4.3 Dynamic Memory Allocation 174 14.4.4 Edge AI Server 175 Exercise 176 Appendix A Kneron Neural Processing Unit 178 Appendix B Object Detection (Overview) 179 B.1 Kneron Environment Setup 179 B.2 Python Installation 180 B.3 Library Installation 184 B.4 Driver Installation 185 B.5 Model Installation 186 B.6 Image/Camera Detection 186 B.7 Yolo Class List 190 Appendix C Object Detection - Hardware 192 C.1 Library Setup 192 C.2 System Parameters 193 C.3 NPU Initialization 194 C.4 Image Detection 195 C.5 Camera Detection 197 Appendix D Hardware Transfer Mode 199 D.1 Serial Transfer Mode 199 D.2 Pipeline Transfer Mode 201 D.3 Parallel Transfer Mode 203 Appendix E Object Detection – Software (Optional) 205 E.1 Library Setup 205 E.2 Image Detection 207 E.3 Video Detection 208 Reference 211

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Book SynopsisMachine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systTable of ContentsSection-1 Machine & Deep Learning techniques for Image Processing 1.1 Image Features in Machine Learning 1.2 Image Segmentation and Classification using Deep Learning 1.3 Deep Learning based Synthetic Aperture Radar Image Classification 1.4 Design Perspectives of Multitask Deep Learning Models and Applications 1.5 Image Reconstruction using Deep Learning 1.6 Machine and Deep Learning Techniques for Image Super-Resolution Section-2 Machine & Deep Learning techniques for Text and Speech Processing 2.1 Machine and Deep Learning Techniques for Text and Speech Processing 2.2 Manipuri Handwritten Script Recognition using Machine and Deep Learning 2.3 Comparison of Different Text Extraction Techniques for Complex Color Images 2.4 Smart Text Reader System for Blind Person using Machine and Deep Learning 2.5 Machine Learning Techniques for Deaf People 2.6 Design and Development of Chatbot based on Reinforcement Learning 2.7 DNN based Speech Quality Enhancement and Multi-speaker Separation for Automatic Speech Recognition System 2.8 Design and Development of Real-Time Music Transcription using Digital Signal Processing Section-3 Applications of Signal and Image Processing with Machine & Deep learning techniques 3.1 Role of Machine Learning in Wrist Pulse Analysis 3.2 An Explainable Convolutional Neural Network based Method for Skin Lesion Classification from Dermoscopic Images 3.3 Future of Machine-Learning and Deep-Learning in Health-Care Monitoring System 3.4 Usage of AI & Wearable IoT Devices for Healthcare Data: A Study 3.5 Impact of IoT in Biomedical Applications using Machine and Deep Learning 3.6 Wireless Communications using Machine Learning and Deep Learning 3.7 Applications of Machine Learning and Deep Learning in Smart Agriculture 3.8 Structural Damage Prediction from Earthquakes using Deep Learning 3.9 Machine Learning and Deep Learning Techniques in Social Sciences 3.1O Green Energy using Machine and Deep Learning 3.11 Light Deep CNN Approach for Multi-Label Pathology Classification using Frontal Chest X-Ray Index

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Book SynopsisHandbook of Human-Machine Systems Insightful and cutting-edge discussions of recent developments in human-machine systems In Handbook of Human-Machine Systems, a team of distinguished researchers delivers a comprehensive exploration of human-machine systems (HMS) research and development from a variety of illuminating perspectives. The book offers a big picture look at state-of-the-art research and technology in the area of HMS. Contributing authors cover Brain-Machine Interfaces and Systems, including assistive technologies like devices used to improve locomotion. They also discuss advances in the scientific and engineering foundations of Collaborative Intelligent Systems and Applications. Companion technology, which combines trans-disciplinary research in fields like computer science, AI, and cognitive science, is explored alongside the applications of human cognition in intelligent and artificially intelligent system designs, human factors engineering, Table of ContentsEditors Biography xxi List of Contributors xxiii Preface xxxiii 1 Introduction 1 Giancarlo Fortino, David Kaber, Andreas Nürnberger, and David Mendonça 1.1 Book Rationale 1 1.2 Chapters Overview 2 Acknowledgments 8 References 8 2 Brain–Computer Interfaces: Recent Advances, Challenges, and Future Directions 11 Tiago H. Falk, Christoph Guger, and Ivan Volosyak 2.1 Introduction 11 2.2 Background 12 2.2.1 Active/Reactive BCIs 13 2.2.2 Passive BCIs 14 2.2.3 Hybrid BCIs 15 2.3 Recent Advances and Applications 15 2.3.1 Active/Reactive BCIs 15 2.3.2 Passive BCIs 16 2.3.3 Hybrid BCIs 16 2.4 Future Research Challenges 16 2.4.1 Current Research Issues 17 2.4.2 Future Research Directions 17 2.5 Conclusions 18 References 18 3 Brain–Computer Interfaces for Affective Neurofeedback Applications 23 Lucas R. Trambaiolli and Tiago H. Falk 3.1 Introduction 23 3.2 Background 23 3.3 State-of-the-Art 24 3.3.1 Depressive Disorder 25 3.3.2 Posttraumatic Stress Disorder, PTSD 26 3.4 Future Research Challenges 27 3.4.1 Open Challenges 27 3.4.2 Future Directions 28 3.5 Conclusion 28 References 29 4 Pediatric Brain–Computer Interfaces: An Unmet Need 35 Eli Kinney-Lang, Erica D. Floreani, Niloufaralsadat Hashemi, Dion Kelly, Stefanie S. Bradley, Christine Horner, Brian Irvine, Zeanna Jadavji, Danette Rowley, Ilyas Sadybekov, Si Long Jenny Tou, Ephrem Zewdie, Tom Chau, and Adam Kirton 4.1 Introduction 35 4.1.1 Motivation 36 4.2 Background 36 4.2.1 Components of a BCI 36 4.2.1.1 Signal Acquisition 36 4.2.1.2 Signal Processing 36 4.2.1.3 Feedback 36 4.2.1.4 Paradigms 37 4.2.2 Brain Anatomy and Physiology 37 4.2.3 Developmental Neurophysiology 38 4.2.4 Clinical Translation of BCI 38 4.2.4.1 Assistive Technology (AT) 38 4.2.4.2 Clinical Assessment 39 4.3 Current Body of Knowledge 39 4.4 Considerations for Pediatric BCI 40 4.4.1 Developmental Impact on EEG-based BCI 40 4.4.2 Hardware for Pediatric BCI 41 4.4.3 Signal Processing for Pediatric BCI 41 4.4.3.1 Feature Extraction, Selection and Classification 42 4.4.3.2 Emerging Techniques 42 4.4.4 Designing Experiments for Pediatric BCI 43 4.4.5 Meaningful Applications for Pediatric BCI 43 4.4.6 Clinical Translation of Pediatric BCI 44 4.5 Conclusions 44 References 45 5 Brain–Computer Interface-based Predator–Prey Drone Interactions 49 Abdelkader Nasreddine Belkacem and Abderrahmane Lakas 5.1 Introduction 49 5.2 Related Work 50 5.3 Predator–Prey Drone Interaction 51 5.4 Conclusion and Future Challenges 57 References 58 6 Levels of Cooperation in Human–Machine Systems: A Human–BCI–Robot Example 61 Marie-Pierre Pacaux-Lemoine, Lydia Habib, and Tom Carlson 6.1 Introduction 61 6.2 Levels of Cooperation 61 6.3 Application to the Control of a Robot by Thought 63 6.3.1 Designing the System 64 6.3.2 Experiments and Results 66 6.4 Results from the Methodological Point of View 67 6.5 Conclusion and Perspectives 68 References 69 7 Human–Machine Social Systems: Test and Validation via Military Use Cases 71 Charlene K. Stokes, Monika Lohani, Arwen H. DeCostanza, and Elliot Loh 7.1 Introduction 71 7.2 Background Summary: From Tools to Teammates 72 7.2.1 Two Sides of the Equation 72 7.2.2 Moving Beyond the Cognitive Revolution 73 7.2.2.1 A Rediscovery of the Unconscious 74 7.3 Future Research Directions 75 7.3.1 Machine: Functional Designs 75 7.3.2 Human: Ground Truth 76 7.3.2.1 Physiological Computing 76 7.3.3 Context: Tying It All Together 77 7.3.3.1 Training and Team Models 77 7.4 Conclusion 79 References 79 8 The Role of Multimodal Data for Modeling Communication in Artificial Social Agents 83 Stephanie Gross and Brigitte Krenn 8.1 Introduction 83 8.2 Background 84 8.2.1 Context 84 8.2.2 Basic Definitions 84 8.3 Related Work 84 8.3.1 HHI Data 85 8.3.2 HRI Data 85 8.3.2.1 Joint Attention and Robot Turn-Taking Capabilities 85 8.3.3 Public Availability of the Data 87 8.4 Datasets and Resulting Implications 87 8.4.1 Human Communicative Signals 87 8.4.1.1 Experimental Setup 87 8.4.1.2 Data Analysis and Results 88 8.4.2 Humans Reacting to Robot Signals 89 8.4.2.1 Comparing Different Robotic Turn-Giving Signals 89 8.4.2.2 Comparing Different Transparency Mechanisms 90 8.5 Conclusions 91 8.6 Future Research Challenges 91 References 91 9 Modeling Interactions Happening in People-Driven Collaborative Processes 95 Maximiliano Canche, Sergio F. Ochoa, Daniel Perovich, and Rodrigo Santos 9.1 Introduction 95 9.2 Background 97 9.3 State-of-the-Art in Interaction Modeling Languages and Notations 98 9.3.1 Visual Languages and Notations 99 9.3.2 Comparison of Interaction Modeling Languages and Notations 100 9.4 Challenges and Future Research Directions 101 References 102 10 Transparent Communications for Human–Machine Teaming 105 JessieY.C.Chen 10.1 Introduction 105 10.2 Definitions and Frameworks 105 10.3 Implementation of Transparent Human–Machine Interfaces in Intelligent Systems 106 10.3.1 Human–Robot Interaction 106 10.3.2 Multiagent Systems and Human–Swarm Interaction 108 10.3.3 Automated/Autonomous Driving 109 10.3.4 Explainable AI-Based Systems 109 10.3.5 Guidelines and Assessment Methods 109 10.4 Future Research Directions 110 References 111 11 Conversational Human–Machine Interfaces 115 María Jesús Rodríguez-Sánchez, Kawtar Benghazi, David Griol, and Zoraida Callejas 11.1 Introduction 115 11.2 Background 115 11.2.1 History of the Development of the Field 116 11.2.2 Basic Definitions 117 11.3 State-of-the-Art 117 11.3.1 Discussion of the Most Important Scientific/Technical Contributions 117 11.3.2 Comparison Table 119 11.4 Future Research Challenges 121 11.4.1 Current Research Issues 121 11.4.2 Future Research Directions Dealing with the Current Issues 121 References 122 12 Interaction-Centered Design: An Enduring Strategy and Methodology for Sociotechnical Systems 125 Ming Hou, Scott Fang, Wenbi Wang, and Philip S. E. Farrell 12.1 Introduction 125 12.2 Evolution of HMS Design Strategy 126 12.2.1 A HMS Technology: Intelligent Adaptive System 126 12.2.2 Evolution of IAS Design Strategy 128 12.3 State-of-the-Art: Interaction-Centered Design 130 12.3.1 A Generic Agent-based ICD Framework 130 12.3.2 IMPACTS: An Human–Machine Teaming Trust Model 132 12.3.3 ICD Roadmap for IAS Design and Development 133 12.3.4 ICD Validation, Adoption, and Contributions 134 12.4 IAS Design Challenges and Future Work 135 12.4.1 Challenges of HMS Technology 136 12.4.2 Future Work in IAS Design and Validation 136 References 137 13 Human–Machine Computing: Paradigm, Challenges, and Practices 141 Zhiwen Yu, Qingyang Li, and Bin Guo 13.1 Introduction 141 13.2 Background 142 13.2.1 History of the Development 142 13.2.2 Basic Definitions 143 13.3 State of the Art 144 13.3.1 Technical Contributions 144 13.3.2 Comparison Table 148 13.4 Future Research Challenges 150 13.4.1 Current Research Issues 150 13.4.2 Future Research Directions 151 References 152 14 Companion Technology 155 Andreas Wendemuth 14.1 Introduction 155 14.2 Background 155 14.2.1 History 156 14.2.2 Basic Definitions 157 14.3 State-of-the-Art 158 14.3.1 Discussion of the Most Important Scientific/Technical Contributions 159 14.4 Future Research Challenges 159 14.4.1 Current Research Issues 159 14.4.2 Future Research Directions Dealing with the Current Issues 160 References 161 15 A Survey on Rollator-Type Mobility Assistance Robots 165 Milad Geravand, Christian Werner, Klaus Hauer, and Angelika Peer 15.1 Introduction 165 15.2 Mobility Assistance Platforms 165 15.2.1 Actuation 166 15.2.2 Kinematics 166 15.2.2.1 Locomotion Support 166 15.2.2.2 STS Support 166 15.2.3 Sensors 168 15.2.4 Human–Machine Interfaces 168 15.3 Functionalities 168 15.3.1 STS Assistance 169 15.3.2 Walking Assistance 169 15.3.2.1 Maneuverability Improvement 169 15.3.2.2 Gravity Compensation 170 15.3.2.3 Obstacle Avoidance 170 15.3.2.4 Falls Risk Prediction and Fall Prevention 170 15.3.3 Localization and Navigation 170 15.3.3.1 Map Building and Localization 171 15.3.3.2 Path Planning 171 15.3.3.3 Assisted Localization 171 15.3.3.4 Assisted Navigation 171 15.3.4 Further Functionalities 171 15.3.4.1 Reminder Systems 171 15.3.4.2 Health Monitoring 171 15.3.4.3 Communication, Information, Entertainment, and Training 172 15.4 Conclusion 172 References 173 16 A Wearable Affective Robot 181 Jia Liu, Jinfeng Xu, Min Chen, and Iztok Humar 16.1 Introduction 181 16.2 Architecture Design and Characteristics 183 16.2.1 Architecture of a Wearable Affective Robot 183 16.2.2 Characteristics of a Wearable Affective Robot 184 16.3 Design of the Wearable, Affective Robot’s Hardware 185 16.3.1 AIWAC Box Hardware Design 185 16.3.2 Hardware Design of the EEG Acquisition 185 16.3.3 AIWAC Smart Tactile Device 185 16.3.4 Prototype of the Wearable Affective Robot 186 16.4 Algorithm for the Wearable Affective Robot 186 16.4.1 Algorithm for Affective Recognition 186 16.4.2 User-Behavior Perception based on a Brain-Wearable Device 186 16.5 Life Modeling of the Wearable Affective Robot 187 16.5.1 Data Set Labeling and Processing 188 16.5.2 Multidimensional Data Integration 188 16.5.3 Modeling of Associated Scenarios 188 16.6 Challenges and Prospects 189 16.6.1 Research Challenges of the Wearable Affective Robot 189 16.6.2 Application Scenarios for the Wearable Affective Robot 189 16.7 Conclusions 190 References 190 17 Visual Human–Computer Interactions for Intelligent Vehicles 193 Xumeng Wang, Wei Chen, and Fei-Yue Wang 17.1 Introduction 193 17.2 Background 193 17.3 State-of-the-Art 194 17.3.1 VHCI in Vehicles 194 17.3.1.1 Information Feedback from Intelligent Vehicles 195 17.3.1.2 Human-Guided Driving 195 17.3.2 VHCI Among Vehicles 195 17.3.3 VHCI Beyond Vehicles 195 17.4 Future Research Challenges 196 17.4.1 VHCI for Intelligent Vehicles 196 17.4.1.1 Vehicle Development 196 17.4.1.2 Vehicle Manufacture 197 17.4.1.3 Preference Recording 197 17.4.1.4 Vehicle Usage 197 17.4.2 VHCI for Intelligent Transportation Systems 198 17.4.2.1 Parallel World 198 17.4.2.2 The Framework of Intelligent Transportation Systems 198 References 199 18 Intelligent Collaboration Between Humans and Robots 203 Andrea Maria Zanchettin 18.1 Introduction 203 18.2 Background 203 18.2.1 Context 203 18.2.2 Basic Definitions 204 18.3 Related Work 205 18.4 Validation Cases 206 18.4.1 A Simple Verification Scenario 207 18.4.2 Activity Recognition Based on Semantic Hand-Object Interaction 208 18.5 Conclusions 210 18.6 Future Research Challenges 210 References 210 19 To Be Trustworthy and To Trust: The New Frontier of Intelligent Systems 213 Rino Falcone, Alessandro Sapienza, Filippo Cantucci, and Cristiano Castelfranchi 19.1 Introduction 213 19.2 Background 214 19.3 Basic Definitions 214 19.4 State-of-the-Art 215 19.4.1 Trust in Different Domains 215 19.4.2 Selected Articles 215 19.4.3 Differences in the Use of Trust 216 19.4.4 Approaches to Model Trust 217 19.4.5 Sources of Trust 218 19.4.6 Different Computational Models of Trust 218 19.5 Future Research Challenges 220 References 221 20 Decoding Humans’ and Virtual Agents’ Emotional Expressions 225 Terry Amorese, Gennaro Cordasco, Marialucia Cuciniello, Olga Shevaleva, Stefano Marrone, Carl Vogel, and Anna Esposito 20.1 Introduction 225 20.2 Related Work 226 20.3 Materials and Methodology 227 20.3.1 Participants 227 20.3.2 Stimuli 228 20.3.3 Tools and Procedures 228 20.4 Descriptive Statistics 229 20.5 Data Analysis and Results 230 20.5.1 Comparison Synthetic vs. Naturalistic Experiment 234 20.6 Discussion and Conclusions 235 Acknowledgment 238 References 238 21 Intelligent Computational Edge: From Pervasive Computing and Internet of Things to Computing Continuum 241 Radmila Juric 21.1 Introduction 241 21.2 The Journey of Pervasive Computing 242 21.3 The Power of the IoT 243 21.3.1 Inherent Problems with the IoT 244 21.4 IoT: The Journey from Cloud to Edge 245 21.5 Toward Intelligent Computational Edge 246 21.6 Is Computing Continuum the Answer? 