Artificial intelligence (AI) Books
John Wiley & Sons Inc AI and IotBased Intelligent Automation in
Book SynopsisThe 24 chapters in this book provides a deep overview of robotics and the application of AI and IoT in robotics. It contains the exploration of AI and IoT based intelligent automation in robotics. The various algorithms and frameworks for robotics based on AI and IoT are presented, analyzed, and discussed. This book also provides insights on application of robotics in education, healthcare, defense and many other fields which utilize IoT and AI. It also introduces the idea of smart cities using robotics.Table of ContentsPreface xvii 1 Introduction to Robotics 1Srinivas Kumar Palvadi, Pooja Dixit and Vishal Dutt 1.1 Introduction 1 1.2 History and Evolution of Robots 3 1.3 Applications 6 1.4 Components Needed for a Robot 7 1.5 Robot Interaction and Navigation 10 1.5.1 Humanoid Robot 11 1.5.2 Control 11 1.5.3 Autonomy Levels 12 1.6 Conclusion 12 References 13 2 Techniques in Robotics for Automation Using AI and IoT 15Sandeep Kr. Sharma, N. Gayathri, S. Rakesh Kumar and Rajiv Kumar Modanval 2.1 Introduction 16 2.2 Brief History of Robotics 16 2.3 Some General Terms 17 2.4 Requirements of AI and IoT for Robotic Automation 20 2.5 Role of AI and IoT in Robotics 21 2.6 Diagrammatic Representations of Some Robotic Systems 23 2.7 Algorithms Used in Robotics 25 2.8 Application of Robotics 27 2.9 Case Studies 30 2.9.1 Sophia 30 2.9.2 ASIMO 30 2.9.3 Cheetah Robot 30 2.9.4 IBM Watson 31 2.10 Conclusion 31 References 31 3 Robotics, AI and IoT in the Defense Sector 35Rajiv Kumar Modanval, S. Rakesh Kumar, N. Gayathri and Sandeep Kr. Sharma 3.1 Introduction 36 3.2 How Robotics Plays an Important Role in the Defense Sector 36 3.3 Review of the World’s Current Robotics Capabilities in the Defense Sector 38 3.3.1 China 38 3.3.2 United State of America 39 3.3.3 Russia 40 3.3.4 India 41 3.4 Application Areas of Robotics in Warfare 43 3.4.1 Autonomous Drones 43 3.4.2 Autonomous Tanks and Vehicles 44 3.4.3 Autonomous Ships and Submarines 45 3.4.4 Humanoid Robot Soldiers 47 3.4.5 Armed Soldier Exoskeletons 48 3.5 Conclusion 50 3.6 Future Work 50 References 50 4 Robotics, AI and IoT in Medical and Healthcare Applications 53Pooja Dixit, Manju Payal, Nidhi Goyal and Vishal Dutt 4.1 Introduction 53 4.1.1 Basics of AI 53 4.1.1.1 AI in Healthcare 54 4.1.1.2 Current Trends of AI in Healthcare 55 4.1.1.3 Limits of AI in Healthcare 56 4.1.2 Basics of Robotics 57 4.1.2.1 Robotics for Healthcare 57 4.1.3 Basics of IoT 59 4.1.3.1 IoT Scenarios in Healthcare 60 4.1.3.2 Requirements of Security 61 4.2 AI, Robotics and IoT: A Logical Combination 62 4.2.1 Artificial Intelligence and IoT in Healthcare 62 4.2.2 AI and Robotics 63 4.2.2.1 Limitation of Robotics in Medical Healthcare 66 4.2.3 IoT with Robotics 66 4.2.3.1 Overview of IoMRT 67 4.2.3.2 Challenges of IoT Deployment 69 4.3 Essence of AI, IoT, and Robotics in Healthcare 70 4.4 Future Applications of Robotics, AI, and IoT 71 4.5 Conclusion 72 References 72 5 Towards Analyzing Skill Transfer to Robots Based on Semantically Represented Activities of Humans 75Devi.T, N. Deepa, S. Rakesh Kumar, R. Ganesan and N. Gayathri 5.1 Introduction 76 5.2 Related Work 77 5.3 Overview of Proposed System 78 5.3.1 Visual Data Retrieval 79 5.3.2 Data Processing to Attain User Objective 80 5.3.3 Knowledge Base 82 5.3.4 Robot Attaining User Goal 83 5.4 Results and Discussion 83 5.5 Conclusion 85 References 85 6 Healthcare Robots Enabled with IoT and Artificial Intelligence for Elderly Patients 87S. Porkodi and D. Kesavaraja 6.1 Introduction 88 6.1.1 Past, Present, and Future 88 6.1.2 Internet of Things 88 6.1.3 Artificial Intelligence 89 6.1.4 Using Robotics to Enhance Healthcare Services 89 6.2 Existing Robots in Healthcare 90 6.3 Challenges in Implementation and Providing Potential Solutions 90 6.4 Robotic Solutions for Problems Facing the Elderly in Society 98 6.4.1 Solutions for Physical and Functional Challenges 98 6.4.2 Solutions for Cognitive Challenges 98 6.5 Healthcare Management 99 6.5.1 Internet of Things for Data Acquisition 99 6.5.2 Robotics for Healthcare Assistance and Medication Management 102 6.5.3 Robotics for Psychological Issues 103 6.6 Conclusion and Future Directions 103 References 104 7 Robotics, AI, and the IoT in Defense Systems 109Manju Payal, Pooja Dixit, T.V.M. Sairam and Nidhi Goyal 7.1 AI in Defense 110 7.1.1 AI Terminology and Background 110 7.1.2 Systematic Sensing Applications 111 7.1.3 Overview of AI in Defense Systems 112 7.2 Overview of IoT in Defense Systems 114 7.2.1 Role of IoT in Defense 116 7.2.2 Ministry of Defense Initiatives 117 7.2.3 IoT Defense Policy Challenges 117 7.3 Robotics in Defense 118 7.3.1 Technical Challenges of Defense Robots 120 7.4 AI, Robotics, and IoT in Defense: A Logical Mix in Context 123 7.4.1 Combination of Robotics and IoT in Defense 123 7.4.2 Combination of Robotics and AI in Defense 124 7.5 Conclusion 126 References 127 8 Techniques of Robotics for Automation Using AI and the IoT 129Kapil Chauhan and Vishal Dutt 8.1 Introduction 130 8.2 Internet of Robotic Things Concept 131 8.3 Definitions of Commonly Used Terms 132 8.4 Procedures Used in Making a Robot 133 8.4.1 Analyzing Tasks 133 8.4.2 Designing Robots 134 8.4.3 Computerized Reasoning 134 8.4.4 Combining Ideas to Make a Robot 134 8.4.5 Making a Robot 134 8.4.6 Designing Interfaces with Different Frameworks or Robots 134 8.5 IoRT Technologies 135 8.6 Sensors and Actuators 137 8.7 Component Selection and Designing Parts 138 8.7.1 Robot and Controller Structure 140 8.8 Process Automation 141 8.8.1 Benefits of Process Automation 141 8.8.2 Incorporating AI in Process Automation 141 8.9 Robots and Robotic Automation 142 8.10 Architecture of the Internet of Robotic Things 142 8.10.1 Concepts of Open Architecture Platforms 143 8.11 Basic Abilities 143 8.11.1 Discernment Capacity 143 8.11.2 Motion Capacity 144 8.11.3 Manipulation Capacity 144 8.12 More Elevated Level Capacities 145 8.12.1 Decisional Self-Sufficiency 145 8.12.2 Interaction Capacity 145 8.12.3 Cognitive Capacity 146 8.13 Conclusion 146 References 146 9 An Artificial Intelligence-Based Smart Task Responder: Android Robot for Human Instruction Using LSTM Technique 149T. Devi, N. Deepa, SP. Chokkalingam, N. Gayathri and S. Rakesh Kumar 9.1 Introduction 150 9.2 Literature Review 152 9.3 Proposed System 152 9.4 Results and Discussion 157 9.5 Conclusion 161 References 162 10 AI, IoT and Robotics in the Medical and Healthcare Field 165V. Kavidha, N. Gayathri and S. Rakesh Kumar 10.1 Introduction 165 10.2 A Survey of Robots and AI Used in the Health Sector 167 10.2.1 Surgical Robots 167 10.2.2 Exoskeletons 168 10.2.3 Prosthetics 170 10.2.4 Artificial Organs 171 10.2.5 Pharmacy and Hospital Automation Robots 172 10.2.6 Social Robots 173 10.2.7 Big Data Analytics 175 10.3 Sociotechnical Considerations 176 10.3.1 Sociotechnical Influence 176 10.3.2 Social Valence 177 10.3.3 The Paradox of Evidence-Based Reasoning 178 10.4 Legal Considerations 180 10.4.1 Liability for Robotics, AI and IoT 180 10.4.2 Liability for Physicians Using Robotics, AI and IoT 181 10.4.3 Liability for Institutions Using Robotics, AI and IoT 182 10.5 Regulating Robotics, AI and IoT as Medical Devices 183 10.6 Conclusion 185 References 185 11 Real-Time Mild and Moderate COVID-19 Human Body Temperature Detection Using Artificial Intelligence 189K. Logu, T. Devi, N. Deepa, S. Rakesh Kumar and N. Gayathri 11.1 Introduction 190 11.2 Contactless Temperature 191 11.2.1 Bolometers (IR-Based) 192 11.2.2 Thermopile Radiation Sensors (IR-Based) 193 11.2.3 Fiber-Optic Pyrometers 193 11.2.4 RGB Photocell 194 11.2.5 3D Sensor 195 11.3 Fever Detection Camera 196 11.3.1 Facial Recognition 197 11.3.2 Geometric Approach 198 11.3.3 Holistic Approach 198 11.3.4 Model-Based 198 11.3.5 Vascular Network 199 11.4 Simulation and Analysis 200 11.5 Conclusion 203 References 203 12 Drones in Smart Cities 205Manju Payal, Pooja Dixit and Vishal Dutt 12.1 Introduction 206 12.1.1 Overview of the Literature 206 12.2 Utilization of UAVs for Wireless Network 209 12.2.1 Use Cases for WN Using UAVs 209 12.2.2 Classifications and Types of UAVs 210 12.2.3 Deployment of UAVS Using IoT Networks 213 12.2.4 IoT and 5G Sensor Technologies for UAVs 214 12.3 Introduced Framework 217 12.3.1 Architecture of UAV IoT 217 12.3.2 Ground Control Station 218 12.3.3 Data Links 218 12.4 UAV IoT