247 21.7 Do We Have More Questions than Answers? 248 21.8 What Would our Vision Be? 249 References 251 22 Implementing Context Awareness in Autonomous Vehicles 257 Federico Faruffini, Alessandro Correa-Victorino, and Marie-Hélène Abel 22.1 Introduction 257 22.2 Background 258 22.2.1 Ontologies 258 22.2.2 Autonomous Driving 258 22.2.3 Basic Definitions 259 22.3 Related Works 260 22.4 Implementation and Tests 261 22.4.1 Implementing the Context of Navigation 261 22.4.2 Control Loop Rule 262 22.4.3 Simulations 263 22.5 Conclusions 264 22.6 Future Research Challenges 264 References 264 23 The Augmented Workforce: A Systematic Review of Operator Assistance Systems 267 Elisa Roth, Mirco Moencks, and Thomas Bohné 23.1 Introduction 267 23.2 Background 268 23.2.1 Definitions 268 23.3 State of the Art 269 23.3.1 Empirical Considerations 270 23.3.1.1 Application Areas 270 23.3.2 Assistance Capabilities 270 23.3.2.1 Task Guidance 271 23.3.2.2 Knowledge Management 271 23.3.2.3 Monitoring 273 23.3.2.4 Communication 273 23.3.2.5 Decision-Making 273 23.3.3 Meta-capabilities 274 23.3.3.1 Configuration Flexibility 274 23.3.3.2 Interoperability 274 23.3.3.3 Content Authoring 274 23.3.3.4 Initiation 274 23.3.3.5 Hardware 275 23.3.3.6 User Interfaces 275 23.4 Future Research Directions 275 23.4.1 Empirical Evidence 275 23.4.2 Collaborative Research 277 23.4.3 Systemic Approaches 277 23.4.4 Technology-Mediated Learning 277 23.5 Conclusion 277 References 278 24 Cognitive Performance Modeling 281 Maryam Zahabi and Junho Park 24.1 Introduction 281 24.2 Background 281 24.3 State-of-the-Art 282 24.4 Current Research Issues 286 24.5 Future Research Directions Dealing with the Current Issues 286 References 287 25 Advanced Driver Assistance Systems: Transparency and Driver Performance Effects 291 Yulin Deng and David B. Kaber 25.1 Introduction 291 25.2 Background 292 25.2.1 Context 292 25.2.2 Basic Definition 292 25.3 Related Work 293 25.4 Method 294 25.4.1 Apparatus 295 25.4.2 Participants 296 25.4.3 Experiment Design 296 25.4.4 Tasks 297 25.4.5 Dependent Variables 297 25.4.5.1 Hazard Negotiation Performance 297 25.4.5.2 Vehicle Control Performance 298 25.4.6 Procedure 298 25.5 Results 299 25.5.1 Hazard Reaction Performance 299 25.5.2 Posthazard Manual Driving Performance 299 25.5.3 Posttesting Usability Questionnaire 301 25.6 Discussion 302 25.7 Conclusion 303 25.8 Future Research 304 References 304 26 RGB-D Based Human Action Recognition: From Handcrafted to Deep Learning 307 Bangli Liu and Honghai Liu 26.1 Introduction 307 26.2 RGB-D Sensors and 3D Data 307 26.3 Human Action Recognition via Handcrafted Methods 308 26.3.1 Skeleton-Based Methods 308 26.3.2 Depth-Based Methods 309 26.3.3 Hybrid Feature-Based Methods 309 26.4 Human Action Recognition via Deep Learning Methods 310 26.4.1 CNN-Based Methods 310 26.4.2 RNN-Based Methods 311 26.4.3 GCN-Based Methods 313 26.5 Discussion 314 26.6 RGB-D Datasets 314 26.7 Conclusion and Future Directions 315 References 316 27 Hybrid Intelligence: Augmenting Employees’ Decision-Making with AI-Based Applications 321 Ina Heine, Thomas Hellebrandt, Louis Huebser, and Marcos Padrón 27.1 Introduction 321 27.2 Background 321 27.2.1 Context 321 27.2.2 Basic Definitions 322 27.3 Related Work 323 27.4 Technical Part of the Chapter 324 27.4.1 Description of the Use Case 324 27.4.1.1 Business Model 324 27.4.1.2 Process 324 27.4.1.3 Use Case Objectives 325 27.4.2 Description of the Envisioned Solution 325 27.4.3 Development Approach of AI Application 326 27.4.3.1 Development Process 326 27.4.3.2 Process Analysis and Time Study 326 27.4.3.3 Development and Deployment Data 327 27.4.3.4 System Testing and Deployment 327 27.4.3.5 Development Infrastructure and Development Cost Monitoring 327 27.5 Conclusions 330 27.6 Future Research Challenges 330 References 330 28 Human Factors in Driving 333 Birsen Donmez, Dengbo He, and Holland M. Vasquez 28.1 Introduction 333 28.2 Research Methodologies 334 28.3 In-Vehicle Electronic Devices 335 28.3.1 Distraction 335 28.3.2 Interaction Modality 336 28.3.2.1 Visual and Manual Modalities 336 28.3.2.2 Auditory and Vocal Modalities 337 28.3.2.3 Haptic Modality 338 28.3.3 Wearable Devices 338 28.4 Vehicle Automation 339 28.4.1 Driver Support Features 339 28.4.2 Automated Driving Features 341 28.5 Driver Monitoring Systems 342 28.6 Conclusion 343 References 343 29 Wearable Computing Systems: State-of-the-Art and Research Challenges 349 Giancarlo Fortino and Raffaele Gravina 29.1 Introduction 349 29.2 Wearable Devices 350 29.2.1 A History of Wearables 350 29.2.2 Sensor Types 351 29.2.2.1 Physiological Sensors 352 29.2.2.2 Inertial Sensors 352 29.2.2.3 Visual Sensors 352 29.2.2.4 Audio Sensors 355 29.2.2.5 Other Sensors 355 29.3 Body Sensor Networks-based Wearable Computing Systems 355 29.3.1 Body Sensor Networks 355 29.3.2 The SPINE Body-of-Knowledge 357 29.3.2.1 The SPINE Framework 357 29.3.2.2 The BodyCloud Framework 359 29.4 Applications of Wearable Devices and BSNs 360 29.4.1 Healthcare 360 29.4.1.1 Cardiovascular Disease 362 29.4.1.2 Parkinson’s Disease 362 29.4.1.3 Respiratory Disease 362 29.4.1.4 Diabetes 363 29.4.1.5 Rehabilitation 363 29.4.2 Fitness 363 29.4.2.1 Diet Monitoring 363 29.4.2.2 Activity/Fitness Tracker 363 29.4.3 Sports 364 29.4.4 Entertainment 364 29.5 Challenges and Prospects 364 29.5.1 Materials and Wearability 364 29.5.2 Power Supply 365 29.5.3 Security and Privacy 365 29.5.4 Communication 365 29.5.5 Embedded Computing, Development Methodologies, and Edge AI 365 29.6 Conclusions 365 Acknowledgment 366 References 366 30 Multisensor Wearable Device for Monitoring Vital Signs and Physical Activity 373 Joshua Di Tocco, Luigi Raiano, Daniela lo Presti, Carlo Massaroni, Domenico Formica, and Emiliano Schena 30.1 Introduction 373 30.2 Background 373 30.2.1 Context 373 30.2.2 Basic Definitions 374 30.3 Related Work 375 30.4 Case Study: Multisensor Wearable Device for Monitoring RR and Physical Activity 376 30.4.1 Wearable Device Description 376 30.4.1.1 Module for the Estimation of RR 377 30.4.1.2 Module for the Estimation of Physical Activity 377 30.4.2 Experimental Setup and Protocol 378 30.4.2.1 Experimental Setup 378 30.4.2.2 Experimental Protocol 378 30.4.3 Data Analysis 378 30.4.4 Results 378 30.5 Conclusions 379 30.6 Future Research Challenges 380 References 380 31 Integration of Machine Learning with Wearable Technologies 383 Darius Nahavandi, Roohallah Alizadehsani, and Abbas Khosravi 31.1 Introduction 383 31.2 Background 384 31.2.1 History of Wearables 384 31.2.2 Supervised Learning 384 31.2.3 Unsupervised Learning 386 31.2.4 Deep Learning 386 31.2.5 Deep Deterministic Policy Gradient 387 31.2.6 Cloud Computing 388 31.2.7 Edge Computing 388 31.3 State of the Art 389 31.4 Future Research Challenges 392 References 393 32 Gesture-Based Computing 397 Gennaro Costagliola, Mattia De Rosa, and Vittorio Fuccella 32.1 Introduction 397 32.2 Background 398 32.2.1 History of the Development of Gesture-Based Computing 398 32.2.2 Basic Definitions 399 32.3 State of the Art 399 32.4 Future Research Challenges 402 32.4.1 Current Research Issues 402 32.4.2 Future Research Directions Dealing with the Current Issues 403 Acknowledgment 403 References 403 33 EEG-based Affective Computing 409 Xueliang Quan and Dongrui Wu 33.1 Introduction 409 33.2 Background 409 33.2.1 Brief History 409 33.2.2 Emotion Theory 410 33.2.3 Emotion Representation 410 33.2.4 Eeg 410 33.2.5 EEG-Based Emotion Recognition 411 33.3 State-of-the-Art 411 33.3.1 Public Datasets 411 33.3.2 EEG Feature Extraction 411 33.3.3 Feature Fusion 412 33.3.4 Affective Computing Algorithms 413 33.3.4.1 Transfer Learning 413 33.3.4.2 Active Learning 413 33.3.4.3 Deep Learning 413 33.4 Challenges and Future Directions 414 Acknowledgment 415 References 415 34 Security of Human Machine Systems 419 Francesco Flammini, Emanuele Bellini, Maria Stella de Biase, and Stefano Marrone 34.1 Introduction 419 34.2 Background 420 34.2.1 An Historical Retrospective 420 34.2.2 Foundations of Security Theory 421 34.2.3 A Reference Model 421 34.3 State of the Art 422 34.3.1 Survey Methodology 422 34.3.2 Research Trends 425 34.4 Conclusions and Future Research 426 References 428 35 Integrating Innovation: The Role of Standards in Promoting Responsible Development of Human–Machine Systems 431 Zach McKinney, Martijn de Neeling, Luigi Bianchi, and Ricardo Chavarriaga 35.1 Introduction to Standards in Human–Machine Systems 431 35.1.1 What Are Standards? 431 35.1.2 Standards in Context: Technology Governance, Best Practice, and Soft Law 432 35.1.3 The Need for Standards in HMS 433 35.1.4 Benefits of Standards 433 35.1.5 What Makes an Effective Standard? 434 35.2 The HMS Standards Landscape 435 35.2.1 Standards in Neuroscience and Neurotechnology for Brain–Machine Interfaces 435 35.2.2 IEEE P2731 – Unified Terminology for BCI 435 35.2.2.1 The BCI Glossary 439 35.2.2.2 The BCI Functional Model 439 35.2.2.3 BCI Data Storage 439 35.2.3 IEEE P2794 – Reporting Standard for in vivo Neural Interface Research (RSNIR) 441 35.3 Standards Development Process 443 35.3.1 Who Can Participate in Standards Development? 443 35.3.2 Why Should I Participate in Standards Development? 444 35.3.3 How Can I get Involved in Standards Development? 444 35.4 Strategic Considerations and Discussion 444 35.4.1 Challenges to Development and Barriers to Adoption of Standards 444 35.4.2 Strategies to Promote Standards Development and Adoption 445 35.4.3 Final Perspective: On Innovation 445 Acknowledgements 446 References 446 36 Situation Awareness in Human-Machine Systems 451 Giuseppe D’Aniello and Matteo Gaeta 36.1 Introduction 451 36.2 Background 452 36.3 State-of-the-Art 453 36.3.1 Situation Identification Techniques in HMS 454 36.3.2 Situation Evolution in HMS 455 36.3.3 Situation-Aware Human Machine-Systems 455 36.4 Discussion and Research Challenges 456 36.5 Conclusion 458 References 458 37 Modeling, Analyzing, and Fostering the Adoption of New Technologies: The Case of Electric Vehicles 463 Valentina Breschi, Chiara Ravazzi, Silvia Strada, Fabrizio Dabbene, and Mara Tanelli 37.1 Introduction 463 37.2 Background 464 37.2.1 An Agent-based Model for EV Transition 464 37.2.2 Calibration Based on Real Mobility Patterns 466 37.3 Fostering the EV Transition via Control over Networks 468 37.3.1 Related Work: A Perspective Analysis 468 37.3.2 A New Model for EV Transition with Incentive Policies 469 37.3.2.1 Modeling Time-varying Thresholds 469 37.3.2.2 Calibration of the Model 470 37.4 Boosting EV Adoption with Feedback 470 37.4.1 Formulation of the Optimal Control Problem 470 37.4.2 Derivation of the Optimal Policies 471 37.4.3 A Receding Horizon Strategy to Boost EV Adoption 472 37.5 Experimental Results 473 37.6 Conclusions 476 37.7 Future Research Challenges 477 Acknowlegments 477 References 477 Index 479

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Book SynopsisFACTORIES OF THE FUTURE The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability. Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like. The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, pTable of ContentsPreface xiii 1 Factories of the Future 1 Talwinder Singh and Davinder Singh 1.0 Introduction 2 1.1 Factory of the Future 3 1.1.1 Plant Structure 3 1.1.2 Plant Digitization 4 1.1.3 Plant Processes 4 1.1.4 Industry of the Future: A Fully Integrated Industry 5 1.2 Current Manufacturing Environment 6 1.3 Driving Technologies and Market Readiness 8 1.4 Connected Factory, Smart Factory, and Smart Manufacturing 11 1.4.1 Potential Benefits of a Connected Factory 13 1.5 Digital and Virtual Factory 13 1.5.1 Digital Factory 13 1.5.2 Virtual Factory 14 1.6 Advanced Manufacturing Technologies 14 1.6.1 Advantages of Advanced Manufacturing Technologies 16 1.7 Role of Factories of the Future (FoF) in Manufacturing Performance 17 1.8 Socio-Econo-Techno Justification of Factories of the Future 17 References 18 2 Industry 5.0 21 Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh 2.1 Introduction 22 2.1.1 Industry 5.0 for Manufacturing 22 2.1.1.1 Industrial Revolutions 23 2.1.2 Real Personalization in Industry 5.0 25 2.1.3 Industry 5.0 for Human Workers 28 2.2 Individualized Human-Machine-Interaction 29 2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 31 2.4 Concerns in Industry 5.0 32 2.5 Humans Closer to the Design Process of Manufacturing 35 2.5.1 Enablers of Industry 5.0 36 2.6 Challenges and Enablers (Socio-Econo-Techno Justification) 37 2.6.1 Social Dimension 37 2.6.2 Governmental and Political Dimension 38 2.6.3 Interdisciplinarity 40 2.6.4 Economic Dimension 40 2.6.5 Scalability 41 2.7 Concluding Remarks 42 References 43 3 Machine Learning – A Survey 47 Navdeep Singh and Aanchal Goyal 3.1 Introduction 48 3.2 Machine Learning 49 3.2.1 Unsupervised Machine Learning 50 3.2.2 Variety of Unsupervised Learning 51 3.2.3 Supervised Machine Learning 52 3.2.4 Categories of Supervised Learning 54 3.3 Reinforcement Machine Learning 54 3.3.1 Applications of Reinforcement Learning 56 3.3.2 Dimensionality Reduction 57 3.4 Importance of Dimensionality Reduction in Machine Learning 58 3.4.1 Methods of Dimensionality Reduction 58 3.4.1.1 Principal Component Analysis (PCA) 58 3.4.1.2 Linear Discriminant Analysis (LDA) 59 3.4.1.3 Generalized Discriminant Analysis (GDA) 61 3.5 Distance Measures 61 3.6 Clustering 65 3.6.1 Algorithms in Clustering 67 3.6.2 Applications of Clustering 68 3.6.3 Iterative Distance-Based Clustering 69 3.7 Hierarchical Model 70 3.8 Density-Based Clustering 72 3.8.1 Dbscan 72 3.8.2 Optics 73 3.9 Role of Machine Learning in Factories of the Future 74 3.10 Identification of the Probable Customers 75 3.11 Conclusion 78 References 79 4 Understanding Neural Networks 83 Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur 4.1 Introduction 83 4.2 Components of Neural Networks 84 4.2.1 Neurons 85 4.2.2 Synapses and Weights 86 4.2.3 Bias 86 4.2.4 Architecture of Neural Networks 86 4.2.5 How Do Neural Networks Work? 87 4.2.6 Types of Neural Networks 88 4.2.6.1 Artificial Neural Network (ANN) 88 4.2.6.2 Recurrent Neural Network (RNN) 89 4.2.6.3 Convolutional Neural Network (CNN) 89 4.2.7 Learning Techniques in Neural Network 90 4.2.8 Applications of Neural Network 90 4.2.9 Advantages of Neural Networks 91 4.2.10 Disadvantages of Neural Network 91 4.2.11 Limitations of Neural Networks 92 4.3 Back-Propagation 92 4.3.1 Working of Back-Propagation 92 4.3.2 Types of Back-Propagation 93 4.3.2.1 Static Back-Propagation 93 4.3.2.2 Recurrent Back-Propagation 93 4.3.2.3 Advantages of Back-Propagation 94 4.3.2.4 Disadvantages of Back-Propagation 94 4.4 Activation Function (AF) 94 4.4.1 Sigmoid Active Function 94 4.4.1.1 Advantages 95 4.4.1.2 Disadvantages 95 4.4.2 RELU Activation Function 95 4.4.2.1 Advantages 96 4.4.2.2 Disadvantages 96 4.4.3 TANH Active Function 96 4.4.3.1 Advantages 97 4.4.3.2 Disadvantages 97 4.4.4 Linear Function 97 4.4.5 Advantages 98 4.4.6 Disadvantages 98 4.4.7 Softmax Function 98 4.4.8 Advantages 98 4.5 Comparison of Activation Functions 98 4.6 Machine Learning 99 4.6.1 Applications of Machine Learning 100 4.7 Conclusion 100 References 101 5 Intelligent Machining 103 Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak 5.1 Introduction 104 5.2 Requirements for the Developments of Intelligent Machining 104 5.3 Components of Intelligent Machining 105 5.3.1 Intelligent Sensors 106 5.3.1.1 Features of Intelligent Sensors 106 5.3.1.2 Functions of Intelligent Sensors 107 5.3.1.3 Data Acquisition and Management System to Process and Store Signals 111 5.3.2 Machine Learning and Knowledge Discovery Component 113 5.3.3 Database Knowledge Discovery 114 5.3.4 Programmable Logical Controller (PLC) 115 5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing 117 5.3.6 Information Integration via Knowledge Graphs 118 5.4 Conclusion 119 References 120 6 Advanced Maintenance and Reliability 121 Davinder Singh and Talwinder Singh 6.1 Introduction 121 6.2 Condition-Based Maintenance 122 6.3 Computerized Maintenance Management Systems (CMMS) 124 6.4 Preventive Maintenance (PM) 127 6.5 Predictive Maintenance (PdM) 128 6.6 Reliability Centered Maintenance (RCM) 129 6.6.1 RCM Principles 130 6.7 Condition Monitoring and Residual Life Prediction 131 6.8 Sustainability 133 6.8.1 Role of Sustainability in Manufacturing 134 6.9 Concluding Remarks 135 References 136 7 Digital Manufacturing 143 Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak 7.1 Introduction 144 7.2 Product Life Cycle and Transition 146 7.3 Digital Thread 148 7.4 Digital Manufacturing Security 150 7.5 Role of Digital Manufacturing in Future Factories 151 7.6 Digital Manufacturing and CNC Machining 152 7.6.1 Introduction to CNC Machining 152 7.6.2 Equipment’s Used in CNC Machining 153 7.6.3 Analyzing Digital Manufacturing Design Considerations 153 7.6.4 Finishing of Part After Machining 153 7.7 Additive Manufacturing 154 7.7.1 Objective of Additive Manufacturing 155 7.7.2 Design Consideration 155 7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries 155 7.9 Conclusion 156 References 157 8 Artificial Intelligence in Machine Learning 161 Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh 8.1 Introduction 162 8.2 Case Studies 162 8.3 Advantages of A.I. in ml 164 8.4 Artificial Intelligence – Basics 166 8.4.1 History of A.I. 166 8.4.2 Limitations of Human Mind 166 8.4.3 Real Artificial Intelligence 166 8.4.4 Artificial Intelligence Subfields 167 8.4.5 The Positives of A.I. 167 8.4.6 Machine Learning 168 8.4.7 Machine Learning Models 168 8.4.8 Neural Networks 169 8.4.9 Constraints of Machine Learning 170 8.4.10 Different Kinds of Machine Learning 171 8.5 Application of Artificial Intelligence 171 8.5.1 Expert Systems 172 8.5.2 Natural Language Processing 172 8.5.3 Speech Recognition 172 8.5.4 Computer Vision 172 8.5.5 Robotics 172 8.6 Neural Networks (N.N.) Basics 173 8.6.1 Application of Neural Networks 173 8.6.2 Architecture of Neural Networks 173 8.6.3 Working of Artificial Neural Networks 175 8.7 Convolution Neural Networks 176 8.7.1 Working of Convolutional Neural Networks 176 8.7.2 Overview of CNN 181 8.7.3 Working of CNN 181 8.8 Image Classification 182 8.8.1 Concept of Image Classification 182 8.8.2 Type of Learning 182 8.8.3 Features of Image Classification 183 8.8.4 Examples of Image Classification 183 8.9 Text Classification 183 8.9.1 Text Classification Examples 183 8.9.2 Phases of Text Classification 184 8.9.3 Text Classification API 186 8.10 Recurrent Neural Network 186 8.10.1 Type of Recurrent Neural Network 187 8.11 Building Recurrent Neural Network 187 8.12 Long Short Term Memory Networks (LSTMs) 190 References 193 9 Internet of Things 195 Davinder Singh 9.1 Introduction 195 9.2 M2M and Web of Things 198 9.3 Wireless Networks 199 9.4 Service Oriented Architecture 203 9.5 Complexity of Networks 205 9.6 Wireless Sensor Networks 205 9.7 Cloud Computing 207 9.8 Cloud Simulators 211 9.9 Fog Computing 214 9.10 Applications of IoT 217 9.11 Research Gaps and Challenges in IoT 220 9.12 Concluding Remarks 223 References 224 10 Product Life Cycle 229 Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj 10.1 Introduction 230 10.2 Product Lifecycle Management (PLM) 230 10.2.1 Why Product Lifecycle Management? 231 10.2.2 Biological Product Lifecycle Stages 231 10.2.3 An Example Related to Stages in Product Lifecycle Management 233 10.2.4 Advanced Stages in Product Lifecycle Management 234 10.2.5 Strategies of Product Lifecycle Management 235 10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies 236 10.3.1 Considerations in High and Low-Level Pricing 236 10.3.2 Penetration Pricing Strategy 236 10.3.3 Example for Penetration Pricing Strategy 237 10.3.4 Considerations in Penetration Pricing 237 10.4 How Do Product Lifecycle Management Work? 240 10.5 Application Process of Product Lifecycle Management (plm) 241 10.6 Role of Unified Modelling Language (UML) 242 10.6.1 UML Activity Diagrams 243 10.7 Management of Product Information Throughout the Entire Product Lifecycle 244 10.8 PDM System in an Organization 245 10.8.1 Benefits of PDM 245 10.8.2 How Does the PDM Work? 245 10.8.3 The Services of Product Data Management 246 10.9 System Architecture 247 10.9.1 Process of System Architecture 248 10.10 Concepts of Model-Based System Engineering (MBSE) 250 10.10.1 Benefits of Model-Based System Engineering (mbse) 251 10.11 Challenges of Post-COVID 19 in Manufacturing Sector 251 10.12 Recent Updates in Product Life Cycle 252 10.13 Conclusion 253 References 254 11 Case Studies 257 Chandan Deep Singh and Harleen Kaur 11.1 Case Study in a Two-Wheeler Manufacturing Industry 258 11.1.1 Company Strategy 258 11.1.2 Initiatives Towards Technological Advancement 262 11.1.3 Management Initiatives 263 11.1.4 Sustainable Development Goals 265 11.1.5 Growth Framework with Customer Needs 269 11.1.6 Vision for the Future 270 11.2 Case Study in a Four-Wheeler Manufacturing Unit 271 11.2.1 Company Principles 271 11.2.2 Company Objectives 271 11.2.3 Company Strategy and Business Initiatives 272 11.2.4 Technology Initiatives 272 11.2.5 Management Initiatives 273 11.2.6 Quality 275 11.2.7 Sustainable Development Goals 276 11.2.8 Future Plan of Action 280 11.3 Conclusions 281 11.3.1 Limitations 282 11.3.2 Suggestions for Future Work 282 Index 285

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Book SynopsisINTEGRATION OF MECHANICAL AND MANUFACTURING ENGINEERING WITH IOT The book provides researchers, professionals, and students with a resource on the basic principles of IoT and its applications, as well as a guide to practicing engineers who want to understand how the Internet of Things can be implemented for different fields of mechanical and manufacturing engineering. This book broadly explores the latest developments of IoT and its integration into mechanical and manufacturing engineering. It details the fundamental concepts and recent developments in IoT & Industry 4.0 with special emphasis on the mechanical engineering platform for such issues as product development and manufacturing, environmental monitoring, automotive applications, energy management, and renewable energy sectors. Topics and related concepts are portrayed comprehensively so that readers can develop expertise and knowledge in the field of IoT. It is packed with reference tables and schematic diaTable of ContentsPreface xvii 1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems 1 Vaishnavi Vadivelu, Moganapriya Chinnasamy, Manivannan Rajendran, Hari Chandrasekaran and Rajasekar Rathanasamy 1.1 Introduction 2 1.2 IoT Revolution 2 1.3 IoT 4 1.4 Fundamental Technologies 5 1.4.1 RFID and NFC 5 1.4.2 Wsn 6 1.4.3 Data Storage and Analytics (DSA) 6 1.5 IoT Architecture 6 1.6 Cloud Computing (CC) and IoT 7 1.6.1 Service of cc 8 1.6.2 Integration of IoT With cc 10 1.7 Edge Computing (EC) and IoT 10 1.7.1 EC with IoT Architecture 11 1.8 Applications of IoT 12 1.8.1 Smart Mobility 12 1.8.2 Smart Grid 14 1.8.3 Smart Home System 14 1.8.4 Public Safety and Environment Monitoring 15 1.8.5 Smart Healthcare Systems 15 1.8.6 Smart Agriculture System 16 1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems 17 1.9.1 Five-Stage Process of IoT for Design and Manufacturing System 19 1.9.2 IoT Architecture for Advanced Manufacturing Technologies 21 1.9.3 Architecture Development 22 1.10 Current Issues and Challenges in IoT 24 1.10.1 Scalability 25 1.10.2 Issue of Trust 25 1.10.3 Service Availability 26 1.10.4 Security Challenges 26 1.10.5 Mobility Issues 27 1.10.6 Architecture for IoT 27 1.11 Conclusion 28 References 29 2 Fourth Industrial Revolution: Industry 4.0 41 Maheswari Rajamanickam, Elizabeth Nirmala John Gerard Royan, Gowtham Ramaswamy, Manivannan Rajendran and Vaishnavi Vadivelu 2.1 Introduction 42 2.1.1 Global Level Adaption 42 2.2 Evolution of Industry 44 2.2.1 Industry 1.0 44 2.2.2 Industry 2.0 44 2.2.3 Industry 3.0 44 2.2.4 Industry 4.0 (or) I4. 0 44 2.3 Basic IoT Concepts and the Term Glossary 45 2.4 Industrial Revolution 47 2.4.1 I4.0 Core Idea 47 2.4.2 Origin of I4.0 Concept 48 2.5 Industry 49 2.5.1 Manufacturing Phases 49 2.5.2 Existing Process Planning vs. I4. 0 50 2.5.3 Software for Product Planning—A Link Between Smart Products and the Main System ERP 52 2.6 Industry Production System 4.0 (Smart Factory) 56 2.6.1 IT Support 58 2.7 I4.0 in Functional Field 60 2.7.1 I4.0 Logistics 60 2.7.2 Resource Planning 60 2.7.3 Systems for Warehouse Management 61 2.7.4 Transportation Management Systems 61 2.7.5 Transportation Systems with Intelligence 63 2.7.6 Information Security 64 2.8 Existing Technology in I4. 0 65 2.8.1 Applications of I4.0 in Existing Industries 65 2.8.2 Additive Manufacturing (AM) 66 2.8.3 Intelligent Machines 66 2.8.4 Robots that are Self-Aware 66 2.8.5 Materials that are Smart 67 2.8.6 IoT 67 2.8.7 The Internet of Things in Industry (IIoT) 67 2.8.8 Sensors that are Smart 67 2.8.9 System Using a Smart Programmable Logic Controller (PLC) 67 2.8.10 Software 68 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) 68 2.8.12 Gateway for the Internet of Things 68 2.8.13 Cloud 68 2.8.14 Applications of Additive Manufacturing in I4. 0 68 2.8.15 Artificial Intelligence (AI) 69 2.9 Applications in Current Industries 69 2.9.1 I4.0 in Logistics 69 2.9.2 I4.0 in Manufacturing Operation 70 2.10 Future Scope of Research 73 2.10.1 Theoretical Framework of I4. 0 73 2.11 Discussion and Implications 75 2.11.1 Hosting: Microsoft 75 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens 76 2.11.3 A Systematic Computational Analysis 76 2.11.4 Festo Proximity Sensor 77 2.11.5 Connectivity Hardware: HMS 77 2.11.6 IT Security: Claroty 77 2.11.7 Accenture Is a Systems Integrator 77 2.11.8 Additive Manufacturing: General Electric 78 2.11.9 Augmented and Virtual Reality: Upskill 78 2.11.10 ABB Collaborative Robots 78 2.11.11 Connected Vision System: Cognex 78 2.11.12 Drones/UAVs: PINC 79 2.11.13 Self-Driving in Vehicles: Clear Path Robotics 79 2.12 Conclusion 79 References 80 3 Interaction of Internet of Things and Sensors for Machining 85 Manivannan Rajendran, Kamesh Nagarajan, Vaishnavi Vadivelu, Harikrishna Kumar Mohankumar and Sathish Kumar Palaniappan 3.1 Introduction 86 3.2 Various Sensors Involved in Machining Process 88 3.2.1 Direct Method Sensors 89 3.2.2 Indirect Method Sensors 89 3.2.3 Dynamometer 90 3.2.4 Accelerometer 91 3.2.5 Acoustic Emission Sensor 93 3.2.6 Current Sensors 94 3.3 Other Sensors 94 3.3.1 Temperature Sensors 94 3.3.2 Optical Sensors 95 3.4 Interaction of Sensors During Machining Operation 96 3.4.1 Milling Machining 96 3.4.2 Turning Machining 97 3.4.3 Drilling Machining Operation 98 3.5 Sensor Fusion Technique 99 3.6 Interaction of Internet of Things 100 3.6.1 Identification 100 3.6.2 Sensing 101 3.6.3 Communication 101 3.6.4 Computation 101 3.6.5 Services 101 3.6.6 Semantics 101 3.7 IoT Technologies in Manufacturing Process 102 3.7.1 IoT Challenges 102 3.7.2 IoT-Based Energy Monitoring System 102 3.8 Industrial Application 104 3.8.1 Integrated Structure 104 3.8.2 Monitoring the System Related to Service Based on Internet of Things 106 3.9 Decision Making Methods 107 3.9.1 Artificial Neural Network 107 3.9.2 Fuzzy Inference System 108 3.9.3 Support Vector Mechanism 108 3.9.4 Decision Trees and Random Forest 109 3.9.5 Convolutional Neural Network 109 3.10 Conclusion 111 References 111 4 Application of Internet of Things (IoT) in the Automotive Industry 115 Solomon Jenoris Muthiya, Shridhar Anaimuthu, Joshuva Arockia Dhanraj, Nandakumar Selvaraju, Gutha Manikanta and C. Dineshkumar 4.1 Introduction 116 4.2 Need For IoT in Automobile Field 118 4.3 Fault Diagnosis in Automobile 119 4.4 Automobile Security and Surveillance System in IoT-Based 123 4.5 A Vehicle Communications 125 4.6 The Smart Vehicle 126 4.7 Connected Vehicles 128 4.7.1 Vehicle-to-Vehicle (V2V) Communications 130 4.7.2 Vehicle-to-Infrastructure (V2I) Communications 131 4.7.3 Vehicle-to-Pedestrian (V2P) Communications 132 4.7.4 Vehicle to Network (V2N) Communication 133 4.7.5 Vehicle to Cloud (V2C) Communication 134 4.7.6 Vehicle to Device (V2D) Communication 134 4.7.7 Vehicle to Grid (V2G) Communications 135 4.8 Conclusion 135 References 136 5 IoT for Food and Beverage Manufacturing 141 Manju Sri Anbupalani, Gobinath Velu Kaliyannan and Santhosh Sivaraj 5.1 Introduction 142 5.2 The Influence of IoT in a Food Industry 143 5.2.1 Management 143 5.2.2 Workers 143 5.2.3 Data 143 5.2.4 It 143 5.3 A Brief Review of IoT’s Involvement in the Food Industry 144 5.4 Challenges to the Food Industry and Role of IoT 144 5.4.1 Handling and Sorting Complex Data 144 5.4.2 A Retiring Skilled Workforce 145 5.4.3 Alternatives for Supply Chain Management 145 5.4.4 Implementation of IoT in Food and Beverage Manufacturing 145 5.4.5 Pilot 145 5.4.6 Plan 146 5.4.7 Proliferate 146 5.5 Applications of IoT in a Food Industry 146 5.5.1 IoT for Handling of Raw Material and Inventory Control 146 5.5.2 Factory Operations and Machine Conditions Using IoT 146 5.5.3 Quality Control With the IoT 