Applications 223 12.4.1 UAV Traffic Management 223 12.4.2 Situation Awareness 223 12.4.3 Public Safety/Saving Lives 225 12.5 Conclusion 227 References 227 13 UAVs in Agriculture 229DeepanshuSrivastava, S. RakeshKumar and N. Gayathri 13.1 Introduction 230 13.2 UAVs in Smart Farming and Take-Off Panel 230 13.2.1 Overview of Systems 230 13.3 Introduction to UGV Systems and Planning 234 13.4 UAV-Hyperspectral for Agriculture 236 13.5 UAV-Based Multisensors for Precision Agriculture 239 13.6 Automation in Agriculture 242 13.7 Conclusion 245 References 245 14 Semi-Automated Parking System Using DSDV and RFID 247Mayank Agrawal, Abhishek Kumar Rawat, Archana, SandhyaKatiyar and Sanjay Kumar 14.1 Introduction 247 14.2 Ad Hoc Network 248 14.2.1 Destination-Sequenced Distance Vector (DSDV) Routing Protocol 248 14.3 Radio Frequency Identification (RFID) 249 14.4 Problem Identification 250 14.5 Survey of the Literature 250 14.6 PANet Architecture 251 14.6.1 Approach for Semi-Automated System Using DSDV 252 14.6.2 Tables for Parking Available/Occupied 253 14.6.3 Algorithm for Detecting the Empty Slots 255 14.6.4 Pseudo Code 255 14.7 Conclusion 256 References 256 15 Survey of Various Technologies Involved in Vehicle-to-Vehicle Communication 259Lisha Kamala K., Sini Anna Alex and Anita Kanavalli 15.1 Introduction 259 15.2 Survey of the Literature 260 15.3 Brief Description of the Techniques 262 15.3.1 ARM and Zigbee Technology 262 15.3.2 VANET-Based Prototype 262 15.3.2.1 Calculating Distance by Considering Parameters 263 15.3.2.2 Calculating Speed by Considering Parameters 263 15.3.3 Wi-Fi–Based Technology 263 15.3.4 Li-Fi–Based Technique 264 15.3.5 Real-Time Wireless System 266 15.4 Various Technologies Involved in V2V Communication 267 15.5 Results and Analysis 267 15.6 Conclusion 268 References 268 16 Smart Wheelchair 271Mekala Ajay, Pusapally Srinivas and Lupthavisha Netam 16.1 Background 271 16.2 System Overview 275 16.3 Health-Monitoring System Using IoT 275 16.4 Driver Circuit of Wheelchair Interfaced with Amazon Alexa 276 16.5 MATLAB Simulations 277 16.5.1 Obstacle Detection 277 16.5.2 Implementing Path Planning Algorithms 278 16.5.3 Differential Drive Robot for Path Following 280 16.6 Conclusion 282 16.7 Future Work 282 Acknowledgment 283 References 283 17 Defaulter List Using Facial Recognition 285Kavitha Esther, Akilindin S.H., Aswin S. and Anand P. 17.1 Introduction 286 17.2 System Analysis 287 17.2.1 Problem Description 287 17.2.2 Existing System 287 17.2.3 Proposed System 287 17.3 Implementation 289 17.3.1 Image Pre-Processing 289 17.3.2 Polygon Shape Family Pre-Processing 289 17.3.3 Image Segmentation 289 17.3.4 Threshold 289 17.3.5 Edge Detection 291 17.3.6 Region Growing Technique 291 17.3.7 Background Subtraction 291 17.3.8 Morphological Operations 291 17.3.9 Object Detection 292 17.4 Inputs and Outputs 292 17.5 Conclusion 292 References 293 18 Visitor/Intruder Monitoring System Using Machine Learning 295G. Jenifa, S. Indu, C. Jeevitha and V. Kiruthika 18.1 Introduction 296 18.2 Machine Learning 296 18.2.1 Machine Learning in Home Security 297 18.3 System Design 297 18.4 Haar-Cascade Classifier Algorithm 298 18.4.1 Creating the Dataset 298 18.4.2 Training the Model 299 18.4.3 Recognizing the Face 299 18.5 Components 299 18.5.1 Raspberry Pi 299 18.5.2 Web Camera 300 18.6 Experimental Results 300 18.7 Conclusion 302 Acknowledgment 302 References 303 19 Comparison of Machine Learning Algorithms for Air Pollution Monitoring System 305Tushr Sethi and R. C. Thakur 19.1 Introduction 305 19.2 System Design 306 19.3 Model Description and Architecture 307 19.4 Dataset 308 19.5 Models 310 19.6 Line of Best Fit for the Dataset 312 19.7 Feature Importance 313 19.8 Comparisons 315 19.9 Results 318 19.10 Conclusion 318 References 321 20 A Novel Approach Towards Audio Watermarking Using FFT and CORDIC-Based QR Decomposition 323Ankit Kumar, Astha Singh, Shiv Prakash and Vrijendra Singh 20.1 Introduction and Related Work 324 20.2 Proposed Methodology 326 20.2.1 Fast Fourier Transform 328 20.2.2 CORDIC-Based QR Decomposition 329 20.2.3 Concept of Cyclic Codes 331 20.2.4 Concept of Arnold’s Cat Map 331 20.3 Algorithm Design 331 20.4 Experiment Results 334 20.5 Conclusion 337 References 338 21 Performance of DC-Biased Optical Orthogonal Frequency Division Multiplexing in Visible Light Communication 339S. Ponmalar and Shiny J.J. 21.1 Introduction 340 21.2 System Model 341 21.2.1 Transmitter Block 341 21.2.2 Receiver Block 342 21.3 Proposed Method 342 21.3.1 Simulation Parameters for OptSim 343 21.3.2 Block Diagram of DCO-OFDM in OptSim 343 21.4 Results and Discussion 344 21.5 Conclusion 352 References 353 22 Microcontroller-Based Variable Rate Syringe Pump for Microfluidic Application 355G. B. Tejashree, S. Swarnalatha, S. Pavithra, M. C. Jobin Christ and N. Ashwin Kumar 22.1 Introduction 356 22.2 Related Work 357 22.3 Methodology 358 22.3.1 Hardware Design 359 22.3.2 Hardware Interface with Software 360 22.3.3 Programming and Debugging 361 22.4 Result 362 22.5 Inference 363 22.5.1 Viscosity (η) 365 22.5.2 Time Taken 365 22.5.3 Syringe Diameter 366 22.5.4 Deviation 366 22.6 Conclusion and Future Works 366 References 368 23 Analysis of Emotion in Speech Signal Processing and Rejection of Noise Using HMM 371S. Balasubramanian 23.1 Introduction 372 23.2 Existing Method 373 23.3 Proposed Method 374 23.3.1 Proposed Module Description 375 23.3.2 MFCC 376 23.3.3 Hidden Markov Models 379 23.4 Conclusion 382 References 383 24 Securing Cloud Data by Using Blend Cryptography with AWS Services 385Vanchhana Srivastava, Rohit Kumar Pathak and Arun Kumar 24.1 Introduction 385 24.1.1 AWS 387 24.1.2 Quantum Cryptography 388 24.1.3 ECDSA 389 24.2 Background 389 24.3 Proposed Technique 392 24.3.1 How the System Works 393 24.4 Results 394 24.5 Conclusion 396 References 396 Index 399
£164.66
John Wiley & Sons Inc In Silico Dreams
Book SynopsisLearn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolutionAn outline of the fundamental breakthroughs in AI and deep learning and their applications across medicineA compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeuticsA summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic developmentA guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.Table of ContentsIntroduction xvii Chapter 1 The Information Revolution’s Impact on Biology 1 A Biological Data Avalanche at Warp Speed 5 Tracking SARS-CoV-2 with Genomic Epidemiology 11 Biology’s Paradigm Shift Enables In Silico Biology 17 Transitions and Computation in Cancer Research 18 Structural Biology and Genomics 24 Sequencing the Human Genome 27 Computational Biology in the Twenty-First Century 33 Applications of Human Genome Sequencing 35 Analyzing Human Genome Sequence Information 37 Omics Technologies and Systems Biology 40 Chapter 2 A New Era of Artificial Intelligence 53 AI Steps Out of the Bronx 55 From Neurons and Cats Brains to Neural Networks 58 Machine Learning and the Deep Learning Breakthrough 66 Deep Learning Arrives for AI 73 Deep Neural Network Architectures 75 Deep Learning’s Beachhead on Medicine: Medical Imaging 78 Limitations on Artificial Intelligence 83 Chapter 3 The Long Road to New Medicines 91 Medicine’s Origins: The Role of Opium Since the Stone Age 96 Industrial Manufacturing of Medicines 102 Paul Ehrlich and the Birth of Chemotherapeutic Drug Discovery 108 The Pharmaceutical Industry: Drugs and War—New Medicines in the Twentieth Century 112 From Synthetic Antibiotics to the Search for New Drugs from the Microbial World 116 Developing Therapeutics for Cancer 119 Antifolates and the Emergence of DNA Synthesis Inhibitors 120 Antibiotics as Cancer Chemotherapeutic Drugs 123 Immunotherapy 125 The Pharmaceutical Business Model in the Twenty-First Century 126 R&D Productivity Challenges Within the Pharmaceutical Industry 131 Sources of Pharmaceutical Innovation: Biotechnology and New Therapeutic Modalities 135 Chapter 4 Gene Editing and the New Tools of Biotechnology 145 Molecular Biology and Biological Information Flow 150 Manipulating Gene Information with Recombinant DNA Technology 154 Genetics, Gene Discovery, and Drugs for Rare Human Diseases 160 Second-Generation Biotechnology Tools: CRISPR- Cas9 and Genome Editing Technologies 167 Human Genome Editing and Clinical Trials 171 Biotechnology to the Rescue: Vaccine Development Platforms Based on Messenger RNA 179 Chapter 5 Healthcare and the