147 5.5.4 IoT for Safety 147 5.5.5 The Internet of Things and Sustainability 147 5.5.6 IoT for Product Delivery and Packaging 147 5.5.7 IoT for Vehicle Optimization 147 5.5.8 IoT-Based Water Monitoring Architecture in the Food and Beverage Industry 148 5.6 A FW Tracking System Methodology Based on IoT 150 5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System 150 5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System 152 5.9 Hardware Design: Intelligent Scale 152 5.10 Software Design 153 References 157 6 Opportunities: Machine Learning for Industrial IoT Applications 159 Poongodi C., Sayeekumar M., Meenakshi C. and Hari Prasath K. 6.1 Introduction 160 6.2 I-IoT Applications 163 6.3 Machine Learning Algorithms for Industrial IoT 170 6.3.1 Supervised Learning 171 6.3.2 Semisupervised Learning 173 6.3.3 Unsupervised Learning 173 6.3.4 Reinforcement Learning 175 6.3.5 The Most Common and Popular Machine Learning Algorithms 176 6.4 I-IoT Data Analytics 177 6.4.1 Tools for IoT Analytics 177 6.4.2 Choosing the Right IoT Data Analytics Platforms 184 6.5 Conclusion 185 References 186 7 Role of IoT in Industry Predictive Maintenance 191 Gobinath Velu Kaliyannan, Manju Sri Anbupalani, Suganeswaran Kandasamy, Santhosh Sivaraj and Raja Gunasekaran 7.1 Introduction 192 7.2 Predictive Maintenance 194 7.3 IPdM Systems Framework and Few Key Methodologies 196 7.3.1 Detection and Collection of Data 196 7.3.2 Initial Processing of Collected Data 196 7.3.3 Modeling as Per Requirement 197 7.3.4 Influential Parameters 198 7.3.5 Identification of Best Working Path 198 7.3.6 Modifying Output with Respect Sensed Input 198 7.4 Economics of PdM 198 7.5 PdM for Production and Product 200 7.6 Implementation of IPdM 202 7.6.1 Manufacturing with Zero Defects 202 7.6.2 Sense of the Windsene INDSENSE 202 7.7 Case Studies 202 7.7.1 Area 1—Heavy Ash Evacuation 203 7.7.2 Area 2—Seawater Pumps 203 7.7.3 Evaporators 204 7.7.4 System Deployment Considerations in General 205 7.8 Automotive Industry—Integrated IoT 205 7.8.1 Navigation Aspect 205 7.8.2 Continual Working of Toll Booth 206 7.8.3 Theft Security System 206 7.8.4 Black Box–Enabled IoT 206 7.8.5 Regularizing Motion of Emergency Vehicle 207 7.8.6 Pollution Monitoring System 207 7.8.7 Timely Assessment of Driver’s Condition 207 7.8.8 Vehicle Performance Monitoring 207 7.9 Conclusion 208 References 208 8 Role of IoT in Product Development 215 Bhuvanesh Kumar M., Balaji N. S., Senthil S. M. and Sathiya P. 8.1 Introduction 216 8.1.1 Industry 4.0 217 8.2 Need to Understand the Product Architecture 220 8.3 Product Development Process 222 8.3.1 Criteria to Classify the New Products 223 8.3.2 Product Configuration 224 8.3.3 Challenges in Product Development while Developing IoT Products (Data-Driven Product Development) 225 8.3.4 Role of IoT in Product Development for Industrial Applications 226 8.3.5 Impacts and Future Perspectives of IoT in Product Development 229 8.4 Conclusion 231 References 232 9 Benefits of IoT in Automated Systems 235 Adithya K. and Girimurugan R. 9.1 Introduction 235 9.2 Benefits of Automation 236 9.2.1 Improved Productivity 236 9.2.2 Efficient Operation Management 236 9.2.3 Better Use of Resources 237 9.2.4 Cost-Effective Operation 237 9.2.5 Improved Work Safety 237 9.2.6 Software Bots 237 9.2.7 Enhanced Public Sector Operations 237 9.2.8 Healthcare Benefits 238 9.3 Smart City Automation 238 9.3.1 Smart Agriculture 240 9.3.2 Smart City Services 240 9.3.3 Smart Energy 240 9.3.4 Smart Health 241 9.3.5 Smart Home 241 9.3.6 Smart Industry 242 9.3.7 Smart Infrastructure 242 9.3.8 Smart Transport 242 9.4 Smart Home Automation 243 9.5 Automation in Manufacturing 247 9.5.1 IoT Manufacturing Use Cases 249 9.5.2 Foundation for IoT in Manufacturing 251 9.6 Healthcare Automation 253 9.6.1 IoT in Healthcare Applications 254 9.6.2 Architecture for IoT-Healthcare Applications 257 9.6.3 Challenges and Solutions 258 9.7 Industrial Automation 259 9.7.1 IoT in Industrial Automation 260 9.7.2 The Essentials of an Industrial IoT Solution 260 9.7.3 Practical Industrial IoT Examples for Daily Use 261 9.8 Automation in Air Pollution Monitoring 265 9.8.1 Methodology 266 9.8.2 Working Principle 267 9.8.3 Results 267 9.9 Irrigation Automation 268 References 269 10 Integration of IoT in Energy Management 271 Ganesh Angappan, Santhosh Sivaraj, Premkumar Bhuvaneshwaran, Mugilan Thanigachalam, Sarath Sekar and Rajasekar Rathanasamy 10.1 Introduction 272 10.2 Energy Management Integration with IoT in Industry 4.0 274 10.3 IoT in Energy Sector 276 10.3.1 Energy Generation 276 10.3.2 Smart Cities 277 10.3.3 Smart Grid 277 10.3.4 Smart Buildings 278 10.3.5 IoT in the Energy Industry 279 10.3.6 Intelligent Transportation 280 10.4 Provocations in the IoT Applications 281 10.4.1 Energy Consumption 281 10.4.2 Subsystems and IoT Integration 282 10.5 Energy Generation 284 10.5.1 Conversion of Mechanical Energy 285 10.5.2 Aeroelastic Energy Harvesting 290 10.5.3 Solar Energy Harvesting 292 10.5.4 Sound Energy Harvesting 292 10.5.5 Wind Energy Harvesting 292 10.5.6 Radiofrequency Energy Harvesting 293 10.5.7 Thermal Energy 293 10.6 Conclusion 294 References 294 11 Role of IoT in the Renewable Energy Sector 305 Veerakumar Chinnasamy and Honghyun Cho 11.1 Introduction 305 11.2 Internet of Things (IoT) 306 11.3 IoT in the Renewable Energy Sector 307 11.3.1 Automation of Energy Generation 307 11.3.2 Smart Grids 309 11.3.3 IoT Increases the Renewable Energy Use 312 11.3.4 Consumer Contribution 312 11.3.5 Balancing Supply and Demand 313 11.3.6 Smart Buildings 313 11.3.7 Smart Cities 314 11.3.8 Cost-Effectiveness 314 11.4 Data Analytics 314 11.4.1 Data Forecasting 314 11.4.2 Safety and Reliability 315 11.5 Conclusion 315 References 315 Index 317

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Book SynopsisMACHINE INTELLIGENCE, BIG DATA ANALYTICS, AND IoT IN IMAGE PROCESSING Discusses both theoretical and practical aspects of how to harness advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, farming solutions, and robotics used in automation. The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, Table of ContentsPreface xv Part I: Demystifying Smart Healthcare 1 1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer’s Disease 3 Monika Sethi, Sachin Ahuja and Puneet Bawa 1.1 Introduction 4 1.2 Transfer Learning Techniques 6 1.3 AD Classification Using Conventional Training Methods 9 1.4 AD Classification Using Transfer Learning 12 1.5 Conclusion 16 References 16 2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques 23 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 2.1 Introduction 24 2.2 The Major Contributions of the Proposed Model 26 2.3 Related Works 28 2.4 Problem Statement 32 2.5 Proposed Model 33 2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis 33 2.5.2 Deep Learning with PSO 34 2.5.3 Proposed CNN Architectures 35 2.6 Dataset Description 37 2.7 Results and Discussions 39 2.7.1 Parameters for Performance Evaluation 39 2.8 Conclusion 47 References 48 3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques 51 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 3.1 Introduction 52 3.1.1 Liver Roles in Human Body 53 3.1.2 Liver Diseases 53 3.1.3 Types of Liver Tumors 55 3.1.3.1 Benign Tumors 55 3.1.3.2 Malignant Tumors 57 3.1.4 Characteristics of a Medical Imaging Procedure 58 3.1.5 Problems Related to Liver Cancer Classification 60 3.1.6 Purpose of the Systematic Study 61 3.2 Related Works 62 3.3 Proposed Methodology 66 3.3.1 Gaussian Mixture Model 68 3.3.2 Dataset Description 69 3.3.3 Performance Metrics 70 3.3.3.1 Accuracy Measures 70 3.3.3.2 Key Findings 74 3.3.3.3 Key Issues Addressed 75 3.4 Conclusion 77 References 77 4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic 81 Garima Kohli and Kumar Gourav 4.1 Introduction 82 4.2 Digital Technologies Used 84 4.2.1 Artificial Intelligence 85 4.2.2 Internet of Things 85 4.2.3 Telehealth/Telemedicine 87 4.2.4 Cloud Computing 87 4.2.5 Blockchain 88 4.2.6 5g 89 4.3 Challenges in Transforming Digital Technology 90 4.3.1 Increasing Digitalization 91 4.3.2 Work From Home Culture 91 4.3.3 Workplace Monitoring and Techno Stress 91 4.3.4 Online Fraud 92 4.3.5 Accessing Internet 92 4.3.6 Internet Shutdowns 92 4.3.7 Digital Payments 92 4.3.8 Privacy and Surveillance 93 4.4 Implications for Research 93 4.5 Conclusion 94 References 95 Part II: Plant Pathology 101 5 Plant Pathology Detection Using Deep Learning 103 Sangeeta V., Appala S. Muttipati and Brahmaji Godi 5.1 Introduction 104 5.2 Plant Leaf Disease 105 5.3 Background Knowledge 109 5.4 Architecture of ResNet 512 V 2 111 5.4.1 Working of Residual Network 112 5.5 Methodology 113 5.5.1 Image Resizing 113 5.5.2 Data Augmentation 113 5.5.2.1 Types of Data Augmentation 114 5.5.3 Data Normalization 114 5.5.4 Data Splitting 116 5.6 Result Analysis 116 5.6.1 Data Collection 117 5.6.2 Feature Extractions 117 5.6.3 Plant Leaf Disease Detection 117 5.7 Conclusion 119 References 120 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT 123 N. Marline Joys Kumari, N. Thirupathi Rao and Debnath Bhattacharyya 6.1 Introduction 124 6.1.1 Background of the Problem 127 6.1.1.1 Need of Water Management 127 6.1.1.2 Importance of Precision Agriculture 127 6.1.1.3 Internet of Things 128 6.1.1.4 Application of IoT in Machine Learning and Deep Learning 129 6.2 Related Works 131 6.3 Challenges of IoT in Smart Irrigation 133 6.4 Farmers’ Challenges in the Current Situation 135 6.5 Data Collection in Precision Agriculture 136 6.5.1 Algorithm 136 6.5.1.1 Environmental Consideration on Stage Production of Crop 140 6.5.2 Implementation Measures 141 6.5.2.1 Analysis of Relevant Vectors 141 6.5.2.2 Mean Square Error 141 6.5.2.3 Potential of IoT in Precision Agriculture 141 6.5.3 Architecture of the Proposed Model 143 6.6 Conclusion 147 References 147 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction 151 Haneet Kour, Vaishali Pandith, Jatinder Manhas and Vinod Sharma 7.1 Introduction 152 7.2 Related Work 153 7.3 Materials and Methods 155 7.3.1 Methodology for the Current Work 155 7.3.1.1 Data Collection for Wheat Crop 155 7.3.1.2 Data Pre-Processing 156 7.3.1.3 Implementation of the Proposed Hybrid Model 157 7.3.2 Techniques Used for Feature Selection 159 7.3.2.1 ReliefF Algorithm 159 7.3.2.2 Genetic Algorithm 161 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction 162 7.3.3.1 K-Nearest Neighbor 162 7.3.3.2 Artificial Neural Network 163 7.3.3.3 Logistic Regression 164 7.3.3.4 Naïve Bayes 164 7.3.3.5 Support Vector Machine 165 7.3.3.6 Linear Discriminant Analysis 166 7.4 Experimental Result and Analysis 167 7.5 Conclusion 173 Acknowledgment 173 References 174 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences 177 Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang 8.1 Introduction 178 8.2 Types of Wireless Sensor for Smart Agriculture 179 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture 179 8.4 ml and WSN-Based Techniques for Smart Agriculture 185 8.5 Future Scope in Smart Agriculture 188 8.6 Conclusion 190 References 190 Part III: Smart City and Villages 197 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics 199 Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja 9.1 Introduction 200 9.1.1 Tasks Involved in Data Pre-Processing 200 9.2 Related Work 202 9.3 Experimental Setup and Methodology 205 9.3.1 Methodology 205 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets 206 9.3.3 Applied Techniques 207 9.3.3.1 Decision Tree 207 9.3.3.2 Naive Bayes 207 9.3.3.3 Artificial Neural Network 208 9.3.4 Proposed Work 208 9.3.4.1 PIMA Diabetes Dataset (PID) 208 9.3.5 Cleveland Heart Disease Dataset 211 9.3.6 Framingham Heart Study 215 9.3.7 Diabetic Dataset 217 9.4 Experimental Result and Discussion 220 9.5 Conclusion and Future Work 222 References 222 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications 225 Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth Sinha 10.1 Introduction 226 10.2 Background 228 10.2.1 History of Cloud Computing 228 10.2.1.1 Software-as-a-Service Model 230 10.2.1.2 Infrastructure-as-a-Service Model 230 10.2.1.3 Platform-as-a-Service Model 232 10.2.2 Types of Cloud Computing 232 10.2.3 Cloud Service Model 232 10.2.4 Characteristics of Cloud Computing 234 10.2.5 Advantages of Cloud Computing 234 10.2.6 Challenges in Cloud Computing 235 10.2.7 Cloud Security 236 10.2.7.1 Foundation Security 236 10.2.7.2 SaaS and PaaS Host Security 237 10.2.7.3 Virtual Server Security 237 10.2.7.4 Foundation Security: The Application Level 238 10.2.7.5 Supplier Data and Its Security 238 10.2.7.6 Need of Security in Cloud 239 10.2.8 Cloud Computing Applications 239 10.3 Literature Review 241 10.4 Cloud Computing Challenges and Its Solution 242 10.4.1 Solution and Practices for Cloud Challenges 246 10.5 Cloud Computing Security Issues and Its Preventive Measures 248 10.5.1 General Security Threats in Cloud 249 10.5.2 Preventive Measures 254 10.6 Cloud Data Protection and Security Using Steganography 258 10.6.1 Types of Steganography 259 10.6.2 Data Steganography in Cloud Environment 260 10.6.3 Pixel Value Differencing Method 261 10.7 Related Study 263 10.8 Conclusion 263 References 264 11 Internet of Drone Things: A New Age Invention 269 Prachi Dahiya 11.1 Introduction 269 11.2 Unmanned Aerial Vehicles 271 11.2.1 UAV Features and Working 274 11.2.2 IoDT Architecture 275 11.3 Application Areas 280 11.3.1 Other Application Areas 284 11.4 IoDT Attacks 285 11.4.1 Counter Measures 291 11.5 Fusion of IoDT With Other Technologies 296 11.6 Recent Advancements in IoDT 299 11.7 Conclusion 302 References 303 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction 305 Mukul Joshi, Gayatri Valluri, Jyoti Rawat and Kriti 12.1 Introduction 305 12.2 Literature Review 307 12.3 System Architecture 309 12.3.1 Model Development Phase 309 12.3.2 Development Environment Phase 311 12.4 Methodology 312 12.4.1 Image Pre-Processing Phase 312 12.4.2 Model Building Phase 313 12.5 Implementation and Results 314 12.5.1 Performance 314 12.5.2 Confusion Matrix 318 12.6 Conclusion and Future Scope 318 References 319 13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work 323 Elamurugan Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu 13.1 Introduction 324 13.2 A Primer on ITS 325 13.3 The ITS Stages 326 13.4 Functions of ITS 327 13.5 ITS Advantages 328 13.6 ITS Applications 329 13.7 ITS Across the World 331 13.8 India’s Status of ITS 333 13.9 Suggestions for Improving India’s ITS Position 334 13.10 Conclusion 335 References 335 14 Evolutionary Approaches in Navigation Systems for Road Transportation System 341 Noopur Tyagi, Jaiteg Singh and Saravjeet Singh 14.1 Introduction 342 14.1.1 Navigation System 343 14.1.2 Genetic Algorithm 347 14.1.3 Differential Evolution 348 14.2 Related Studies 349 14.2.1 Related Studies of Evolutionary Algorithms 351 14.3 Navigation Based on Evolutionary Algorithm 352 14.3.1 Operators and Terms Used in Evolutionary Algorithms 353 14.3.2 Operator and Terms Used in Evolutionary Algorithm 357 14.4 Meta-Heuristic Algorithms for Navigation 359 14.4.1 Drawbacks of DE 362 14.5 Conclusion 362 References 363 15 IoT-Based Smart Parking System for Indian Smart Cities 369 E. Fantin Irudaya Raj, M. Appadurai, M. Chithamabara Thanu and E. Francy Irudaya Rani 15.1 Introduction 370 15.2 Indian Smart Cities Mission 371 15.3 Vehicle Parking and Its Requirements in a Smart City Configuration 373 15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities 375 15.5 Sensors for Vehicle Parking System 383 15.5.1 Active Sensors 384 15.5.2 Passive Sensors 386 15.6 IoT-Based Vehicle Parking System for Indian Smart Cities 387 15.6.1 Guidance to the Customers Through Smart Devices 389 15.6.2 Smart Parking Reservation System 391 15.7 Advantages of IoT-Based Vehicle Parking System 392 15.8 Conclusion 392 References 393 16 Security of Smart Home Solution Based on Secure Piggybacked Key Exchange Mechanism 399 Jatin Arora and Saravjeet Singh 16.1 Introduction 400 16.2 IoT Challenges 404 16.3 IoT Vulnerabilities 405 16.4 Layer-Wise Threats in IoT Architecture 406 16.4.1 Sensing Layer Security Issues 407 16.4.2 Network Layer Security Issues 408 16.4.3 Middleware Layer Security Issues 409 16.4.4 Gateways Security Issues 410 16.4.5 Application Layer Security Issues 411 16.5 Attack Prevention Techniques 411 16.5.1 IoT Authentication 412 16.5.2 Session Establishment 413 16.6 Conclusion 414 References 414 17 Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams 419 Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor and Ashok Kumar 17.1 Introduction 420 17.1.1 Defining Fiber-Reinforced Polymer 421 17.1.2 Types of FRP Composites 422 17.1.2.1 Carbon Fiber–Reinforced Polymer 422 17.1.2.2 Glass Fiber 423 17.1.2.3 Aramid Fiber 424 17.1.2.4 Basalt Fiber 424 17.2 Strengthening of RC Beams With FRP Systems 425 17.2.1 FRP-to-Concrete Bond 426 17.2.2 Flexural Strengthening of Beams With FRP Composite 427 17.2.3 Shear Strengthening of Beams With FRP Composite 427 17.3 Machine Learning Models 428 17.3.1 Prediction of Bond Strength 430 17.3.2 Estimation of Flexural Strength 434 17.3.3 Estimation of Shear Strength 434 17.4 Conclusion 441 References 441 18 Prediction of Indoor Air Quality Using Artificial Intelligence 447 Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Aman Kumar and Harish Chandra Arora 18.1 Introduction 448 18.2 Indoor Air Quality Parameters 450 18.2.1 Physical Parameters 453 18.2.1.1 Humidity 453 18.2.1.2 Air Changes (Ventilation) 454 18.2.1.3 Air Velocity 454 18.2.1.4 Temperature 454 18.2.2 Particulate Matter 455 18.2.3 Chemical Parameters 456 18.2.3.1 Carbon Dioxide 456 18.2.3.2 Carbon Monoxide 456 18.2.3.3 Nitrogen Dioxide 456 18.2.3.4 Sulphur Dioxide 457 18.2.3.5 Ozone 457 18.2.3.6 Gaseous Ammonia 458 18.2.3.7 Volatile Organic Compounds 458 18.2.4 Biological Parameters 459 18.3 AI in Indoor Air Quality Prediction 459 18.4 Conclusion 464 References 465 Index 471

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Book SynopsisIntroduction to BiomedicalImaging A state-of-the-art exploration of the foundations and latest developments in biomedical imaging technology In the newly revised second edition of Introduction to Biomedical Imaging, distinguished researcher Dr. Andrew Webb delivers a comprehensive description of the fundamentals and applications of the most important current medical imaging techniques: X-ray and computed tomography, nuclear medicine, ultrasound, magnetic resonance imaging, and various optical-based methods. Each chapter explains the physical principles, instrument design, data acquisition, image reconstruction, and clinical applications of its respective modality. This latest edition incorporates descriptions of recent developments in photon counting CT, total body PET, superresolution-based ultrasound, phased-array MRI technology, optical coherence tomography, and iterative and model-based image reconstruction techniques. The final chapter discusses the iTable of ContentsPreface xv Introduction xix About the Companion Website xxxi 1 Image and Imaging System Characteristics 1 1.1 General Image and Imaging System Characteristics 1 1.2 Concept of Spatial Frequency 2 1.3 Spatial Resolution 3 1.3.1 Imaging System Point Spread Function 4 1.3.2 Imaging System Resolving Power 5 1.3.3 Imaging System Modulation Transfer Function 6 1.4 Signal-to-Noise Ratio 7 1.5 Contrast-to-Noise Ratio 9 1.6 Signal Digitization: Dynamic Range and Resolution 9 1.7 Post-acquisition Image Filtering 11 1.8 Assessing the Clinical Impact of Improvements in System Performance 12 1.8.1 The Receiver Operating Characteristic Curve 13 1.A.1 Fourier Transforms 14 1.A.2 Fourier Transforms of Time Domain and Spatial Frequency Domain Signals 15 1.A.3 Useful Properties of the Fourier Transform 16 Exercises 17 References 19 Further Reading 20 2 X-ray Imaging and Computed Tomography 23 2.1 General Principles of Imaging with X-rays 23 2.2 X-ray Production 25 2.2.1 The X-ray Tube 25 2.2.2 The X-ray Energy Spectrum 29 2.3 Interactions of X-rays with Tissue 32 2.3.1 Compton Scattering 33 2.3.2 The Photoelectric Effect 34 2.4 Linear and Mass Attenuation Coefficients of X-rays in Tissue 36 2.5 Instrumentation for Planar X-ray Imaging 38 2.5.1 Collimator 38 2.5.2 Anti-scatter Grid 38 2.6 Digital X-ray Detectors 40 2.7 X-ray Image Characteristics 42 2.7.1 Signal-to-Noise 42 2.7.2 Spatial Resolution 44 2.7.3 Contrast-to-Noise 45 2.8 X-ray Contrast Agents 46 2.8.1 Contrast Agents for the Gastrointestinal Tract 46 2.8.2 Iodine-Based Contrast Agents 46 2.9 X-ray Imaging Methods 47 2.9.1 X-ray Fluoroscopy 48 2.9.2 Digital Subtraction Angiography 48 2.10 Clinical Applications of X-ray Imaging 49 2.10.1 Digital Mammography 49 2.10.2 Abdominal X-ray Scans 50 2.11 Computed Tomography 51 2.12 CT Scanner Instrumentation 53 2.12.1 Beam Filtration 55 2.12.2 Detectors for Computed Tomography 56 2.13 Image Processing for Computed Tomography 57 2.13.1 Filtered Backprojection (FBP) Techniques 57 2.13.2 Fan-Beam and Spiral Reconstructions 61 2.14 Iterative Algorithms 63 2.15 Radiation Dose 65 2.16 Spectral/Dual Energy CT 66 2.17 Photon-Counting CT 69 2.18 Cone Beam, Mobile, and Portable CT Units 71 2.19 Clinical Applications of Computed Tomography 72 2.19.1 Head and Neurovascular Scans 72 2.19.2 Pulmonary Disease 73 2.19.3 Abdominal Imaging 73 2.19.4 Cardiovascular Imaging 74 Exercises 75 References 81 Further Reading 82 3 Nuclear Medicine 85 3.1 General Principles of Nuclear Medicine 85 3.2 Radioactivity and Radiotracer Half-life 87 3.3 Common Radiotracers Used for SPECT 89 3.4 The Technetium Generator 90 3.5 The Distribution of Technetium-Based Radiotracers within the Body 92 3.6 Instrumentation for SPECT and SPECT/CT 94 3.6.1 Collimators 94 3.6.2 Scintillation Crystal and Photomultiplier Tube-Based Detectors 98 3.6.3 The Anger Position Network and Pulse Height Analyzer 100 3.6.4 Solid-State Detectors and Specialized Cardiac Scanners 102 3.7 Image Reconstruction 103 3.7.1 Attenuation Correction 104 3.7.2 Scatter Correction 105 3.8 Image Characteristics 106 3.8.1 Signal-to-Noise 106 3.8.2 Spatial Resolution 107 3.8.3 Contrast-to-Noise 107 3.9 Clinical Applications of SPECT 107 3.9.1 Brain Imaging 108 3.9.2 Bone Scanning and Tumor Detection 108 3.9.3 Cardiac Imaging 110 3.9.4 The Respiratory System 110 3.9.5 The Liver and Reticuloendothelial System 112 3.10 Positron Emission Tomography 113 3.11 Radiotracers Used for PET 115 3.12 Instrumentation for PET 116 3.12.1 Scintillation Crystals and Detector Electronics 117 3.13 Image Reconstruction 118 3.13.1 Annihilation Coincidence Detection and Removal of Accidental Coincidences 119 3.13.2 Attenuation Correction 120 3.13.3 Scatter Correction 120 3.13.4 Dead-Time Correction 120 3.14 Image Characteristics 121 3.14.1 Spatial Resolution 121 3.14.2 Signal-to-Noise 121 3.14.3 Contrast-to-Noise 122 3.15 Acquisition Methods for PET 122 3.16 Total Body PET Systems 122 3.17 Clinical Applications of PET/CT 124 3.17.1 Body Oncology 124 3.17.2 Brain Imaging 125 3.17.3 Cardiac Imaging 125 Exercises 126 References 131 Further Reading 132 4 Ultrasound Imaging 135 4.1 General Principles of Ultrasound Imaging 135 4.2 Wave Propagation and Acoustic Impedance 137 4.3 Wave Reflection 139 4.4 Energy Loss Mechanisms in Tissue 142 4.4.1 Scattering 142 4.4.2 Absorption 143 4.4.3 OverallWave Attenuation 145 4.5 Instrumentation 145 4.5.1 Transducer Construction 146 4.5.2 Transducer Arrays 149 4.5.2.1 Linear Sequential Array 151 4.5.2.2 Curvilinear/Convex Sequential Array 151 4.5.2.3 Linear-Phased Array 152 4.6 Signal Detection and Processing 153 4.6.1 Time Gain Compensation 153 4.6.2 Receive Beam Forming 154 4.7 Diagnostic Scanning Modes 155 4.7.1 A-Mode, M-Mode, and B-Mode Scans 155 4.7.2 Three-Dimensional Imaging 156 4.7.3 Compound Imaging 156 4.7.4 Other Transmit and Receive Beamforming Techniques 158 4.8 Image Characteristics 158 4.8.1 Signal-to-Noise 158 4.8.2 Spatial Resolution 159 4.8.2.1 Axial Resolution 159 4.8.2.2 Lateral Resolution 160 4.8.3 Contrast-to-Noise 161 4.9 Artifacts in Ultrasound Imaging 161 4.10 Blood Velocity Measurements Using Ultrasound 163 4.10.1 The Doppler Effect 163 4.10.2 Pulsed-Mode Doppler Measurements 164 4.10.3 Color Doppler/B-mode Duplex and Triplex Imaging 167 4.10.4 ContinuousWave Doppler (CWD) Measurements 168 4.11 Ultrasound Contrast Agents 169 4.11.1 Harmonic and Pulse Inversion Techniques 171 4.11.2 Super-Resolution in Ultrasound Imaging 172 4.12 Safety and Bioeffects in Ultrasound Imaging 174 4.13 Point-of-Care Ultrasound Systems 175 4.14 Clinical Applications of Ultrasound 176 4.14.1 Obstetrics and Gynecology 176 4.14.2 Breast Imaging 176 4.14.3 Musculoskeletal Structure 177 4.14.4 Abdominal 178 Exercises 179 References 185 Further Reading 186 5 Magnetic Resonance Imaging 189 5.1 General Principles of MRI Acquisition and Hardware 189 5.2 Nuclear Magnetization 191 5.2.1 Quantum Mechanical Description 191 5.2.2 Classical Description 195 5.2.3 Hydrogen Nuclei inWater and Lipid 197 5.2.4 Radiofrequency Pulses and the Creation of Transverse Magnetization 197 5.2.5 Signal Detection and Fourier Transformation 199 5.3 T1 and T2 Relaxation Mechanisms and Tissue Relaxation Times 200 5.3.1 Tissue-Dependent Relaxation Times 202 5.3.2 Measurement of T1 and T2: Inversion-Recovery and Spin-Echo Sequences 204 5.4 The MR Free Induction Decay 206 5.5 Magnetic Resonance Imaging 207 5.5.1 Spatial Localization 207 5.5.2 Imaging Concepts 209 5.5.2.1 Slice Selection 210 5.5.2.2 Phase-encoding 212 5.5.2.3 Frequency-encoding 214 5.5.2.4 The k-Space Formalism and Image Reconstruction 214 5.6 Imaging Sequences and Techniques 217 5.6.1 Multislice Gradient-Echo Sequences 217 5.6.2 Multislice Spin-Echo and Turbo-Spin-Echo Sequences 219 5.6.3 Three-Dimensional Gradient-Echo and Spin-Echo Sequences 221 5.6.4 Proton Density, T1-, T2-, and T∗2 -Weighted Sequences 222 5.6.5 Lipid Suppression Techniques 223 5.7 MRI Contrast Agents 226 5.8 Advanced Sequences 228 5.8.1 Magnetic Resonance Angiography 228 5.8.2 Diffusion-Weighted Imaging with Echo Planar Readout 230 5.8.3 In Vivo Localized Spectroscopy 232 5.8.4 Functional MRI 233 5.9 Instrumentation 235 5.9.1 Magnet Design 235 5.9.1.1 Clinical Superconducting Magnets 235 5.9.1.2 Very High Field Magnets 238 5.9.1.3 High-Temperature Superconductors 239 5.9.1.4 Mid- and Low-Field Magnets 239 5.9.2 Magnetic Field Gradient Coils 240 5.9.3 Radiofrequency Coils 244 5.9.3.1 Transmit Coil 244 5.9.4 Receiver Coil Array 244 5.9.5 Receiver Electronics 246 5.10 Image Reconstruction from Undersampled Data 247 5.10.1 Parallel Imaging Using an Array of Receiver Coils 248 5.10.2 Compressed Sensing 250 5.11 Image Characteristics 252 5.11.1 Signal-to-Noise 252 5.11.2 Spatial Resolution 254 5.11.3 Contrast-to-Noise 254 5.12 Image Artifacts 255 5.13 RF Safety Considerations 256 5.14 Clinical Applications of MRI 257 5.14.1 Neurological 258 5.14.2 Body Imaging 259 5.14.3 Musculoskeletal 259 5.14.4 Cardiac 259 Exercises 262 References 274 Further Reading 277 6 Optical Imaging 279 6.1 General Properties of Optical Imaging Methods 279 6.2 Propagation of Light Through Tissue 281 6.3 Body Emissivity Techniques – Infrared Thermography 284 6.4 Direct Imaging with Visible Light 285 6.4.1 Fundus Photography 285 6.4.2 Scheimpflug Camera 287 6.5 Optical Coherence Tomography (OCT) 288 6.5.1 Basic Principles of Interferometry 289 6.5.2 Instrumentation for OCT 291 6.5.2.1 Light Sources 291 6.5.2.2 Beam-Splitter 292 6.5.2.3 Photodetectors 292 6.5.3 Image Characteristics of OCT 292 6.5.4 OCT Angiography 294 6.5.5 Clinical Applications of OCT 295 6.6 Fluorescence-Guided Surgery (FGS) 296 6.6.1 Principle of Fluorescence 296 6.6.2 Fluorescent Probes 296 6.6.3 Instrumentation for Fluorescence Imaging 297 6.6.4 Clinical Applications of Fluorescence-Guided Surgery 298 6.7 Near-Infrared Spectroscopy (NIRS) and Diffuse Optical Tomography (DOT) 299 6.7.1 Principle of NIRS 299 6.7.2 Instrumentation for NIRS 301 6.7.3 Principle of DOT 301 6.7.4 Clinical Applications of DOT 302 6.8 Photoacoustic Imaging (PAI) 303 6.8.1 Principles of PAI 303 6.8.2 Photoacoustic Microscopy and Photoacoustic Computed Tomography 304 6.8.3 Instrumentation for PAI 305 6.8.4 Clinical Applications of PAI 305 References 306 Further Reading 308 7 Artificial Intelligence 311 7.1 Artificial Intelligence in Biomedical Imaging 311 7.2 Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks 312 7.2.1 Neural Networks 313 7.3 Deep Learning in Image Reconstruction 317 7.4 Convolutional Neural Networks (CNNs) 318 7.5 Artificial Intelligence in X-ray and CT 321 7.5.1 Image Reconstruction 321 7.5.2 Clinical Applications 322 7.6 Artificial Intelligence in SPECT and PET 323 7.6.1 Image Reconstruction 323 7.6.2 Clinical Applications 324 7.7 Artificial Intelligence in Ultrasound 324 7.7.1 Improved Data Acquisition 325 7.7.2 Image Post-processing 326 7.7.3 Image Analysis and Clinical Applications 326 7.8 Artificial Intelligence in MRI 326 7.8.1 Image Reconstruction 326 7.8.2 Clinical Applications 328 7.9 Artificial Intelligence in Optical Imaging 329 7.10 AI and Radiomics 329 7.11 Challenges for AI in Biomedical Imaging 330 References 331 Further Reading 337 Index 341