Entrance of the Technology Titans 189 Digital Health and the New Healthcare Investment Arena 191 Assessing the Tech Titans as Disruptors in Healthcare 195 Alphabet: Extending Its Tentacles Into Healthcare with Google and Other Bets 196 Apple Inc: Consumer Technology Meets Healthcare 200 Amazon: Taking Logistics to the Next Level for Delivering Healthcare 204 Echoes of the Final Frontier 207 Chapter 6 AI-Based Algorithms in Biology and Medicine 211 Recognizing the Faces of Cancer 217 Tumor Classification Using Deep Learning with Genomic Features 222 AI for Diseases of the Nervous System: Seeing and Changing the Brain 229 Regulatory Approval and Clinical Implementation: Twin Challenges for AI-Based Algorithms in Medicine 234 Chapter 7 AI in Drug Discovery and Development 245 A Brief Survey of In Silico Methods in Drug Discovery 247 Virtual Screening with Cheminformatics and HTS Technologies 250 AI Brings a New Toolset for Computational Drug Design 252 AI-Based Virtual Screening Tools 257 Generative Models for De Novo Drug Design 257 A New Base of Innovation for the Pharmaceutical Industry 259 Atomwise 261 Recursion Pharmaceuticals 262 Deep Genomics 262 Relay Therapeutics 263 Summary 265 Chapter 8 Biotechnology, AI, and Medicine’s Future 269 Building Tools to Decipher Molecular Structures and Biological Systems 272 AlphaFold: Going Deep in Protein Structure Prediction 274 Predicting Genome 3D Organization and Regulatory Elements 276 AI Approaches to Link Genetics-Based Targets to Disease 277 Quantum Computing for In Silico Chemistry and Biology 278 Neuroscience and AI: Modeling Brain and Behavior 280 Brain Information Processing and Modularity: Climbing a Granite Wall 283 Engineering Medicines with Biotechnology and AI 289 Glossary 295 Index 303
£27.99
John Wiley & Sons Inc The Internet of Medical Things Iomt
Book SynopsisTable of ContentsPreface xv 1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins 1Manisha Sritharan and Asita Elengoe 1.1 Introduction 2 1.2 Methodology 4 1.2.1 Sequence of Protein 4 1.2.2 Homology Modeling 4 1.2.3 Physiochemical Characterization 4 1.2.4 Determination of Secondary Models 4 1.2.5 Determination of Stability of Protein Structures 4 1.2.6 Identification of Active Site 4 1.2.7 Preparation of Ligand Model 5 1.2.8 Docking of Target Protein and Phytocompound 5 1.3 Results and Discussion 5 1.3.1 Determination of Physiochemical Characters 5 1.3.2 Prediction of Secondary Structures 7 1.3.3 Verification of Stability of Protein Structures 7 1.3.4 Identification of Active Sites 14 1.3.5 Target Protein-Ligand Docking 14 1.4 Conclusion 18 References 18 2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review 23Saurabh Sharma, Harish K. Shakya and Ashish Mishra 2.1 Introduction 24 2.1.1 Security in Medical Big Data Analytics 24 2.1.1.1 Capture 24 2.1.1.2 Cleaning 25 2.1.1.3 Storage 25 2.1.1.4 Security 26 2.1.1.5 Stewardship 26 2.2 Access Control–Based Security 27 2.2.1 Authentication 27 2.2.1.1 User Password Authentication 28 2.2.1.2 Windows-Based User Authentication 28 2.2.1.3 Directory-Based Authentication 28 2.2.1.4 Certificate-Based Authentication 28 2.2.1.5 Smart Card–Based Authentication 29 2.2.1.6 Biometrics 29 2.2.1.7 Grid-Based Authentication 29 2.2.1.8 Knowledge-Based Authentication 29 2.2.1.9 Machine Authentication 29 2.2.1.10 One-Time Password (OTP) 30 2.2.1.11 Authority 30 2.2.1.12 Global Authorization 30 2.3 System Model 30 2.3.1 Role and Purpose of Design 31 2.3.1.1 Patients 31 2.3.1.2 Cloud Server 31 2.3.1.3 Doctor 31 2.4 Data Classification 32 2.4.1 Access Control 32 2.4.2 Content 33 2.4.3 Storage 33 2.4.4 Soft Computing Techniques for Data Classification 34 2.5 Related Work 36 2.6 Conclusion 42 References 43 3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment 45Nirmala Devi N. and Vengatesh Kumar S. 3.1 Introduction 46 3.1.1 Cloud Computing 46 3.1.1.1 Cloud Service Provider 47 3.1.1.2 Data Storage and Security 47 3.1.2 Virtualization 48 3.1.2.1 Virtualization Terminology 49 3.1.3 Approach to Virtualization 50 3.1.4 Processor Issues 51 3.1.5 Memory Management 51 3.1.6 Benefits of Virtualization 51 3.1.7 Virtual Machine Migration 51 3.1.7.1 Pre-Copy 52 3.1.7.2 Post-Copy 52 3.1.7.3 Stop and Copy 53 3.2 Existing Technology and Its Review 54 3.3 Research Design 56 3.3.1 Basic Overview of VM Pre-Copy Live Migration 57 3.3.2 Improved Pre-Copy Approach 58 3.3.3 Time Series–Based Pre-Copy Approach 60 3.3.4 Memory-Bound Pre-Copy Live Migration 62 3.3.5 Three-Phase Optimization Method (TPO) 62 3.3.6 Multiphase Pre-Copy Strategy 64 3.4 Results 65 3.4.1 Finding 65 3.5 Discussion 69 3.5.1 Limitation 69 3.5.2 Future Scope 70 3.6 Conclusion 70 References 71 4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images 73Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar 4.1 Introduction 74 4.2 Classes of Brain Tumors 75 4.3 Literature Survey 76 4.4 Methodology 78 4.5 Conclusion 93 References 95 5 An Intelligent Healthcare Monitoring System for Coma Patients 99Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L. 5.1 Introduction 100 5.2 Related Works 102 5.3 Materials and Methods 104 5.3.1 Existing System 104 5.3.2 Proposed System 105 5.3.3 Working 105 5.3.4 Module Description 106 5.3.4.1 Pulse Sensor 106 5.3.4.2 Temperature Sensor 107 5.3.4.3 Spirometer 107 5.3.4.4 OpenCV (Open Source Computer Vision) 108 5.3.4.5 Raspberry Pi 108 5.3.4.6 USB Camera 109 5.3.4.7 AVR Module 109 5.3.4.8 Power Supply 109 5.3.4.9 USB to TTL Converter 110 5.3.4.10 EEG of Comatose Patients 110 5.4 Results and Discussion 111 5.5 Conclusion 116 References 117 6 Deep Learning Interpretation of Biomedical Data 121T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral 6.1 Introduction 122 6.2 Deep Learning Models 125 6.2.1 Recurrent Neural Networks 125 6.2.2 LSTM/GRU Networks 127 6.2.3 Convolutional Neural Networks 128 6.2.4 Deep Belief Networks 130 6.2.5 Deep Stacking Networks 131 6.3 Interpretation of Deep Learning With Biomedical Data 132 6.4 Conclusion 139 References 140 7 Evolution of Electronic Health Records 143G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar 7.1 Introduction 143 7.2 Traditional Paper Method 144 7.3 IoMT 144 7.4 Telemedicine and IoMT 145 7.4.1 Advantages of Telemedicine 145 7.4.2 Drawbacks 146 7.4.3 IoMT Advantages with Telemedicine 146 7.4.4 Limitations of IoMT With Telemedicine 147 7.5 Cyber Security 147 7.6 Materials and Methods 147 7.6.1 General Method 147 7.6.2 Data Security 148 7.7 Literature Review 148 7.8 Applications of Electronic Health Records 150 7.8.1 Clinical Research 150 7.8.1.1 Introduction 150 7.8.1.2 Data Significance and Evaluation 151 7.8.1.3 Conclusion 151 7.8.2 Diagnosis and Monitoring 151 7.8.2.1 Introduction 151 7.8.2.2 Contributions 152 7.8.2.3 Applications 152 7.8.3 Track Medical Progression 153 7.8.3.1 Introduction 153 7.8.3.2 Method Used 153 7.8.3.3 Conclusion 154 7.8.4 Wearable Devices 154 7.8.4.1 Introduction 154 7.8.4.2 Proposed Method 155 7.8.4.3 Conclusion 155 7.9 Results and Discussion 155 7.10 Challenges Ahead 157 7.11 Conclusion 158 References 158 8 Architecture of IoMT in Healthcare 161A. Josephin Arockia Dhiyya 8.1 Introduction 161 8.1.1 On-Body Segment 162 8.1.2 In-Home Segment 162 8.1.3 Network Segment Layer 163 8.1.4 In-Clinic Segment 163 8.1.5 In-Hospital Segment 163 8.1.6 Future of IoMT? 164 8.2 Preferences of the Internet of Things 165 8.2.1 Cost Decrease 165 8.2.2 Proficiency and Efficiency 165 8.2.3 Business Openings 165 8.2.4 Client Experience 166 8.2.5 Portability and Nimbleness 166 8.3 loMT Progress in COVID-19 Situations: Presentation 167 8.3.1 The IoMT Environment 168 8.3.2 IoMT Pandemic Alleviation Design 169 8.3.3 Man-Made Consciousness and Large Information Innovation in IoMT 170 8.4 Major Applications of IoMT 171 References 172 9 Performance Assessment of IoMT Services and Protocols 173A. Keerthana and Karthiga 9.1 Introduction 174 9.2 IoMT Architecture and Platform 175 9.2.1 Architecture 176 9.2.2 Devices Integration Layer 177 9.3 Types of Protocols 177 9.3.1 Internet Protocol for Medical IoT Smart Devices 177 9.3.1.1 HTTP 178 9.3.1.2 Message Queue Telemetry Transport (MQTT) 179 9.3.1.3 Constrained Application Protocol (CoAP) 180 9.