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Book SynopsisDeep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatterTable of ContentsNotes on Contributors xiii Foreword xiv Preface xv Acknowledgments xviii Acronyms xix Introduction xxii Part I Fundamentals of Deep Reinforcement Learning 1 1 Deep Reinforcement Learning and Its Applications 3 1.1 Wireless Networks and Emerging Challenges 3 1.2 Machine Learning Techniques and Development of DRL 4 1.2.1 Machine Learning 4 1.2.2 Artificial Neural Network 7 1.2.3 Convolutional Neural Network 8 1.2.4 Recurrent Neural Network 9 1.2.5 Development of Deep Reinforcement Learning 10 1.3 Potentials and Applications of DRL 11 1.3.1 Benefits of DRL in Human Lives 11 1.3.2 Features and Advantages of DRL Techniques 12 1.3.3 Academic Research Activities 12 1.3.4 Applications of DRL Techniques 13 1.3.5 Applications of DRL Techniques in Wireless Networks 15 1.4 Structure of this Book and Target Readership 16 1.4.1 Motivations and Structure of this Book 16 1.4.2 Target Readership 19 1.5 Chapter Summary 20 References 21 2 Markov Decision Process and Reinforcement Learning 25 2.1 Markov Decision Process 25 2.2 Partially Observable Markov Decision Process 26 2.3 Policy and Value Functions 29 2.4 Bellman Equations 30 2.5 Solutions of MDP Problems 31 2.5.1 Dynamic Programming 31 2.5.1.1 Policy Evaluation 31 2.5.1.2 Policy Improvement 31 2.5.1.3 Policy Iteration 31 2.5.2 Monte Carlo Sampling 32 2.6 Reinforcement Learning 33 2.7 Chapter Summary 35 References 35 3 Deep Reinforcement Learning Models and Techniques 37 3.1 Value-Based DRL Methods 37 3.1.1 Deep Q-Network 38 3.1.2 Double DQN 41 3.1.3 Prioritized Experience Replay 42 3.1.4 Dueling Network 44 3.2 Policy-Gradient Methods 45 3.2.1 REINFORCE Algorithm 46 3.2.1.1 Policy Gradient Estimation 46 3.2.1.2 Reducing the Variance 48 3.2.1.3 Policy Gradient Theorem 50 3.2.2 Actor-Critic Methods 51 3.2.3 Advantage of Actor-Critic Methods 52 3.2.3.1 Advantage of Actor-Critic (A2C) 53 3.2.3.2 Asynchronous Advantage Actor-Critic (A3C) 55 3.2.3.3 Generalized Advantage Estimate (GAE) 57 3.3 Deterministic Policy Gradient (DPG) 59 3.3.1 Deterministic Policy Gradient Theorem 59 3.3.2 Deep Deterministic Policy Gradient (DDPG) 61 3.3.3 Distributed Distributional DDPG (D4PG) 63 3.4 Natural Gradients 63 3.4.1 Principle of Natural Gradients 64 3.4.2 Trust Region Policy Optimization (TRPO) 67 3.4.2.1 Trust Region 69 3.4.2.2 Sample-Based Formulation 70 3.4.2.3 Practical Implementation 70 3.4.3 Proximal Policy Optimization (PPO) 72 3.5 Model-Based RL 74 3.5.1 Vanilla Model-Based RL 75 3.5.2 Robust Model-Based RL: Model-Ensemble TRPO (ME-TRPO) 76 3.5.3 Adaptive Model-Based RL: Model-Based Meta-Policy Optimization (mb-mpo) 77 3.6 Chapter Summary 78 References 79 4 A Case Study and Detailed Implementation 83 4.1 System Model and Problem Formulation 83 4.1.1 System Model and Assumptions 84 4.1.1.1 Jamming Model 84 4.1.1.2 System Operation 85 4.1.2 Problem Formulation 86 4.1.2.1 State Space 86 4.1.2.2 Action Space 87 4.1.2.3 Immediate Reward 88 4.1.2.4 Optimization Formulation 88 4.2 Implementation and Environment Settings 89 4.2.1 Install TensorFlow with Anaconda 89 4.2.2 Q-Learning 90 4.2.2.1 Codes for the Environment 91 4.2.2.2 Codes for the Agent 96 4.2.3 Deep Q-Learning 97 4.3 Simulation Results and Performance Analysis 102 4.4 Chapter Summary 106 References 106 Part II Applications of Drl in Wireless Communications and Networking 109 5 DRL at the Physical Layer 111 5.1 Beamforming, Signal Detection, and Decoding 111 5.1.1 Beamforming 111 5.1.1.1 Beamforming Optimization Problem 111 5.1.1.2 DRL-Based Beamforming 113 5.1.2 Signal Detection and Channel Estimation 118 5.1.2.1 Signal Detection and Channel Estimation Problem 118 5.1.2.2 RL-Based Approaches 120 5.1.3 Channel Decoding 122 5.2 Power and Rate Control 123 5.2.1 Power and Rate Control Problem 123 5.2.2 DRL-Based Power and Rate Control 124 5.3 Physical-Layer Security 128 5.4 Chapter Summary 129 References 131 6 DRL at the MAC Layer 137 6.1 Resource Management and Optimization 137 6.2 Channel Access Control 139 6.2.1 DRL in the IEEE 802.11 MAC 141 6.2.2 MAC for Massive Access in IoT 143 6.2.3 MAC for 5G and B5G Cellular Systems 147 6.3 Heterogeneous MAC Protocols 155 6.4 Chapter Summary 158 References 158 7 DRL at the Network Layer 163 7.1 Traffic Routing 163 7.2 Network Slicing 166 7.2.1 Network Slicing-Based Architecture 166 7.2.2 Applications of DRL in Network Slicing 168 7.3 Network Intrusion Detection 179 7.3.1 Host-Based IDS 180 7.3.2 Network-Based IDS 181 7.4 Chapter Summary 183 References 183 8 DRL at the Application and Service Layer 187 8.1 Content Caching 187 8.1.1 QoS-Aware Caching 187 8.1.2 Joint Caching and Transmission Control 189 8.1.3 Joint Caching, Networking, and Computation 191 8.2 Data and Computation Offloading 193 8.3 Data Processing and Analytics 198 8.3.1 Data Organization 198 8.3.1.1 Data Partitioning 198 8.3.1.2 Data Compression 199 8.3.2 Data Scheduling 200 8.3.3 Tuning of Data Processing Systems 201 8.3.4 Data Indexing 202 8.3.4.1 Database Index Selection 202 8.3.4.2 Index Structure Construction 203 8.3.5 Query Optimization 205 8.4 Chapter Summary 206 References 207 Part III Challenges, Approaches, Open Issues, and Emerging Research Topics 213 9 DRL Challenges in Wireless Networks 215 9.1 Adversarial Attacks on DRL 215 9.1.1 Attacks Perturbing the State space 215 9.1.1.1 Manipulation of Observations 216 9.1.1.2 Manipulation of Training Data 218 9.1.2 Attacks Perturbing the Reward Function 220 9.1.3 Attacks Perturbing the Action Space 222 9.2 Multiagent DRL in Dynamic Environments 223 9.2.1 Motivations 223 9.2.2 Multiagent Reinforcement Learning Models 224 9.2.2.1 Markov/Stochastic Games 225 9.2.2.2 Decentralized Partially Observable Markov Decision Process (dpomdp) 226 9.2.3 Applications of Multiagent DRL in Wireless Networks 227 9.2.4 Challenges of Using Multiagent DRL in Wireless Networks 229 9.2.4.1 Nonstationarity Issue 229 9.2.4.2 Partial Observability Issue 229 9.3 Other Challenges 230 9.3.1 Inherent Problems of Using RL in Real-Word Systems 230 9.3.1.1 Limited Learning Samples 230 9.3.1.2 System Delays 230 9.3.1.3 High-Dimensional State and Action Spaces 231 9.3.1.4 System and Environment Constraints 231 9.3.1.5 Partial Observability and Nonstationarity 231 9.3.1.6 Multiobjective Reward Functions 232 9.3.2 Inherent Problems of DL and Beyond 232 9.3.2.1 Inherent Problems of dl 232 9.3.2.2 Challenges of DRL Beyond Deep Learning 233 9.3.3 Implementation of DL Models in Wireless Devices 236 9.4 Chapter Summary 237 References 237 10 DRL and Emerging Topics in Wireless Networks 241 10.1 DRL for Emerging Problems in Future Wireless Networks 241 10.1.1 Joint Radar and Data Communications 241 10.1.2 Ambient Backscatter Communications 244 10.1.3 Reconfigurable Intelligent Surface-Aided Communications 247 10.1.4 Rate Splitting Communications 249 10.2 Advanced DRL Models 252 10.2.1 Deep Reinforcement Transfer Learning 252 10.2.1.1 Reward Shaping 253 10.2.1.2 Intertask Mapping 254 10.2.1.3 Learning from Demonstrations 255 10.2.1.4 Policy Transfer 255 10.2.1.5 Reusing Representations 256 10.2.2 Generative Adversarial Network (GAN) for DRL 257 10.2.3 Meta Reinforcement Learning 258 10.3 Chapter Summary 259 References 259 Index 263

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Book SynopsisAGILE SOFTWARE DEVELOPMENT A unique title that introduces the whole range of agile software development processes from the fundamental concepts to the highest levels of applications such as requirement analysis, software testing, quality assurance, and risk management. Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community. Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliabili

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Book SynopsisEVOLUTION and APPLICATIONS of QUANTUM COMPUTING The book is about the Quantum Model replacing traditional computing's classical model and gives a state-of-the-art technical overview of the current efforts to develop quantum computing and applications for Industry 4.0. A holistic approach to the revolutionary world of quantum computing is presented in this book, which reveals valuable insights into this rapidly emerging technology. The book reflects the dependence of quantum computing on the physical phenomenon of superposition, entanglement, teleportation, and interference to simplify difficult mathematical problems which would have otherwise taken years to derive a definite solution for. An amalgamation of the information provided in the multiple chapters will elucidate the revolutionary and riveting research being carried out in the brand-new domain encompassing quantum computation, quantum information and quantum mechanics. Each chapter gives a concise introduction to the topic. ThTable of ContentsPreface xvii 1 Introduction to Quantum Computing 1 V. Padmavathi, C. N. Sujatha, V. Sitharamulu, K. Sudheer Reddy and A. Mallikarjuna Reddy 1.1 Quantum Computation 2 1.2 Importance of Quantum Mechanics 2 1.3 Security Options in Quantum Mechanics 2 1.4 Quantum States and Qubits 3 1.5 Quantum Mechanics Interpretation 4 1.6 Quantum Mechanics Implementation 4 1.6.1 Photon Polarization Representation 4 1.7 Quantum Computation 6 1.7.1 Quantum Gates 7 1.8 Comparison of Quantum and Classical Computation 11 1.9 Quantum Cryptography 12 1.10 Qkd 12 1.11 Conclusion 12 References 13 2 Fundamentals of Quantum Computing and Significance of Innovation 15 Swapna Mudrakola, Uma Maheswari V., Krishna Keerthi Chennam and MVV Prasad Kantidpudi 2.1 Quantum Reckoning Mechanism 16 2.2 Significance of Quantum Computing 16 2.3 Security Opportunities in Quantum Computing 16 2.4 Quantum States of Qubit 17 2.5 Quantum Computing Analysis 17 2.6 Quantum Computing Development Mechanism 18 2.7 Representation of Photon Polarization 18 2.8 Theory of Quantum Computing 20 2.9 Quantum Logical Gates 21 2.9.1 I-Qubit GATE 21 2.9.2 Hadamard-GATE 22 2.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE 22 2.9.3.1 Pauli_Y-GATE 23 2.9.3.2 Pauli_Z-GATE 23 2.9.3.3 Pauli_S-Gate 23 2.9.4 Two-Qubit GATE 24 2.9.5 Controlled NOT(C-NOT) 24 2.9.6 The Two-Qubits are Swapped Using SWAP_GATE 24 2.9.7 C-Z-GATE (Controlled Z-GATE) 24 2.9.8 C-P-GATE (Controlled-Phase-GATE) 25 2.9.9 Three-Qubit Quantum GATE 25 2.9.9.1 GATE: Toffoli Gate 25 2.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE) 26 2.10 Quantum Computation and Classical Computation Comparison 27 2.11 Quantum Cryptography 27 2.12 Quantum Key Distribution – QKD 27 2.13 Conclusion 28 References 28 3 Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates 31 Virat Tara, Navneet Sharma, Pravindra Kumar and Kumar Gautam 3.1 Introduction 32 3.2 Mathematical Background of Quantum Circuits 34 3.2.1 Hadamard Gate 34 3.2.2 CSWAP Gates 35 3.2.3 Controlled-R Gates 36 3.3 Methodology of Designing Quantum Multiplexer (QMUX) 36 3.3.1 QMUX Using CSWAP Gates 36 3.3.1.1 Generalization 37 3.3.2 QMUX Using Controlled-R Gates 37 3.4 Analysis and Synthesis of Proposed Methodology 39 3.5 Complexity and Cost of Quantum Circuits 41 3.6 Conclusion 42 References 42 4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain 45 Syed Abdul Moeed, P. Niranjan and G. Ashmitha 4.1 Introduction 46 4.1.1 Quantum Computing Convolutional Neural Network 51 4.2 Literature Survey 52 4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems 58 4.3.1 Minimizing Quantum Algorithm 58 4.3.2 K-NN Algorithm 58 4.3.3 K-Means Algorithm 60 4.4 Tree Tensor Networking 61 4.5 TNN Implementation on IBM Quantum Processor 62 4.6 Neurotomography 62 4.7 Conclusion and Future Scope 63 References 64 5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids 67 Kanak Pandit, Aditya Mogare, Achal Shah, Prachi Thete and Megharani Patil 5.1 Introduction 68 5.2 Literature Review 71 5.3 Proposed Work 74 5.3.1 Methodology 74 5.3.2 Work Flow of Neural Style Transfer 75 5.3.3 A-Frame 75 5.3.3.1 Setting Up the Virtual World and Adding Components 75 5.3.3.2 Adding Interactivity Through Raycasting 76 5.3.3.3 Animating the Components 77 5.3.4 Neural Style Transfer 78 5.3.4.1 Choosing the Content and Styling Image 79 5.3.4.2 Image Preprocessing and Generation of a Random Image 79 5.3.4.3 Model Design and Extraction of Content and Style 81 5.3.4.4 Loss Calculation 81 5.3.4.5 Model Optimization 84 5.4 Evaluation Metrics 86 5.5 Results 89 5.5.1 A-Frame 89 5.5.2 Neural Style Transfer 90 5.6 Conclusion 90 References 91 6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor 93 Abishek Mahesh, Prithvi Seshadri, Shruti Mishra and Sandeep Kumar Satapathy 6.1 Introduction 94 6.2 Related Work 94 6.3 Proposed Model 95 6.3.1 URL Feature Extractor 95 6.3.2 Dataset 103 6.3.3 Methodologies 104 6.3.3.1 AdaBoost Classifier 105 6.3.3.2 Gradient Boosting Classifier 105 6.3.3.3 K-Nearest Neighbors 105 6.3.3.4 Logistic Regression 106 6.3.3.5 Artificial Neural Networks 106 6.3.3.6 Support Vector Machines (SVM) 107 6.3.3.7 Naïve Bayes Classifier 107 6.4 Results 109 6.5 Conclusions 109 References 109 7 Detection of Malicious Emails and URLs Using Text Mining 111 Heetakshi Fating, Aditya Narawade, Sandeep Kumar Satapathy and Shruti Mishra 7.1 Introduction 112 7.2 Related Works 112 7.3 Dataset Description 114 7.4 Proposed Architecture 115 7.5 Methodology 116 7.5.1 Methodology for the URL Dataset 116 7.5.2 Methodology for the Email Dataset 118 7.5.2.1 Overcoming the Overfitting Problem 118 7.5.2.2 Tokenization 119 7.5.2.3 Applying Machine Learning Algorithms 119 7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails 119 7.5.3.1 Preparation of Dataset 119 7.5.3.2 Creation of Features 120 7.5.3.3 Applying Machine Learning Algorithms 120 7.6 Results 120 7.6.1 URL Dataset 120 7.6.2 Email Dataset 121 7.6.3 Final Dataset 121 7.7 Conclusion 122 References 122 8 Quantum Data Traffic Analysis for Intrusion Detection System 125 Anshul Harish Khatri, Vaibhav Gadag, Simrat Singh, Sandeep Kumar Satapathy and Shruti Mishra 8.1 Introduction 126 8.2 Literature Overview 127 8.3 Methodology 129 8.3.1 Autoviz 129 8.3.2 Dataset 132 8.3.3 Proposed Models 132 8.3.3.1 Decision Tree 135 8.3.3.2 Random Forest Classifier Algorithm 136 8.3.3.3 AdaBoost Classifier 136 8.3.3.4 Ridge Classifier 137 8.3.3.5 Logistic Regression 137 8.3.3.6 SVM-Linear Kernel 138 8.3.3.7 Naive Bayes 138 8.3.3.8 Quadratic Discriminant Analysis 139 8.4 Results 140 8.5 Conclusion 141 References 142 9 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting 145 Sarthak Dash, Sugyanta Priyadarshini, Sachi Nandan Mohanty, Sukanya Priyadarshini and Nisrutha Dulla 9.1 Introduction 146 9.2 Discussion 148 9.2.1 Exploring Market Functioning via Quantum Network Economy 148 9.2.1.1 Internal Networking Marketing 149 9.2.1.2 Layered Marketing 149 9.2.1.3 Role of Marketing in Pharma Network Organizations 150 9.2.1.4 Role of Marketing in Vertical Networking Organizations 152 9.2.1.5 Generic e-Commerce Entity Model in Pharmaceutical Industry 153 9.2.2 Analyzing the Usability of Quantum Netnomics in Attending Economic Development 154 9.2.2.1 Theory of 4Ps in Pharma Marketing mix 155 9.2.2.2 Buying Behavior of the e-Consumers 156 9.2.2.3 Maintaining of Privacy and Security via Quantum Technology in e-Structure 157 9.2.2.4 Interface Influencing Sales 157 9.3 Results 158 9.4 Conclusion 159 References 159 10 Machine Learning Approach in the Indian Service Industry: A Case Study on Indian Banks 163 Pragati Priyadarshinee 10.1 Introduction 163 10.2 Literature Survey 164 10.3 Experimental Results 170 10.4 Conclusion 172 References 172 11 Accelerating Drug Discovery with Quantum Computing 175 Mahesh V. and Shimil Shijo 11.1 Introduction 175 11.2 Working Nature of Quantum Computers 176 11.3 Use Cases of Quantum Computing in Drug Discovery 178 11.4 Target Drug Identification and Validation 179 11.5 Drug Discovery Using Quantum Computers is Expected to Start by 2030 179 11.6 Conclusion 180 References 181 12 Problems and Demanding Situations in Traditional Cryptography: An Insistence for Quantum Computing to Secure Private Information 183 D. DShivaprasad, Mohamed Sirajudeen Yoosuf, P. Selvaramalakshmi, Manoj A. Patil and Dasari Promod Kumar 12.1 Introduction to Cryptography 184 12.1.1 Confidentiality 184 12.1.2 Authentication 185 12.1.3 Integrity 185 12.1.4 Non-Repudiation 186 12.2 Different Types of Cryptography 186 12.2.1 One-Way Processing 186 12.2.1.1 Hash Function (One-Way Processing) 186 12.2.2 Two-Way Processing 187 12.2.2.1 Symmetric Cryptography 188 12.2.2.2 Asymmetric Cryptography 189 12.2.3 Algorithms Types 190 12.2.3.1 Stream Cipher 190 12.2.3.2 Block Cipher 191 12.2.4 Modes of Algorithm 192 12.2.4.1 Cipher Feedback Mode 192 12.2.4.2 Output Feedback Mode 192 12.2.4.3 Cipher Block Chaining Mode 192 12.2.4.4 Electronic Code Book 192 12.3 Common Attacks 193 12.3.1 Passive Attacks 193 12.3.1.1 Traffic Analysis 193 12.3.1.2 Eavesdropping 194 12.3.1.3 Foot Printing 195 12.3.1.4 War Driving 195 12.3.1.5 Spying 195 12.3.2 Active Attacks 196 12.3.2.1 Denial of Service 196 12.3.2.2 Distributed Denial of Service (DDOS) 197 12.3.2.3 Message Modification 197 12.3.2.4 Masquerade 197 12.3.2.5 Trojans 198 12.3.2.6 Replay Attacks 199 12.3.3 Programming Weapons for the Attackers 199 12.3.3.1 Dormant Phase 200 12.3.3.2 Propagation Phase 200 12.3.3.3 Triggering Phase 201 12.3.3.4 Execution Phase 201 12.4 Recent Cyber Attacks 201 12.5 Drawbacks of Traditional Cryptography 203 12.5.1 Cost and Time Delay 203 12.5.2 Disclosure of Mathematical Computation 203 12.5.3 Unsalted Hashing 204 12.5.4 Attacks 204 12.6 Need of Quantum Cryptography 204 12.6.1 Quantum Mechanics 204 12.7 Evolution of Quantum Cryptography 205 12.8 Conclusion and Future Work 205 References 205 13 Identification of Bacterial Diseases in Plants Using Re-Trained Transfer Learning in Quantum Computing Environment 207 Sri Silpa Padmanabhuni, B. Srikanth Reddy, A. Mallikarjuna Reddy and K. Sudheer Reddy 13.1 Introduction 208 13.2 Literature Review 218 13.3 Proposed Methodology 220 13.3.1 SVM Classifier 222 13.3.2 Random Forest to Classify the Rice Leaf 223 13.3.2.1 Image Pre-Processing 223 13.3.2.2 Feature Extraction 223 13.3.2.3 Classification 224 13.4 Experiment Results 226 Conclusion 230 References 230 14 Quantum Cryptography 233 Salma Fauzia 14.1 Fundamentals of Cryptography 234 14.2 Principle of Quantum Cryptography 237 14.2.1 Quantum vs. Conventional Cryptography 237 14.3 Quantum Key Distribution Protocols 238 14.3.1 Overview and BB84 Protocol 238 14.3.2 The B92 Protocol 240 14.3.3 E91 Protocol 241 14.3.4 SARG04 Protocol 243 14.4 Impact of the Sifting and Distillation Steps on the Key Size 243 14.5 Cryptanalysis 246 14.6 Quantum Key Distribution in the Real World 247 References 248 15 Security Issues in Vehicular Ad Hoc Networks and Quantum Computing 249 B. Veera Jyothi, L. Suresh Kumar and B. Surya Samantha 15.1 Introduction 250 15.2 Overview of VANET Security 250 15.2.1 Security of VANET 250 15.2.2 Attacks are Classified 251 15.3 Architectural and Systematic Security Methods 252 15.3.1 Solutions for Cryptography 252 15.3.2 Framework for Trust Groups 252 15.3.3 User Privacy Security System Based on ID 253 15.4 Suggestions on Particular Security Challenges 254 15.4.1 Content Delivery Integrity Metrics 254 15.4.2 Position Detection 254 15.4.3 Protective Techniques 255 15.5 Quantum Computing in Vehicular Networks 257 15.5.1 Securing Automotive Ecosystems: A Challenge 257 15.5.2 Generation of Quantum Random Numbers (QRNG) 258 15.6 Quantum Key Transmission (QKD) 258 15.7 Quantum Internet – A Future Vision 259 15.7.1 Quantum Internet Applications 259 15.7.2 Application Usage-Based Categorization 260 15.8 Conclusions 262 References 263 16 Quantum Cryptography with an Emphasis on the Security Analysis of QKD Protocols 265 Radhika Kavuri, Santhosh Voruganti, Sheena Mohammed, Sucharitha Inapanuri and B. Harish Goud 16.1 Introduction 266 16.2 Basic Terminology and Concepts of Quantum Cryptography 267 16.2.1 Quantum Cryptography and Quantum Key Distribution 267 16.2.2 Quantum Computing and Quantum Mechanics 267 16.2.3 Post-Quantum Cryptography 267 16.2.4 Quantum Entanglement 267 16.2.5 Heisenberg’s Uncertainty Principle 268 16.2.6 Qubits 268 16.2.7 Polarization 269 16.2.8 Traditional Cryptography vs. Quantum Cryptography 269 16.3 Trends in Quantum Cryptography 270 16.3.1 Global Quantum Key Distribution Links 271 16.3.2 Research Statistics on Quantum Cryptography 273 16.4 An Overview of QKD Protocols 274 16.4.1 Introduction to the Prepare-and-Measure Protocols 275 16.4.2 The BB84 Protocol 275 16.4.3 B92 Protocol 278 16.4.4 Six State Protocol (SSP) 278 16.4.5 SARG04 Protocol 279 16.4.6 Introduction to the Entanglement-Based Protocols 280 16.4.7 The E91 Protocol 280 16.4.8 The BBM92 Protocol 280 16.5 Security Concerns in QKD 282 16.6 Future Research Foresights 284 16.6.1 Increase in Bit Rate 284 16.6.2 Longer Distance Coverage 284 16.6.3 Long Distance Quantum Repeaters 285 16.6.4 Device Independent Quantum Cryptography 285 16.6.5 Development of Tools for Simulation and Measurements 285 16.6.6 Global Quantum Communication Network 285 16.6.7 Integrated Photonic Spaced QKD 285 16.6.8 Quantum Teleportation 286 References 286 17 Deep Learning-Based Quantum System for Human Activity Recognition 289 Shoba Rani Salvadi, Narsimhulu Pallati and Madhuri T. 17.1 Introduction 290 17.2 Related Works 292 17.3 Proposed Scheme 293 17.3.1 Datasets Description 294 17.3.2 Pre-Processing 294 17.3.3 Feature Extraction 295 17.3.4 Preliminaries 295 17.3.4.1 Quantum Computing 296 17.3.4.2 Convolutional Neural Networks 296 17.3.5 Proposed ORQC-CNN Model 296 17.3.5.1 Quantum Convolutional Layer 297 17.3.5.2 Convolutional Layer 299 17.3.5.3 Max-Pooling Layer 299 17.3.5.4 Fully Connected Layer 299 17.3.6 Parameter Selection Using Artificial Gorilla Troops Optimization Algorithm (AGTO) 300 17.3.6.1 Exploration Phase 301 17.3.6.2 Exploitation Phase 302 17.3.6.3 Follow the Silverback 303 17.3.6.4 Competition for Adult Females 303 17.3.7 Computational Difficulty 304 17.4 Results and Discussion 304 17.4.1 Performance Measure 305 17.4.2 Performance Analysis of Dataset 1 306 17.4.3 Performance Analysis of Dataset 2 307 17.4.4 Comparison 308 17.5 Conclusion 309 References 309 18 Quantum Intelligent Systems and Deep Learning 313 Bhagaban Swain and Debasis Gountia 18.1 Introduction 313 18.2 Quantum Support Vector Machine 315 18.3 Quantum Principal Component Analysis 318 18.4 Quantum Neural Network 319 18.5 Variational Quantum Classifier 321 18.6 Conclusion 323 References 323 Index 327

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Book SynopsisOPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I Soft Computing and Evolutionary-Based Optimization; and Part II Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: An overview and brief background of optimizatTable of ContentsPreface xxi Acknowledgment xxix Part 1: Soft Computing and Evolutionary-Based Optimization 1 1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles 3 Anjali Jain, Ashish Mani and Anwar S. Siddiqui 1.1 Introduction 4 1.2 Problem Formulation 5 1.2.1 Power Output Limits 6 1.2.2 Power Balance Limits 6 1.2.3 Ramp Rate Limits 7 1.2.4 Electric Vehicles 7 1.3 Proposed Algorithm 8 1.3.1 Overview of Grey Wolf Optimizer 8 1.3.2 Improved Grey Wolf Optimizer with Levy Flight 9 1.3.3 Modeling of Prey Position with Levy Flight Distribution 10 1.4 Simulation and Results 13 1.4.1 Performance of Improved GWOLF on Benchmark Functions 14 1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution 14 1.5 Conclusion 29 References 34 xxi vii 2 Comparison of YOLO and Faster R-CNN on Garbage Detection 37 Arulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D. 2.1 Introduction 37 2.2 Garbage Detection 39 2.2.1 Transfer Learning-Technique 39 2.2.2 Inception-Custom Model 39 2.2.2.1 Convolutional Neural Network 40 2.2.2.2 Max Pooling 41 2.2.2.3 Stride 41 2.2.2.4 Average Pooling 41 2.2.2.5 Inception Layer 42 2.2.2.6 3*3 and 1*1 Convolution 43 2.2.2.7 You Only Look Once (YOLO) Architecture 43 2.2.2.8 Faster R-CNN Algorithm 44 2.2.2.9 Mean Average Precision (mAP) 46 2.3 Experimental Results 46 2.3.1 Results Obtained Using YOLO Algorithm 46 2.3.2 Results Obtained Using Faster R-CNN 46 2.4 Future Scope 48 2.5 Conclusion 48 References 48 3 Smart Power Factor Correction and Energy Monitoring System 51 Amutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D. 3.1 Introduction 51 3.2 Block Diagram 53 3.2.1 Power Factor Concept 54 3.2.2 Power Factor Calculation 54 3.3 Simulation 54 3.4 Conclusion 56 References 57 4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications 59 Sivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A. 4.1 Introduction 59 4.2 Block Diagram 60 4.3 ANN-Based MPPT for Boost Converter 64 4.4 Closed Loop Control 66 4.5 Simulation Results 67 4.6 Conclusion 70 References 70 5 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm 73 Omveer Singh, Shalini Gupta and Shabana Urooj 5.1 Introduction 74 5.2 Modeling Structure 75 5.2.1 Single-Junction Solar Cell Model 75 5.2.2 Modeling of Multijunction Solar PV Cell 77 5.3 MPPT Design Techniques 80 5.3.1 Design of MPPT Scheme Based on P&O Technique 80 5.3.2 Design of MPPT Scheme Based on FLA 82 5.4 Results and Discussions 84 5.4.1 Single-Junction Solar Cell 84 5.4.2 Multijunction Solar PV Cell 86 5.4.3 Implementation of MPPT Scheme Based on P&O Technique 90 5.4.4 Implementation of MPPT Scheme Based on FLA 91 5.5 Conclusion 93 References 93 6 Particle Swarm Optimization: An Overview, Advancements and Hybridization 95 Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant 6.1 Introduction 96 6.2 The Particle Swarm Optimization: An Overview 97 6.3 PSO Algorithms and Pseudo-Code 98 6.3.1 PSO Algorithm 98 6.3.2 Pseudo-Code for PSO 101 6.3.3 PSO Limitations 101 6.4 Advancements in PSO and Its Perspectives 102 6.4.1 Inertia Weight 102 6.4.1.1 Random Selection (RS) 102 6.4.1.2 Linear Time Varying (LTV) 103 6.4.1.3 Nonlinear Time Varying (NLTV) 103 6.4.1.4 Fuzzy Adaptive (FA) 103 6.4.2 Constriction Factors 104 6.4.3 Topologies 104 6.4.4 Analysis of Convergence 104 6.5 Hybridization of PSO 105 6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) 105 6.5.2 PSO Hybridization with Ant Colony Optimization (aco) 106 6.5.3 PSO Hybridization with Genetic Algorithms (GA) 106 6.6 Area of Applications of PSO 107 6.7 Conclusions 109 References 109 7 Application of Genetic Algorithm in Sensor Networks and Smart Grid 115 Geeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni 7.1 Introduction 115 7.2 Communication Sector 116 7.2.1 Sensor Networks 116 7.3 Electrical Sector 117 7.3.1 Smart Microgrid 117 7.4 A Brief Outline of GAs 118 7.5 Sensor Network’s Energy Optimization 120 7.6 Sensor Network’s Coverage and Uniformity Optimization Using GA 126 7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid 131 7.8 GA Versus Traditional Methods 135 7.9 Summaries and Conclusions 136 References 137 8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber–Reinforced Polymer (CFRP) Drilling Process 139 Rohit Volety and Geetha Mani 8.1 Introduction 140 8.2 Methodology 142 8.3 AI-Based Predictive Modeling 143 8.3.1 Linear Regression 143 8.3.2 Random Forests 144 8.3.3 XGBoost 145 8.3.4 Svm 146 8.4 Performance Indices 146 8.4.1 Root Mean Squared Error (RMSE) 146 8.4.2 Mean Squared Error (MSE) 147 8.4.3 R 2 (R-Squared) 147 8.5 Results and Discussion 147 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase 148 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase 148 8.5.3 K Cross Fold Validation 149 8.6 Conclusions 151 References 152 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery 155 Jacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar 9.1 Introduction 156 9.2 Literature Survey 157 9.3 Research Methodology 159 9.3.1 Dataset and Metrics 161 9.4 Result and Discussion 162 9.5 Conclusion 165 References 165 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA 169 Jyotirmayee Subudhi and P. Indumathi 10.1 Introduction 170 10.2 System Model 172 10.3 User Clustering 175 10.4 Optimal Power Allocation for EE-SE Tradeoff 176 10.4.1 Multiobjective Optimization Problem 177 10.4.2 Multiobjective PSO 178 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA 180 10.5 Numerical Results 180 10.6 Conclusion 183 References 184 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews 187 Rishabh Singh, Akarshan Kumar and Mousim Ray 11.1 Introduction 188 11.1.1 Related Work 189 11.2 Materials and Methods 190 11.2.1 Data Cleaning and Pre-Processing 191 11.2.2 Feature Extraction 191 11.2.3 Classifiers 193 11.3 Results and Experiments 194 11.4 Conclusion 197 References 198 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm 201 Mintu Pal and Sibsankar Dasmahapatra 12.1 Introduction 202 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique 203 12.3 Design of Multiobjective Optimization Problem 204 12.3.1 Decision Variables 204 12.3.2 Objective Functions 204 12.3.2.1 Minimization of Main Cutting Force 205 12.3.2.2 Minimization of Feed Force 205 12.3.3 Bounds of Decision Variables 205 12.3.4 Response Variables 206 12.4 Results and Discussions 206 12.4.1 Single Objective Optimization 206 12.4.2 Results of Multiobjective Optimization 208 12.5 Conclusion 212 References 212 13 Genetic Algorithm-Based Optimization for Speech Processing Applications 215 Ramya.R, M. Preethi and R. Rajalakshmi 13.1 Introduction to GA 215 13.1.1 Enhanced GA 216 13.1.1.1 Hybrid GA 216 13.1.1.2 Interval GA 217 13.1.1.3 Adaptive GA 217 13.2 GA in Automatic Speech Recognition 218 13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) 218 13.2.2 Classification of Features in ASR Using GA 219 13.2.3 GA-Based Distinctive Phonetic Features Recognition 219 13.2.4 GA in Phonetic Decoding 220 13.3 Genetic Algorithm in Speech Emotion Recognition 221 13.3.1 Speech Emotion Recognition 221 13.3.2 Genetic Algorithms in Speech Emotion Recognition 222 13.3.2.1 Feature Extraction Using GA for SER 222 13.