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP) 181 9.3.1.5 Extensible Message and Presence Protocol (XMPP) 181 9.3.1.6 DDS 183 9.4 Testing Process in IoMT 183 9.5 Issues and Challenges 185 9.6 Conclusion 185 References 185 10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring 187G. Merlin Sheeba and Y. Bevish Jinila 10.1 Introduction 188 10.2 Proposed System Framework 190 10.2.1 System Description 190 10.2.2 Health Monitoring Center 192 10.2.2.1 Body Sensor 192 10.2.2.2 Wireless Sensor Coordinator/Transceiver 192 10.2.2.3 Ontology Information Center 195 10.2.2.4 Mesh Backbone-Placement and Routing 196 10.3 Experimental Evaluation 200 10.4 Performance Evaluation 201 10.4.1 Energy Consumption 201 10.4.2 Survival Rate 201 10.4.3 End-to-End Delay 202 10.5 Conclusion 204 References 204 11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT) 207Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J. 11.1 Introduction 208 11.1.1 Prevalence 209 11.1.2 Management of Diabetes 209 11.1.3 Blood Glucose Monitoring 210 11.1.4 Continuous Glucose Monitors 211 11.1.5 Minimally Invasive Glucose Monitors 211 11.1.6 Non-Invasive Glucose Monitors 211 11.1.7 Existing System 211 11.2 Materials and Methods 212 11.2.1 Artificial Neural Network 212 11.2.2 Data Acquisition 213 11.2.3 Histogram Calculation 213 11.2.4 IoT Cloud Computing 214 11.2.5 Proposed System 215 11.2.6 Advantages 215 11.2.7 Disadvantages 215 11.2.8 Applications 216 11.2.9 Arduino Pro Mini 216 11.2.10 LM78XX 217 11.2.11 MAX30100 218 11.2.12 LM35 Temperature Sensors 218 11.3 Results and Discussion 219 11.4 Summary 222 11.5 Conclusion 222 References 223 12 Wearable Health Monitoring Systems Using IoMT 225Jaya Rubi and A. Josephin Arockia Dhivya 12.1 Introduction 225 12.2 IoMT in Developing Wearable Health Surveillance System 226 12.2.1 A Wearable Health Monitoring System with Multi-Parameters 227 12.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel 228 12.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity 228 12.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry 228 12.3 Vital Parameters That Can Be Monitored Using Wearable Devices 229 12.3.1 Electrocardiogram 230 12.3.2 Heart Rate 231 12.3.3 Blood Pressure 232 12.3.4 Respiration Rate 232 12.3.5 Blood Oxygen Saturation 234 12.3.6 Blood Glucose 235 12.3.7 Skin Perspiration 236 12.3.8 Capnography 238 12.3.9 Body Temperature 239 12.4 Challenges Faced in Customizing Wearable Devices 240 12.4.1 Data Privacy 240 12.4.2 Data Exchange 240 12.4.3 Availability of Resources 241 12.4.4 Storage Capacity 241 12.4.5 Modeling the Relationship Between Acquired Measurement and Diseases 242 12.4.6 Real-Time Processing 242 12.4.7 Intelligence in Medical Care 242 12.5 Conclusion 243 References 244 13 Future of Healthcare: Biomedical Big Data Analysis and IoMT 247Tamiziniyan G. and Keerthana A. 13.1 Introduction 248 13.2 Big Data and IoT in Healthcare Industry 250 13.3 Biomedical Big Data Types 251 13.3.1 Electronic Health Records 252 13.3.2 Administrative and Claims Data 252 13.3.3 International Patient Disease Registries 252 13.3.4 National Health Surveys 253 13.3.5 Clinical Research and Trials Data 254 13.4 Biomedical Data Acquisition Using IoT 254 13.4.1 Wearable Sensor Suit 254 13.4.2 Smartphones 255 13.4.3 Smart Watches 255 13.5 Biomedical Data Management Using IoT 256 13.5.1 Apache Spark Framework 257 13.5.2 MapReduce 258 13.5.3 Apache Hadoop 258 13.5.4 Clustering Algorithms 259 13.5.5 K-Means Clustering 259 13.5.6 Fuzzy C-Means Clustering 260 13.5.7 DBSCAN 261 13.6 Impact of Big Data and IoMT in Healthcare 262 13.7 Discussions and Conclusions 263 References 264 14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review 269Saurabh Sharma, Harish K. Shakya and Ashish Mishra 14.1 Introduction 270 14.2 Blockchain 272 14.2.1 Blockchain Architecture 272 14.2.2 Types of Blockchain Architecture 273 14.2.3 Blockchain Applications 274 14.2.4 General Applications of the Blockchain 276 14.3 Blockchain as a Decentralized Security Framework 277 14.3.1 Characteristics of Blockchain 278 14.3.2 Limitations of Blockchain Technology 280 14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science 281 14.4.1 Data Science in Healthcare 281 14.5 Literature Review: Medical Data Security in Cloud Storage 281 14.6 Conclusion 286 References 287 15 Electronic Health Records: A Transitional View 289Srividhya G. 15.1 Introduction 289 15.2 Ancient Medical Record, 1600 BC 290 15.3 Greek Medical Record 291 15.4 Islamic Medical Record 291 15.5 European Civilization 292 15.6 Swedish Health Record System 292 15.7 French and German Contributions 293 15.8 American Descriptions 293 15.9 Beginning of Electronic Health Recording 297 15.10 Conclusion 298 References 298 Index 301
£169.16
John Wiley & Sons Inc Big Data Analytics and Machine Intelligence in
Book SynopsisBIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data miniTable of ContentsPreface xiii 1 An Introduction to Big Data Analytics Techniques in Healthcare 1Anil Audumbar Pise 1.1 Introduction 1 1.2 Big Data in Healthcare 3 1.3 Areas of Big Data Analytics in Medicine 5 1.4 Healthcare as a Big Data Repository 9 1.5 Applications of Healthcare Big Data 10 1.6 Challenges in Big Data Analytics 16 1.7 Big Data Privacy and Security 17 1.8 Conclusion 18 1.9 Future Work 18 2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique 2.1 Introduction 22 2.2 Literature Review 23 2.3 Methodology and Data Source 25 2.4 Implementation and Results 28 2.5 Conclusion 44 3 Pre-Trained CNN Models in Early Alzheimer's Prediction Using Post-Processed MRI 47Kalyani Gunda and Pradeepini Gera 3.1 Introduction 48 3.2 Experimental Study 51 3.3 Data Exploration 55 3.4 OASIS Dataset Pre-Processing 61 3.5 Alzheimer's 4-Class-MRI Features Extraction 69 3.6 Alzheimer 4-Class MRI Image Dataset 69 3.7 RMSProp (Root Mean Square Propagation) 80 3.8 Activation Function 81 3.9 Batch Normalization 81 3.10 Dropout 81 3.11 Result--I 82 3.12 Conclusion and Future Work 89 4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal 4.1 Introduction 98 4.2 Basics of Proposed Methods 100 4.3 Experimental Results and Discussion 107 4.4 Conclusion 115 5 Analysis of Healthcare Systems Using Computational Approaches 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani 5.1 Introduction 120 5.2 AI & ML Analysis in Health Systems 124 5.3 Healthcare Intellectual Approaches 127 5.4 Precision Approaches to Medicine 133 5.5 Methodology of AI, ML With Healthcare Examples 134 5.6 Big Analytic Data Tools 136 5.7 Discussion 141 5.8 Conclusion 142 6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy 147Shrikaant Kulkarni 6.1 Introduction 148 6.2 AI Methods 149 6.3 Turing Test 156 6.4 Barriers to Technologies 157 6.5 Advantages of AI for Behavioral & Mental Healthcare 157 6.6 Enhanced Self-Care & Access to Care 158 6.7 Other Considerations 160 6.8 Expert Systems in Mental & Behavioral Healthcare 161 6.9 Dynamical Approaches to Clinical AI and Expert Systems 165 6.10 Conclusion 173 6.11 Future Prospects 175 7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra 7.1 Introduction 188 7.2 Related Work 189 7.3 Proposed Frameworks 190 7.4 Results and Discussion 194 7.5 Conclusion 201 8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy 8.1 Introduction 206 8.2 Related Work 212 8.3 Need for Blockchain in Healthcare 216 8.4 Proposed Frameworks 219 8.5 Use Cases 223 8.6 Discussions 229 8.7 Challenges and Limitations 231 8.8 Future Work 231 8.9 Conclusion 232 9 An Epidemic Graph's Modeling Application to the COVID-19 Outbreak 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani 9.1 Introduction 237 9.2 Related Work 239 9.3 Theoretical Approaches 240 9.4 Frameworks 243 9.5 Evaluation of COVID-19 Outbreak 246 9.6 Conclusions and Future Works 250 10 Big Data and Data Mining in e-Health: Legal Issues and Challenges 257Amita Verma and Arpit Bansal Object of Study 257 10.1 Introduction 258 10.2 Big Data and Data Mining in e-Health 260 10.3 Big Data and e-Health in India 262 10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 263 10.5 Big Data and Issues of Privacy in e-Health 271 10.6 Conclusion and Suggestions 272 11 Basic Scientific and Clinical Applications 275Manna Sheela Rani Chetty and Kiran Babu C. V. 11.1 Introduction 275 11.2 Case Study-1: Continual Learning Using ML for Clinical pplications 283 11.3 Case Study-2 286 11.4 Case Study-3: ML Will Improve the Radiology Patient Experience 289 11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 292 11.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 295 11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 298 11.8 Conclusion 300 12 Healthcare Branding Through Service Quality 305Saraju Prasad and Sunil Dhal 12.1 Introduction to Healthcare 305 12.2 Quality in Healthcare 307 12.3 Service Quality 311 12.4 Conclusion and Road Ahead 315 References 316 Index 321