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization 224 13.4 Genetic Programming in Hate Speech Using Deep Learning 225 13.4.1 Introduction to Hate Speech Detection 225 13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection 226 13.5 Conclusion 228 References 228 14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant 231 Ranjit Singh and L. Ramesh 14.1 Introduction 231 14.2 Single-Area Power System 232 14.3 Automatic Load Frequency Control (ALFC) 233 14.4 Controllers Used in the Simulink Model 233 14.4.1 PID Controller 233 14.4.2 PI Controller 234 14.4.3 P Controller 234 14.5 Circuit Description 235 14.6 ANN and NARMA L2 Controller 236 14.7 Simulation Results and Comparative Analysis 237 14.8 Conclusion 239 References 240 Part 2: Decision Science and Simulation-Based Optimization 243 15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment 245 Bipradas Bairagi 15.1 Introduction 246 15.2 Fuzzy Set Theory 248 15.2.1 Some Important Fuzzy Definitions 248 15.2.2 Fuzzy Operations 249 15.2.3 Linguistic Variable (LV) 250 15.3 Fvikor 251 15.4 Problem Definition 253 15.5 Results and Discussions 253 15.6 Conclusions 258 References 259 16 Slightly and Almost Neutrosophic gsα*—Continuous Function in Neutrosophic Topological Spaces 261 P. Anbarasi Rodrigo and S. Maheswari 16.1 Introduction 261 16.2 Preliminaries 262 16.3 Slightly Neutrosophic gsα* – Continuous Function 263 16.4 Almost Neutrosophic gsα* – Continuous Function 266 16.5 Conclusion 274 References 274 17 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers 275 Hullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra 17.1 Introduction 275 17.2 Materials and Methods 277 17.2.1 ELECTRE Technique 278 17.3 Result and Discussion 281 17.4 Conclusion 293 References 294 18 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection 297 Bipradas Bairagi 18.1 Introduction 298 18.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm 300 18.3 Illustrative Example 303 18.3.1 Problem Definition 303 18.3.2 Calculation and Discussions 305 18.4 Conclusions 309 References 310 19 Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry 313 Rajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma 19.1 Introduction 314 19.2 Methodology 315 19.3 Results and Discussion 316 19.4 Conclusions 320 References 321 20 Determining Key Influential Factors of Rural Tourism— An AHP Model 323 Puspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy 20.1 Introduction 324 20.2 Rural Tourism 325 20.3 Literature Review 326 20.4 Objectives 328 20.5 Methodology 328 20.6 Analysis 332 20.7 Results and Discussion 332 20.8 Conclusions 340 20.9 Managerial Implications 340 References 341 21 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment 345 Partha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das 21.1 Introduction 346 21.1.1 Overview 346 21.1.2 Motivation and Specific Study 346 21.2 Preliminaries 348 21.2.1 Pollution Function 348 21.2.2 Triangular Dense Fuzzy Set (TDFS) 349 21.3 Notations and Assumptions 350 21.3.1 Case Study 351 21.4 Formulation of the Mathematical Model 352 21.4.1 Crisp Mathematical Model 352 21.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model 352 21.4.3 Defuzzification of Triangular Dense Fuzzy Model 353 21.5 Numerical Illustration 354 21.6 Sensitivity Analysis 355 21.7 Graphical Illustration 355 21.8 Merits and Demerits 358 21.9 Conclusion 358 Acknowledgement 359 Appendix 359 References 360 22 Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach 363 Rajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy 22.1 Introduction 364 22.2 Literature Review 365 22.3 Analytic Hierarchy Process (AHP) 367 22.4 Result and Discussion 372 22.5 Conclusion 373 References 373 23 E-Waste Management Challenges in India: An AHP Approach 377 Amit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara 23.1 Introduction 378 23.2 Literature Review 379 23.3 Methodology 379 23.4 Results and Discussion 379 23.5 Conclusion 390 References 391 24 Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States 393 Tanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De 24.1 Introduction 394 24.2 Literature Review 395 24.3 Materials and Methods 397 24.3.1 Data Preparation 397 24.3.2 Methods and Approach 397 24.3.2.1 Cluster Analysis 397 24.3.2.2 Agglomerative Hierarchical Clustering 397 24.3.2.3 K-Means Clustering 398 24.4 Exploratory Data Analysis 398 24.5 Results and Discussion 401 24.6 Conclusion 405 References 406 25 Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon 409 Garba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed 25.1 Introduction 410 25.1.1 Airwaves 411 25.1.2 Fast Fourier Transform 412 25.2 Related Works 413 25.3 Theoretical Framework 415 25.4 Methodology 416 25.5 Results and Discussions 417 25.6 Conclusion 420 References 420 26 Design and Implementation of Control for Nonlinear Active Suspension System 423 Ravindra S. Rana and Dipak M. Adhyaru 26.1 Introduction 423 26.2 Mathematical Model of Quarter Car Suspension System 426 26.2.1 Mathematical Model 426 26.2.2 Linearization Method for Nonlinear System Model 429 26.2.3 Discussion of Result 430 26.3 Conclusion 433 References 434 27 A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS) 437 Himanshu Kumar Sinha, Anand Kumar and Devasis Pradhan 27.1 Introduction 437 27.2 Literature Review 439 27.3 Overview of 5G Cellular System 440 27.4 Papr 441 27.4.1 Continuous Time PAPR 441 27.4.2 Continuous Time PAPR 442 27.5 Factors on which PAPR Reduction Depends 442 27.6 PAPR Reduction Technique 443 27.6.1 Scrambling of Signals 443 27.6.2 Signal Distortion Technique 446 27.6.3 High Power Amplifier (HPA) 447 27.7 Limitation of OFDM 447 27.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G 448 27.8.1 Transmitter of UMFC 448 27.8.2 Receiver of UMFC 450 27.9 Comparison Between Various Techniques 450 27.10 Conclusion 450 References 452 28 Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test 455 Aman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan 28.1 Introduction 455 28.2 Investigation 458 28.2.1 Specimen for Tests 458 28.2.2 Design of Die and Tool 458 28.2.3 Configuration and Procedure 459 28.3 Mathematical Evaluation 460 28.3.1 Simulation Methodology 460 28.4 Modeling for Material 461 28.4.1 Suppressing Rebound Phenomenon 461 28.5 Conclusion 466 References 466 29 Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System 469 Lazar Z. Velimirovic 29.1 Introduction 469 29.2 OFDM System 471 29.2.1 PAPR of OFDM Signal 472 29.3 Companding Technique 474 29.3.1 Quadratic Spline Function Companding 474 29.4 Numerical Results and Discussion 475 29.5 Conclusion 480 Acknowledgment 480 References 480 30 A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management 483 Bipradas Bairagi 30.1 Introduction 484 30.2 Proposed Algorithm 486 30.3 Illustrative Example 491 30.3.1 Problem Definition 491 30.3.2 Calculation and Discussions 492 30.4 Conclusions 498 References 499 Index 501

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Book SynopsisMACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets.Table of ContentsList of Contributors xiii Preface xix 1 Applications of VLSI Design in Artificial Intelligence and Machine Learning 1 Imran Ullah Khan, Nupur Mittal and Mohd. Amir Ansari 1.1 Introduction 2 1.2 Artificial Intelligence 4 1.3 Artificial Intelligence & VLSI (AI and VLSI) 4 1.4 Applications of AI 4 1.5 Machine Learning 5 1.6 Applications of ml 6 1.6.1 Role of ML in Manufacturing Process 6 1.6.2 Reducing Maintenance Costs and Improving Reliability 6 1.6.3 Enhancing New Design 7 1.7 Role of ML in Mask Synthesis 7 1.8 Applications in Physical Design 8 1.8.1 Lithography Hotspot Detection 9 1.8.2 Pattern Matching Approach 9 1.9 Improving Analysis Correlation 10 1.10 Role of ML in Data Path Placement 12 1.11 Role of ML on Route Ability Prediction 12 1.12 Conclusion 13 References 14 2 Design of an Accelerated Squarer Architecture Based on Yavadunam Sutra for Machine Learning 19 A.V. Ananthalakshmi, P. Divyaparameswari and P. Kanimozhi 2.1 Introduction 20 2.2 Methods and Methodology 21 2.2.1 Design of an n-Bit Squaring Circuit Based on (n-1)-Bit Squaring Circuit Architecture 22 2.2.1.1 Architecture for Case 1: A < B 22 2.2.1.2 Architecture for Case 2: A > B 24 2.2.1.3 Architecture for Case 3: A = B 24 2.3 Results and Discussion 25 2.4 Conclusion 29 References 30 3 Machine Learning–Based VLSI Test and Verification 33 Jyoti Kandpal 3.1 Introduction 33 3.2 The VLSI Testing Process 35 3.2.1 Off-Chip Testing 35 3.2.2 On-Chip Testing 35 3.2.3 Combinational Circuit Testing 36 3.2.3.1 Fault Model 36 3.2.3.2 Path Sensitizing 36 3.2.4 Sequential Circuit Testing 36 3.2.4.1 Scan Path Test 36 3.2.4.2 Built-In-Self Test (BIST) 36 3.2.4.3 Boundary Scan Test (BST) 37 3.2.5 The Advantages of VLSI Testing 37 3.3 Machine Learning’s Advantages in VLSI Design 38 3.3.1 Ease in the Verification Process 38 3.3.2 Time-Saving 38 3.3.3 3Ps (Power, Performance, Price) 38 3.4 Electronic Design Automation (EDA) 39 3.4.1 System-Level Design 40 3.4.2 Logic Synthesis and Physical Design 42 3.4.3 Test, Diagnosis, and Validation 43 3.5 Verification 44 3.6 Challenges 47 3.7 Conclusion 47 References 48 4 IoT-Based Smart Home Security Alert System for Continuous Supervision 51 Rajeswari, N. Vinod Kumar, K. M. Suresh, N. Sai Kumar and K. Girija Sravani 4.1 Introduction 52 4.2 Literature Survey 53 4.3 Results and Discussions 54 4.3.1 Raspberry Pi-3 B+Module 54 4.3.2 Pi Camera 56 4.3.3 Relay 56 4.3.4 Power Source 56 4.3.5 Sensors 56 4.3.5.1 IR & Ultrasonic Sensor 56 4.3.5.2 Gas Sensor 56 4.3.5.3 Fire Sensor 57 4.3.5.4 GSM Module 57 4.3.5.5 Buzzer 57 4.3.5.6 Cloud 57 4.3.5.7 Mobile 57 4.4 Conclusions 62 References 62 5 A Detailed Roadmap from Conventional-MOSFET to Nanowire-MOSFET 65 P. Kiran Kumar, B. Balaji, M. Suman, P. Syam Sundar, E. Padmaja and K. Girija Sravani 5.1 Introduction 66 5.2 Scaling Challenges Beyond 100nm Node 67 5.3 Alternate Concepts in MOFSETs 69 5.4 Thin-Body Field-Effect Transistors 70 5.4.1 Single-Gate Ultrathin-Body Field-Effect Transistor 71 5.4.2 Multiple-Gate Ultrathin-Body Field-Effect Transistor 73 5.5 Fin-FET Devices 74 5.6 GAA Nanowire-MOSFETS 77 5.7 Conclusion 86 References 86 6 Gate All Around MOSFETs-A Futuristic Approach 95 Ritu Yadav and Kiran Ahuja 6.1 Introduction 95 6.1.1 Semiconductor Technology: History 96 6.2 Importance of Scaling in CMOS Technology 98 6.2.1 Scaling Rules 99 6.2.2 The End of Planar Scaling 100 6.2.3 Enhance Power Efficiency 101 6.2.4 Scaling Challenges 102 6.2.4.1 Poly Silicon Depletion Effect 102 6.2.4.2 Quantum Effect 103 6.2.4.3 Gate Tunneling 103 6.2.5 Horizontal Scaling Challenges 103 6.2.5.1 Threshold Voltage Roll-Off 103 6.2.5.2 Drain Induce Barrier Lowering (DIBL) 103 6.2.5.3 Trap Charge Carrier 104 6.2.5.4 Mobility Degradation 104 6.3 Remedies of Scaling Challenges 104 6.3.1 By Channel Engineering (Horizontal) 104 6.3.1.1 Shallow S/D Junction 105 6.3.1.2 Multi-Material Gate 105 6.3.2 By Gate Engineering (Vertical) 105 6.3.2.1 High-K Dielectric 105 6.3.2.2 Metal Gate 105 6.3.2.3 Multiple Gate 105 6.4 Role of High-K in CMOS Miniaturization 106 6.5 Current Mosfet Technologies 108 6.6 Conclusion 108 References 109 7 Investigation of Diabetic Retinopathy Level Based on Convolution Neural Network Using Fundus Images 113 K. Sasi Bhushan, U. Preethi, P. Naga Sai Navya, R. Abhilash, T. Pavan and K. Girija Sravani 7.1 Introduction 114 7.2 The Proposed Methodology 115 7.3 Dataset Description and Feature Extraction 116 7.3.1 Depiction of Datasets 116 7.3.2 Preprocessing 116 7.3.3 Detection of Blood Vessels 117 7.3.4 Microaneurysm Detection 118 7.4 Results and Discussions 120 7.5 Conclusions 123 References 123 8 Anti-Theft Technology of Museum Cultural Relics Using RFID Technology 127 B. Ramesh Reddy, K. Bhargav Manikanta, P.V.V.N.S. Jaya Sai, R. Mohan Chandra, M. Greeshma Vyas and K. Girija Sravani 8.1 Introduction 128 8.2 Literature Survey 128 8.3 Software Implementation 129 8.4 Components 130 8.4.1 Arduino UNO 130 8.4.2 EM18 Reader Module 130 8.4.3 RFID Tag 131 8.4.4 LCD Display 131 8.4.5 Sensors 132 8.4.5.1 Fire Sensor 132 8.4.5.2 IR Sensor 132 8.4.6 Relay 133 8.5 Working Principle 134 8.5.1 Working Principle 134 8.6 Results and Discussions 135 8.7 Conclusions 137 References 138 9 Smart Irrigation System Using Machine Learning Techniques 139 B. V. Anil Sai Kumar, Suryavamsham Prem Kumar, Konduru Jaswanth, Kola Vishnu and Abhishek Kumar 9.1 Introduction 139 9.2 Hardware Module 141 9.2.1 Soil Moisture Sensor 141 9.2.2 LM35-Temperature Sensor 143 9.2.3 POT Resistor 143 9.2.4 BC-547 Transistor 143 9.2.5 Sounder 144 9.2.6 LCD 16x2 145 9.2.7 Relay 145 9.2.8 Push Button 146 9.2.9 Led 146 9.2.10 Motor 147 9.3 Software Module 148 9.3.1 Proteus Tool 148 9.3.2 Arduino Based Prototyping 149 9.4 Machine Learning (Ml) Into Irrigation 155 9.5 Conclusion 158 References 158 10 Design of Smart Wheelchair with Health Monitoring System 161 Narendra Babu Alur, Kurapati Poorna Durga, Boddu Ganesh, Manda Devakaruna, Lakkimsetti Nandini, A. Praneetha, T. Satyanarayana and K. Girija Sravani 10.1 Introduction 162 10.2 Proposed Methodology 163 10.3 The Proposed System 164 10.4 Results and Discussions 168 10.5 Conclusions 169 References 169 11 Design and Analysis of Anti-Poaching Alert System for Red Sandalwood Safety 171 K. Rani Rudrama, Mounika Ramala, Poorna sasank Galaparti, Manikanta Chary Darla, Siva Sai Prasad Loya and K. Srinivasa Rao 11.1 Introduction 172 11.2 Various Existing Proposed Anti-Poaching Systems 173 11.3 System Framework and Construction 174 11.4 Results and Discussions 176 11.5 Conclusion and Future Scope 182 References 182 12 Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C 6748 185 T. Anil Raju, K. Srihari Reddy, Sk. Arifulla Rabbani, G. Suresh, K. Saikumar Reddy and K. Girija Sravani 12.1 Introduction 186 12.2 Image Processing 186 12.2.1 Image Acquisition 186 12.2.2 Image Segmentation Method 186 12.3 TMS320C6748 DSP Processor 187 12.4 Code Composer Studio 188 12.5 Morphological Image Segmentation 188 12.5.1 Optimization 190 12.6 Results and Discussions 192 12.7 Conclusions 193 References 193 13 Design Challenges for Machine/Deep Learning Algorithms 195 Rajesh C. Dharmik and Bhushan U. Bawankar 13.1 Introduction 196 13.2 Design Challenges of Machine Learning 197 13.2.1 Data of Low Quality 197 13.2.2 Training Data Underfitting 197 13.2.3 Training Data Overfitting 198 13.2.4 Insufficient Training Data 198 13.2.5 Uncommon Training Data 199 13.2.6 Machine Learning Is a Time-Consuming Process 199 13.2.7 Unwanted Features 200 13.2.8 Implementation is Taking Longer Than Expected 200 13.2.9 Flaws When Data Grows 200 13.2.10 The Model’s Offline Learning and Deployment 200 13.2.11 Bad Recommendations 201 13.2.12 Abuse of Talent 201 13.2.13 Implementation 201 13.2.14 Assumption are Made in the Wrong Way 202 13.2.15 Infrastructure Deficiency 202 13.2.16 When Data Grows, Algorithms Become Obsolete 202 13.2.17 Skilled Resources are Not Available 203 13.2.18 Separation of Customers 203 13.2.19 Complexity 203 13.2.20 Results Take Time 203 13.2.21 Maintenance 204 13.2.22 Drift in Ideas 204 13.2.23 Bias in Data 204 13.2.24 Error Probability 204 13.2.25 Inability to Explain 204 13.3 Commonly Used Algorithms in Machine Learning 205 13.3.1 Algorithms for Supervised Learning 205 13.3.2 Algorithms for Unsupervised Learning 206 13.3.3 Algorithm for Reinforcement Learning 206 13.4 Applications of Machine Learning 207 13.4.1 Image Recognition 207 13.4.2 Speech Recognition 207 13.4.3 Traffic Prediction 207 13.4.4 Product Recommendations 208 13.4.5 Email Spam and Malware Filtering 208 13.5 Conclusion 208 References 208 About the Editors 211 Index 213

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