£136.80
John Wiley & Sons Inc Handbook on Intelligent Healthcare Analytics
Book SynopsisHANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reaTable of ContentsPreface xvii 1 An Introduction to Knowledge Engineering and Data Analytics 1D. Karthika and K. Kalaiselvi 1.1 Introduction 2 1.1.1 Online Learning and Fragmented Learning Modeling 2 1.2 Knowledge and Knowledge Engineering 5 1.2.1 Knowledge 5 1.2.2 Knowledge Engineering 5 1.3 Knowledge Engineering as a Modelling Process 6 1.4 Tools 7 1.5 What are KBSs? 8 1.5.1 What is KBE? 8 1.5.2 When Can KBE Be Used? 10 1.5.3 CAD or KBE? 12 1.6 Guided Random Search and Network Techniques 13 1.6.1 Guide Random Search Techniques 13 1.7 Genetic Algorithms 14 1.7.1 Design Point Data Structure 15 1.7.2 Fitness Function 15 1.7.3 Constraints 16 1.7.4 Hybrid Algorithms 16 1.7.5 Considerations When Using a GA 16 1.7.6 Alternative to Genetic-Inspired Creation of Children 17 1.7.7 Alternatives to GA 18 1.7.8 Closing Remarks for GA 18 1.8 Artificial Neural Networks 19 1.9 Conclusion 19 References 20 2 A Framework for Big Data Knowledge Engineering 21Devi T. and Ramachandran A. 2.1 Introduction 22 2.1.1 Knowledge Engineering in AI and Its Techniques 23 2.1.1.1 Supervised Model 23 2.1.1.2 Unsupervised Model 23 2.1.1.3 Deep Learning 24 2.1.1.4 Deep Reinforcement Learning 24 2.1.1.5 Optimization 25 2.1.2 Disaster Management 25 2.2 Big Data in Knowledge Engineering 26 2.2.1 Cognitive Tasks for Time Series Sequential Data 27 2.2.2 Neural Network for Analyzing the Weather Forecasting 27 2.2.3 Improved Bayesian Hidden Markov Frameworks 28 2.3 Proposed System 30 2.4 Results and Discussion 32 2.5 Conclusion 33 References 36 3 Big Data Knowledge System in Healthcare 39P. Sujatha, K. Mahalakshmi and P. Sripriya 3.1 Introduction 40 3.2 Overview of Big Data 41 3.2.1 Big Data: Definition 41 3.2.2 Big Data: Characteristics 42 3.3 Big Data Tools and Techniques 43 3.3.1 Big Data Value Chain 43 3.3.2 Big Data Tools and Techniques 45 3.4 Big Data Knowledge System in Healthcare 45 3.4.1 Sources of Medical Big Data 51 3.4.2 Knowledge in Healthcare 53 3.4.3 Big Data Knowledge Management Systems in Healthcare 55 3.4.4 Big Data Analytics in Healthcare 56 3.5 Big Data Applications in the Healthcare Sector 59 3.5.1 Real Time Healthcare Monitoring and Altering 59 3.5.2 Early Disease Prediction with Big Data 59 3.5.3 Patients Predictions for Improved Staffing 61 3.5.4 Medical Imaging 61 3.6 Challenges with Healthcare Big Data 62 3.6.1 Challenges of Big Data 62 3.6.2 Challenges of Healthcare Big Data 62 3.7 Conclusion 64 References 64 4 Big Data for Personalized Healthcare 67Dhanalakshmi R. and Jose Anand 4.1 Introduction 68 4.1.1 Objectives 68 4.1.2 Motivation 69 4.1.3 Domain Description 70 4.1.4 Organization of the Chapter 70 4.2 Related Literature 71 4.2.1 Healthcare Cyber Physical System Architecture 71 4.2.2 Healthcare Cloud Architecture 71 4.2.3 User Authentication Management 72 4.2.4 Healthcare as a Service (HaaS) 72 4.2.5 Reporting Services 73 4.2.6 Chart and Trend Analysis 73 4.2.7 Medical Data Analysis 73 4.2.8 Hospital Platform Based On Cloud Computing 74 4.2.9 Patient’s Data Collection 74 4.2.10 H-Cloud Challenges 75 4.2.11 Healthcare Information System and Cost 75 4.3 System Analysis and Design 75 4.3.1 Proposed Solution 76 4.3.2 Software Components 76 4.3.3 System Design 76 4.3.4 Architecture Diagram 77 4.3.5 List of Modules 78 4.3.6 Use Case Diagram 81 4.3.7 Sequence Diagram 81 4.3.8 Class Diagram 82 4.4 System Implementation 83 4.4.1 User Interface 83 4.4.2 Storage Module 84 4.4.3 Notification Module 85 4.4.4 Middleware 86 4.4.5 OTP Module 87 4.5 Results and Discussion 88 4.6 Conclusion 90 References 90 5 Knowledge Engineering for AI in Healthcare 93A. Thirumurthi Raja and B. Mahalakshmi 5.1 Introduction 94 5.2 Overview 95 5.2.1 Knowledge Representation 95 5.2.2 Types of Knowledge in Artificial Intelligence 96 5.2.3 Relation Between Knowledge and Intelligence 97 5.2.4 Approaches to Knowledge Representation 97 5.2.5 Requirements for Knowledge Representation System 98 5.2.6 Techniques of Knowledge Representation 98 5.2.6.1 Logical Representation 99 5.2.6.2 Semantic Network Representation 99 5.2.6.3 Frame Representation 99 5.2.6.4 Production Rules 100 5.2.7 Process of Knowledge Engineering 101 5.2.8 Knowledge Discovery Process 106 5.3 Applications of Knowledge Engineering in AI for Healthcare 106 5.3.1 AI Supports in Clinical Decisions 107 5.3.2 AI-Assisted Robotic Surgery 107 5.3.3 Enhance Primary Care and Triage 108 5.3.4 Clinical Judgments or Diagnosis 108 5.3.5 Precision Medicine 109 5.3.6 Drug Discovery 109 5.3.7 Deep Learning to Diagnose Diseases 110 5.3.8 Automating Administrative Tasks 111 5.3.9 Reducing Operational Costs 112 5.3.10 Virtual Nursing Assistants 113 5.4 Conclusion 113 References 114 6 Business Intelligence and Analytics from Big Data to Healthcare 115Maheswari P., A. Jaya and João Manuel R. S. Tavares 6.1 Introduction 116 6.1.1 Impact of Healthcare Industry on Economy 116 6.1.2 Coronavirus Impact on the Healthcare Industry 117 6.1.3 Objective of the Study 117 6.1.4 Limitations of the Study 117 6.2 Related Works 118 6.3 Conceptual Healthcare Stock Prediction System 120 6.3.1 Data Source 122 6.3.2 Business Intelligence and Analytics Framework 122 6.3.2.1 Simple Machine Learning Model 122 6.3.2.2 Time Series Forecasting 123 6.3.2.3 Complex Deep Neural Network 123 6.3.3 Predicting the Stock Price 124 6.4 Implementation and Result Discussion 124 6.4.1 Apollo Hospitals Enterprise Limited 125 6.4.2 Cadila Healthcare Ltd 125 6.4.3 Dr. Reddy’s Laboratories 128 6.4.4 Fortis Healthcare Limited 130 6.4.5 Max Healthcare Institute Limited 131 6.4.6 Opto Circuits Limited 131 6.4.7 Panacea Biotec 135 6.4.8 Poly Medicure Ltd 136 6.4.9 Thyrocare Technologies Limited 138 6.4.10 Zydus Wellness Ltd 138 6.5 Comparisons of Healthcare Stock Prediction Framework 141 6.6 Conclusion and Future Enhancement 143 References 143 Books 145 Web Citation 145 7 Internet of Things and Big Data Analytics for Smart Healthcare 147Sathish Kumar K., Om Prakash P.G., Alangudi Balaji N. and Robertas Damaševičius 7.1 Introduction 148 7.2 Literature Survey 149 7.3 Smart Healthcare Using Internet of Things and Big Data Analytics 151 7.3.1 Smart Diabetes Prediction 151 7.3.2 Smart ADHD Prediction 154 7.4 Security for Internet of Things 159 7.4.1 K(Binary) ECC FSM 159 7.4.2 NAF Method 160 7.4.3 K-NAF Multiplication Architecture 161 7.4.4 K(NAF) ECC FSM 161 7.5 Conclusion 164 References 165 8 Knowledge-Driven and Intelligent Computing in Healthcare 167R. Mervin, Dinesh Mavalaru and Tintu Thomas 8.1 Introduction 168 8.1.1 Basics of Health Recommendation System 169 8.1.2 Basics of Ontology 169 8.1.3 Need of Ontology in Health Recommendation System 170 8.2 Literature Review 171 8.2.1 Ontology in Various Domain 172 8.2.2 Ontology in Health Recommendation System 174 8.3 Framework for Health Recommendation System 175 8.3.1 Domain Ontology Creation 176 8.3.2 Query Pre-Processing 178 8.3.3 Feature Selection 179 8.3.4 Recommendation System 180 8.4 Experimental Results 182 8.5 Conclusion and Future Perspective 183 References 183 9 Secure Healthcare Systems Based on Big Data Analytics 189A. Angel Cerli, K. Kalaiselvi and Vijayakumar Varadarajan 9.1 Introduction 190 9.2 Healthcare Data 193 9.2.1 Structured Data 193 9.2.2 Unstructured Data 194 9.2.3 Semi-Structured Data 194 9.2.4 Genomic Data 194 9.2.5 Patient Behavior and Sentiment Data 194 9.2.6 Clinical Data and Clinical Notes 194 9.2.7 Clinical Reference and Health Publication Data 195 9.2.8 Administrative and External Data 195 9.3 Recent Works in Big Data Analytics in Healthcare Data 195 9.4 Healthcare Big Data 197 9.5 Privacy of Healthcare Big Data 198 9.6 Privacy Right by Country and Organization 200 9.7 How Blockchain is Big Data Usable for Healthcare 200 9.7.1 Digital Trust 200 9.7.2 Smart Data Tracking 202 9.7.3 Ecosystem Sensible 202 9.7.4 Switch Digital 202 9.7.5 Cybersecurity 203 9.7.6 Sharing Interoperability and Data 203 9.7.7 Improving Research and Development (R&D) 206 9.7.8 Drugs Fighting Counterfeit 206 9.7.9 Patient Mutual Participation 206 9.7.10 Internet Access by Patient to Longitudinal Data 206 9.7.11 Data Storage into Off Related to Confidentiality and Data Scale 207 9.8 Blockchain Threats and Medical Strategies Big Data Technology 207 9.9 Conclusion and Future Research 208 References 208 10 Predictive and Descriptive Analysis for Healthcare Data 213Pritam R. Ahire and Rohini Hanchate 10.1 Introduction 214 10.2 Motivation 215 10.2.1 Healthcare Analysis 215 10.2.2 Predictive Analytics 217 10.2.3 Predictive Analytics Current Trends 217 10.2.3.1 Importance of PA 217 10.2.4 Descriptive Analysis 218 10.2.4.1 Descriptive Statistics 218 10.2.4.2 Categories of Descriptive Analysis 219 10.2.5 Method of Modeling 221 10.2.6 Measures of Data Analytics 221 10.2.7 Healthcare Data Analytics Platforms and Tools 223 10.2.8 Challenges 225 10.2.9 Issues in Predictive Healthcare Analysis 226 10.2.9.1 Integrating Separate Data Sources 226 10.2.9.2 Advanced Cloud Technologies 226 10.2.9.3 Privacy and Security 227 10.2.9.4 The Fast Pace of Technology Changes 227 10.2.10 Applications of Predictive Analysis 227 10.2.10.1 Improving Operational Efficiency 227 10.2.10.2 Personal Medicine 228 10.2.10.3 Population Health and Risk Scoring 228 10.2.10.4 Outbreak Prediction 228 10.2.10.5 Controlling Patient Deterioration 228 10.2.10.6 Supply Chain Management 228 10.2.10.7 Potential in Precision Medicine 229 10.2.10.8 Cost Savings From Reducing Waste and Fraud 229 10.3 Conclusion 229 References 229 11 Machine and Deep Learning Algorithms for Healthcare Applications 233K. France, A. Jaya and Doru Tiliute 11.1 Introduction 234 11.2 Artificial Intelligence, Machine Learning, and Deep Learning 234 11.3 Machine Learning 236 11.3.1 Supervised Learning 236 11.3.2 Unsupervised Learning 238 11.3.3 Semi-Supervised 238 11.3.4 Reinforcement Learning 238 11.4 Advantages of Using Deep Learning on Top of Machine Learning 239 11.5 Deep Learning Architecture 239 11.6 Medical Image Analysis using Deep Learning 242 11.7 Deep Learning in Chest X-Ray Images 243 11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval 246 11.9 Image Retrieval Performance Metrics 249 11.10 Conclusion 250 References 250 12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 255S. Asha, Kanchana Devi V. and G. Sahaja Vaishnavi 12.1 Introduction 256 12.2 Literature Review 260 12.3 AI in Healthcare 266 12.4 Data Science and Knowledge Engineering for COVID-19 268 12.5 Proposed Architecture and Its Implementation 270 12.5.1 Implementation 270 12.5.1.1 Data Collection 270 12.5.1.2 Understanding Class and Dependencies 270 12.5.1.3 Pre-Processing 272 12.5.1.4 Sampling 273 12.5.1.5 Model Fixing 273 12.5.1.6 Analysis of Real-Time Datasets 273 12.5.1.7 Machine Learning Algorithms 276 12.6 Conclusions and Future Work 278 References 280 13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 285Agasba Saroj S. J., B. Saleena and B. Prakash 13.1 Introduction 285 13.1.1 Motivation 287 13.2 Ongoing Research on Intelligent Decision Support System 289 13.3 Methodology and Architecture of the Intelligent Rule-Based System 291 13.3.1 Proposed System Design 292 13.3.2 Algorithms Used 293 13.3.2.1 Forward Chaining 293 13.3.2.2 Backward Chaining 294 13.4 Creating a Rule-Based System using Prolog 295 13.5 Results and Discussions 304 13.6 Conclusion 306 13.7 Acknowledgments 307 References 307 14 Big Data in Healthcare: Management, Analysis, and Future Prospects 309A. Akila, R. Parameswari and C. Jayakumari 14.1 Introduction 309 14.2 Breast Cancer: Overview 310 14.3 State-of-the-Art Technology in Treatment of Cancer 311 14.3.1 Chemotherapy 311 14.3.2 Radiotherapy 311 14.4 Early Diagnosis of Breast Cancer: Overview 312 14.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer 312 14.4.2 Diagnosis the Breast Cancer 313 14.5 Literature Review 314 14.6 Machine Learning Algorithms 315 14.6.1 Principal Component Analysis Algorithms 316 14.6.2 K-Means Algorithm 317 14.6.3 K-Nearest Neighbor Algorithm 317 14.6.4 Logistic Regression Algorithm 318 14.6.5 Support Vector Machine Algorithm 318 14.6.6 AdaBoost Algorithm 319 14.6.7 Neural Networks Algorithm 319 14.6.8 Random Forest Algorithm 319 14.7 Result and Discussion 320 14.7.1 Performance Metrics 320 14.7.1.1 ROC Curve 320 14.7.1.2 Accuracy 321 14.7.1.3 Precision and Recall 321 14.7.1.4 F1-Score 322 14.8 Experimental Result and Discussion 322 14.9 Conclusion 324 References 325 15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 327G. Jaculine Priya and S. Saradha 15.1 Introduction 327 15.2 Machine Learning in Healthcare 329 15.3 Types of Learnings in Machine Learning 331 15.3.1 Supervised Learning 332 15.3.2 Unsupervised Algorithms 333 15.3.3 Semi-Supervised Learning 334 15.3.4 Reinforcement Learning 334 15.4 Types of Machine Learning Algorithms 334 15.4.1 Classification 335 15.4.2 Bayes Classification 335 15.4.3 Association Analysis 335 15.4.4 Correlation Analysis 336 15.4.5 Cluster Analysis 336 15.4.6 Outlier Analysis 336 15.4.7 Regression Analysis 337 15.4.8 K-Means 337 15.4.9 Apriori Algorithm 337 15.4.10 K Nearest Neighbor 337 15.4.11 Naive Bayes 338 15.4.12 AdaBoost 338 15.4.13 Support Vector Machine 338 15.4.14 Classification and Regression Trees 339 15.4.15 Linear Discriminant Analysis 339 15.4.16 Logistic Regression 339 15.4.17 Linear Regression 339 15.4.18 Principal Component Analysis 339 15.5 Machine Learning for Information Extraction 340 15.5.1 Natural Language Processing 340 15.6 Predictive Analysis in Healthcare 341 15.7 Conclusion 342 References 342 16 Knowledge Fusion Patterns in Healthcare 345N. Deepa and N. Kanimozhi 16.1 Introduction 346 16.2 Related Work 348 16.3 Materials and Methods 349 16.3.1 Classification of Data Fusion 349 16.3.2 Levels and Its Working in Healthcare Ecosystems 351 16.3.2.1 Initial Level Data Access (ILA) 351 16.3.2.2 Middle Level Access (MLA) 352 16.3.2.3 High Level Access (HLA) 352 16.4 Proposed System 352 16.4.1 Objective 353 16.4.2 Sample Dataset 355 16.5 Results and Discussion 355 16.6 Conclusion and Future Work 361 References 362 17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 365J.K. Adedeji, T.O. Owolabi and R.S. Fayose 17.1 Introduction 366 17.2 Materials and Methods 367 17.2.1 Data Acquisition and Pre-Processing 367 17.2.2 Sickle Cells Normalization Image 368 17.2.3 Gradient Calculation 369 17.2.4 Gradient Descent Step 371 17.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks 372 17.2.6 Segments of Convolutional Neural Networks 372 17.2.6.1 Convolutional Layer 372 17.2.6.2 Pooling Layer 373 17.2.6.3 Fully Connected Layer 374 17.2.6.4 Softmax Layer 374 17.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare 374 17.2.8 Algorithm Review and Comparison 376 17.2.9 Feedforward 376 17.3 Results and Discussion 377 17.3.1 Results on Suitability for Applications in Healthcare 377 17.3.2 Class Prediction 377 17.3.3 The Model Sanity Checking 377 17.3.4 Analysis of the Epoch and Training Losses 378 17.3.5 Discussion and Healthcare Interpretations 379 17.3.6 Load Data 379 17.3.7 Image Pre-Processing 380 17.3.8 Building and Training the Classifier 381 17.3.9 Saving the Checkpoint Suitable for Healthcare 382 17.3.10 Loading the Checkpoint 383 17.4 Conclusion 383 References 383 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 387Niha K. and Aisha Banu W. 18.1 Introduction 388 18.2 Related Work 389 18.3 Convolutional Layer 389 18.4 Pooling Layer 390 18.5 Fully Connected Layer 390 18.6 Recurrent Neural Network 391 18.7 LSTM and GRU 392 18.8 Materials and Methods 397 18.8.1 Pre-Processing Strategy Selection 397 18.8.2 Feature Extraction and Classification 400 18.9 Results and Discussions 406 18.10 Conclusion 408 18.11 Acknowledgement 409 References 409 Index 413
£153.90
John Wiley & Sons Inc Machine Learning in Chemical Safety and Health
Book SynopsisIntroduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validatTable of ContentsList of Contributors xiii Preface xvii 1 Introduction 1 Pingfan Hu and Qingsheng Wang 1.1 Background 2 1.2 Current State 5 1.2.1 Flammability Characteristics Prediction Using Quantitative Structure–Property Relationship 5 1.2.2 Consequence Prediction Using Quantitative Property–Consequence Relationship 6 1.2.3 Machine Learning in Process Safety and Asset Integrity Management 6 1.2.4 Machine Learning for Process Fault Detection and Diagnosis 7 1.2.5 Intelligent Method for Chemical Emission Source Identification 7 1.2.6 Machine Learning and Deep Learning Applications in Medical Image Analysis 7 1.2.7 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials 8 1.2.8 Machine Learning in Environmental Exposure Assessment 8 1.2.9 Air Quality Prediction Using Machine Learning 8 1.3 Software and Tools 9 1.3.1 R 9 1.3.2 Python 12 References 13 2 Machine Learning Fundamentals 19 Yan Yan 2.1 What Is Learning? 19 2.1.1 Machine Learning Applications and Examples 20 2.1.2 Machine Learning Tasks 21 2.2 Concepts of Machine Learning 22 2.3 Machine Learning Paradigms 24 2.4 Probably Approximately Correct Learning 25 2.4.1 Deterministic Setting 26 2.4.2 Stochastic Setting 29 v 0005453285.3D 5 30/8/2022 8:51:33 PM 2.5 Estimation and Approximation 31 2.6 Empirical Risk Minimization 32 2.6.1 Empirical Risk Minimizer 32 2.6.2 VC-dimension Generalization Bound 33 2.6.3 General Loss Functions 34 2.7 Regularization 35 2.7.1 Regularized Loss Minimization 35 2.7.2 Constrained and Regularized Problem 36 2.7.3 Trade-off Between Estimation and Approximation Error 37 2.8 Maximum Likelihood Principle 38 2.8.1 Maximum Likelihood Estimation 39 2.8.2 Cross Entropy Minimization 40 2.9 Optimization 41 2.9.1 Linear Regression: An Example 42 2.9.2 Closed-form Solution 42 2.9.3 Gradient Descent 43 2.9.4 Stochastic Gradient Descent 45 References 46 3 Flammability Characteristics Prediction Using QSPR Modeling 47 Yong Pan and Juncheng Jiang 3.1 Introduction 47 3.1.1 Flammability Characteristics 47 3.1.2 QSPR Application 48 3.1.2.1 Concept of QSPR 48 3.1.2.2 Trends and Characteristics of QSPR 48 3.2 Flowchart for Flammability Characteristics Prediction 49 3.2.1 Dataset Preparation 51 3.2.2 Structure Input and Molecular Simulation 52 3.2.3 Calculation of Molecular Descriptors 53 3.2.4 Preliminary Screening of Molecular Descriptors 54 3.2.5 Descriptor Selection and Modeling 55 3.2.6 Model Validation 57 3.2.6.1 Model Fitting Ability Evaluation 57 3.2.6.2 Model Stability Analysis 59 3.2.6.3 Model Predictivity Evaluation 60 3.2.7 Model Mechanism Explanation 61 3.2.8 Summary of QSPR Process 61 3.3 QSPR Review for Flammability Characteristics 62 3.3.1 Flammability Limits 62 3.3.1.1 LFLT and LFL 62 3.3.1.2 UFLT and UFL 64 3.3.2 Flash Point 65 3.3.3 Auto-ignition Temperature 68 3.3.4 Heat of Combustion 69 vi Contents 0005453285.3D 6 30/8/2022 8:51:33 PM 3.3.5 Minimum Ignition Energy 70 3.3.6 Gas-liquid Critical Temperature 70 3.3.7 Other Properties 72 3.4 Limitations 72 3.5 Conclusions and Future Prospects 73 References 73 4 Consequence Prediction and Quantitative Property–Consequence Relationship Models 81 Zeren Jiao and Qingsheng Wang 4.1 Introduction 81 4.2 Conventional Consequence Prediction Methods 82 4.2.1 Empirical Method 82 4.2.2 Computational Fluid Dynamics (CFD) Method 83 4.2.3 Integral Method 84 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models 84 4.4 Quantitative Property–Consequence Relationship Models 86 4.4.1 Consequence Database 88 4.4.2 Property Descriptors 89 4.4.3 Machine Learning and Deep Learning Algorithms 89 4.5 Challenges and Future Directions 90 References 91 5 Machine Learning in Process Safety and Asset Integrity Management 93 Ming Yang ,Hao Sun and Rustam Abubarkirov 5.1 Opportunities and Threats 93 5.2 State-of-the-Art Reviews 95 5.2.1 Artificial Neural Networks (ANNs) 95 5.2.2 Principal Component Analysis (PCA) 97 5.2.3 Genetic Algorithm (GA) 97 5.3 Case Study of Asset Integrity Assessment 98 5.4 Data-Driven Model of Asset Integrity Assessment 105 5.4.1 Condition Monitoring Data Collection 106 5.4.2 Data Processing and Storage 106 5.4.3 Data Mining for Risk Quantification and Monitoring Control 107 5.4.4 AIM Application 107 5.4.5 The Application of the Framework 108 5.5 Conclusion 109 References 109 6 Machine Learning for Process Fault Detection and Diagnosis 113 Rajeevan Arunthavanathan, Salim Ahmed, Faisal Khan and Syed Imtiaz 6.1 Background 113 6.2 Machine Learning Approaches in Fault Detection and Diagnosis 114 6.3 Supervised Methods for Fault Detection and Diagnosis 115 Contents vii 0005453285.3D 7 30/8/2022 8:51:33 PM 6.3.1 Neural Network 115 6.3.1.1 Neural Network Theory and Algorithm 115 6.3.1.2 Neural Network Learning for Fault Classification 117 6.3.1.3 Algorithm for Fault Classification Using Neural Network 118 6.3.2 Support Vector Machine 118 6.3.2.1 Support Vector Machine Theory and Algorithm 118 6.3.3 Support Vector Machine Model Selection and Algorithm 120 6.3.4 Support Vector Machine Multiclass Classification 121 6.4 Unsupervised Learning Models for Fault Detection and Diagnosis 122 6.4.1 K-Nearest Neighbors 122 6.4.2 One-Class Support Vector Machine 123 6.4.3 One-Class Neural Network 124 6.4.4 Comparison Between Deep Learning with Machine Learning in Fault Detection and Diagnosis 126 6.5 Intelligent FDD Using Machine Learning 127 6.5.1 Model Development 127 6.5.2 Data Collection 129 6.5.2.1 Model Development Steps 129 6.5.2.2 Result Comparison 130 6.6 Concluding Remarks 134 References 134 7 Intelligent Method for Chemical Emission Source Identification 139 Denglong Ma 7.1 Introduction 139 7.1.1 Development of Detecting Gas Emission 139 7.1.2 Development of Source Term Identification 140 7.2 Intelligent Methods for Recognizing Gas Emission 141 7.2.1 Leakage Recognition of Sequestrated CO2 in the Atmosphere 141 7.2.1.1 Gas Leakage Recognition for CO2 Geological Sequestration 142 7.2.1.2 Case Studies for CO2 Recognition 144 7.2.2 Emission Gas Identification with Artificial Olfactory 149 7.2.2.1 Features of Responses in AOS 150 7.2.2.2 Support Vector Machine Models for Gas Identification 150 7.2.2.3 Deep Learning Models for Gas Identification 155 7.3 Intelligent Methods for Identifying Emission Sources 158 7.3.1 Source Estimation with Intelligent Optimization Method 158 7.3.1.1 Principle of Source Estimation with Optimization Method 158 7.3.1.2 Case Studies of Source Estimation with Optimization Method 159 7.3.2 Source Estimation with MRE-PSO Method 159 7.3.2.1 Principle of PSO-MRE for Source Estimation 161 7.3.2.2 Case Studies 163 7.3.3 Source Estimation with PSO-Tikhonov Regulation Method 164 7.3.3.1 Principle of PSO-Tikhonov Regularization Hybrid Method 164 7.3.3.2 Case Study 167 viii Contents 0005453285.3D 8 30/8/2022 8:51:33 PM 7.3.4 Source Estimation with MCMC-MLA Method 168 7.3.4.1 Forward Gas Dispersion Model Based on MLA 168 7.3.4.2 Source Estimation with MCMC-MLA Method 169 7.3.4.3 Case Study 172 7.4 Conclusions and Future Work 173 7.4.1 Conclusions 173 7.4.2 Limitations and Future Work 177 References 178 8 Machine Learning and Deep Learning Applications in Medical Image Analysis 183 Pingfan Hu, Changjie Cai, Yu Feng and Qingsheng Wang 8.1 Introduction 183 8.1.1 Machine Learning in Medical Imaging 183 8.1.2 Deep Learning in Medical Imaging 183 8.2 CNN-Based Models for Classification 184 8.2.1 ResNet50 184 8.2.2 YOLOv4 (Darknet53) 185 8.2.3 Grad-CAM 186 8.3 Case Study 186 8.3.1 Background 186 8.3.2 Study Design 187 8.3.3 Training and Testing Database Preparation 187 8.3.4 Results 190 8.3.4.1 Classification Performance of the Modified ResNet50 Model 190 8.3.4.2 Classification Performance of the YOLOv4 Model 190 8.3.4.3 Post-Processing Via Grad-CAM Model and HSV 193 8.3.5 Conclusion 194 8.4 Limitations and Future Work 194 References 195 9 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials 199 Bilal M. Khan and Yoram Cohen 9.1 Predictive Nanotoxicology 199 9.1.1 Introduction 199 9.1.2 Nano Quantitative Structure–Activity Relationship (QSAR) 200 9.1.3 Importance of Data for Nanotoxicology 204 9.2 Machine Learning Modeling for Predictive Nanotoxicology 205 9.2.1 Overview 205 9.2.2 Unsupervised Learning 211 9.2.2.1 Data Exploration Via Self-Organizing Maps (SOMs) 211 9.2.2.2 Evaluating Associations among Sublethal Toxicity Responses 214 9.2.3 Supervised Learning 215 9.2.3.1 Random Forest Models 216 Contents ix 0005453285.3D 9 30/8/2022 8:51:33 PM 9.2.3.2 Support Vector Machines 216 9.2.3.3 Bayesian Networks 216 9.2.3.4 Supervised Classification and Regression-Based Models for Nano-(Q)SARs 218 9.2.4 Predictive Nano-(Q)SARs for the Assessment of Causal Relationships 220 9.3 Development of Machine Learning Based Models for Nano-(Q)SARs 224 9.3.1 Overview 224 9.3.1.1 Data-Driven Models 224 9.3.1.2 Mechanistic/Theoretical Models 225 9.3.2 Data Generation, Collection, and Preprocessing 225 9.3.3 Descriptor Selection 226 9.3.4 Model Selection and Training 229 9.3.5 Model Validation 230 9.3.5.1 Descriptor Importance 231 9.3.5.2 Applicability Domain 231 9.3.6 Model Diagnosis and Debugging 231 9.4 Nanoinformatics Approaches to Predictive Nanotoxicology 234 9.5 Summary 235 References 238 10 Machine Learning in Environmental Exposure Assessment 251 Gregory L. Watson 10.1 Introduction 251 10.2 Environmental Exposure Modeling 252 10.3 Machine Learning Exposure Models 254 10.4 Model Evaluation 257 10.5 Case Study 258 10.6 Other Topics 260 10.6.1 Bias and Fairness 260 10.6.2 Wearable Sensors 260 10.6.3 Interpretability 260 10.6.4 Extreme Events 260 10.7 Conclusion 261 References 261 11 Air Quality Prediction Using Machine Learning 267 Lan Gao, Changjie Cai and Xiao-Ming Hu 11.1 Introduction 267 11.2 Air Quality and Climate Data Acquisition 269 11.2.1 Earth Satellite Observation Datasets 269 11.2.1.1 Basics of Earth Satellite Observations 269 11.2.1.2 Earth Satellite Products 270 11.2.2 Ground-Based In Situ Observation Datasets 276 11.2.2.1 Basics of the Ground-Based In Situ Observations 276 11.2.2.2 Ground-Based In Situ Products 277 11.3 Applications of Machine Learning in Air Quality Study 279 x Contents 0005453285.3D 10 30/8/2022 8:51:34 PM 11.3.1 Shallow Learning 280 11.3.2 Deep Learning 280 11.4 An Application Practice Example 281 11.4.1 Satellite Data Acquisition and Variable Selections 282 11.4.2 Machine Learning and Deep Learning Algorithms 282 References 283 12 Current Challenges and Perspectives 289 Changjie Cai and Qingsheng Wang 12.1 Current Challenges 289 12.1.1 Data Development and Cleaning 289 12.1.2 Hardware Issues 290 12.1.3 Data Confidentiality 290 12.1.4 Other Challenges 291 12.2 Perspectives 291 12.2.1 Real-Time Monitoring and Forecast of Chemical Hazards 291 12.2.2 Toolkits for Dummies 292 12.2.3 Physics-Informed Machine Learning 292 References 293 Index 000
£104.00
John Wiley & Sons Inc Machine Learning for Civil and Environmental
Book SynopsisTable of ContentsPreface xiii About the Companion Website xix 1 Teaching Methods for This Textbook 1 Synopsis 1 1.1 Education in Civil and Environmental Engineering 1 1.2 Machine Learning as an Educational Material 2 1.3 Possible Pathways for Course/Material Delivery 3 1.4 Typical Outline for Possible Means of Delivery 7 Chapter Blueprint 8 Questions and Problems 8 References 8 2 Introduction to Machine Learning 11 Synopsis 11 2.1 A Brief History of Machine Learning 11 2.2 Types of Learning 12 2.3 A Look into ML from the Lens of Civil and Environmental Engineering 15 2.4 Let Us Talk a Bit More about ML 17 2.5 ML Pipeline 18 2.6 Conclusions 27 Definitions 27 Chapter Blueprint 29 Questions and Problems 29 References 30 3 Data and Statistics 33 Synopsis 33 3.1 Data and Data Science 33 3.2 Types of Data 34 3.3 Dataset Development 37 3.4 Diagnosing and Handling Data 37 3.5 Visualizing Data 38 3.6 Exploring Data 59 3.7 Manipulating Data 66 3.8 Manipulation for Computer Vision 68 3.9 A Brief Review of Statistics 68 3.10 Conclusions 76 4 Machine Learning Algorithms 81 Synopsis 81 4.1 An Overview of Algorithms 81 4.2 Conclusions 127 5 Performance Fitness Indicators and Error Metrics 133 Synopsis 133 5.1 Introduction 133 5.2 The Need for Metrics and Indicators 134 5.3 Regression Metrics and Indicators 135 5.4 Classification Metrics and Indicators 142 5.5 Clustering Metrics and Indicators 142 5.6 Functional Metrics and Indicators* 151 5.7 Other Techniques (Beyond Metrics and Indicators) 154 5.8 Conclusions 159 6 Coding-free and Coding-based Approaches to Machine Learning 169 Synopsis 169 6.1 Coding-free Approach to ML 169 6.2 Coding-based Approach to ML 280 6.3 Conclusions 322 7 Explainability and Interpretability 327 7 Synopsis 327 7.1 The Need for Explainability 327 7.2 Explainability from a Philosophical Engineering Perspective* 329 7.3 Methods for Explainability and Interpretability 331 7.4 Examples 335 7.5 Conclusions 428 8 Causal Discovery and Causal Inference 433 Synopsis 433 8.1 Big Ideas Behind This Chapter 433 8.2 Re-visiting Experiments 434 8.3 Re-visiting Statistics and ML 435 8.4 Causality 436 8.5 Examples 451 8.6 A Note on Causality and ML 475 8.7 Conclusions 475 9 Advanced Topics (Synthetic and Augmented Data, Green ML, Symbolic Regression, Mapping Functions, Ensembles, and AutoML) 481 Synopsis 481 9.1 Synthetic and Augmented Data 481 9.2 Green ML 488 9.3 Symbolic Regression 498 9.4 Mapping Functions 529 9.5 Ensembles 539 9.6 AutoML 548 9.7 Conclusions 552 10 Recommendations, Suggestions, and Best Practices 559 Synopsis 559 10.1 Recommendations 559 10.2 Suggestions 564 10.3 Best Practices 566 11 Final Thoughts and Future Directions 573 Synopsis 573 11.1 Now 573 11.2 Tomorrow 573 11.3 Possible Ideas to Tackle 575 11.4 Conclusions 576 References 576 Index 577
£58.50
John Wiley & Sons Inc A Roadmap for Enabling Industry 4.0 by Artificial
Book SynopsisA ROADMAP FOR ENABLING INDUSTRY 4.0 BY ARTIFICAIAL INTELLIGENCE The book presents comprehensive and up-to-date technological solutions to the main aspects regarding the applications of artificial intelligence to Industry 4.0. The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework Table of ContentsPreface xv 1 Artificial Intelligence—The Driving Force of Industry 4.0 1 Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy 1.1 Introduction 2 1.2 Methodology 2 1.3 Scope of AI in Global Economy and Industry 4.0 3 1.3.1 Artificial Intelligence—Evolution and Implications 4 1.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 5 1.3.3 The Driving Forces for Industry 4.0 7 1.4 Artificial Intelligence—Manufacturing Sector 8 1.4.1 AI Diversity—Applications to Manufacturing Sector 9 1.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 12 1.5 Conclusion 13 References 14 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17 Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra 2.1 Introduction 17 2.2 Industrial Transformation/Value Chain Transformation 18 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 19 2.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 20 2.3 IIoT Reference Architecture 20 2.4 IIoT Technical Concepts 22 2.5 IIoT and Cloud Computing 26 2.6 IIoT and Security 27 References 29 3 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31 Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah 3.1 Introduction 32 3.2 Concepts 33 3.2.1 Internet of Things 33 3.2.2 The Industrial Internet of Things (IIoT) 34 3.2.3 Artificial Intelligence of Things (AIoT) 35 3.3 AIoT-Based Supply Chain 36 3.4 Conclusion 40 References 40 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43 Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah 4.1 Introduction 44 4.2 Literature Review 45 4.2.1 Summary of the First Three Industrial Revolutions 45 4.2.2 Emergence of Industry 4.0 45 4.2.3 Some of the Challenges of Industry 4.0 47 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 48 4.4 Proposed Approach 50 4.4.1 Mathematical Model 50 4.4.2 Advantages of the Proposed Model 51 4.5 Discussion and Conclusion 52 References 53 5 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57 Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer 5.1 Motivation and Background 58 5.2 Bringing Intelligence Into IoT Devices 60 5.3 The Foundation of CR-IoT Network 62 5.3.1 Various AI Technique in CR-IoT Network 63 5.3.2 Artificial Neural Network (ANN) 63 5.3.3 Metaheuristic Technique 64 5.3.4 Rule-Based System 64 5.3.5 Ontology-Based System 65 5.3.6 Probabilistic Models 65 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 65 5.5 Realization of CR-IoT Network in Daily Life Examples 69 5.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 70 5.7 Conclusion 75 References 75 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment 79 Mahendra Prasad Nath, Sushree Bibhuprada B. Priyadarshini, Debahuti Mishra and Brojo Kishore Mishra 6.1 Introduction 80 6.2 Overview of Blockchain 83 6.3 Components of Blockchain 85 6.3.1 Data Block 85 6.3.2 Smart Contracts 87 6.3.3 Consensus Algorithms 87 6.4 Safety Issues in Blockchain Technology 88 6.5 Usage of Big Data Framework in Dynamic Supply Chain System 91 6.6 Machine Learning and Big Data 94 6.6.1 Overview of Shallow Models 95 6.6.1.1 Support Vector Machine (SVM) 95 6.6.1.2 Artificial Neural Network (ANN) 95 6.6.1.3 K-Nearest Neighbor (KNN) 95 6.6.1.4 Clustering 96 6.6.1.5 Decision Tree 96 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 96 6.7.1 Replenishment Planning 96 6.7.2 Optimizing Orders 97 6.7.3 Arranging and Organizing 97 6.7.4 Enhanced Demand Structuring 97 6.7.5 Real-Time Management of the Supply Chain 97 6.7.6 Enhanced Reaction 98 6.7.7 Planning and Growth of Inventories 98 6.8 IoT-Enabled Blockchains 98 6.8.1 Securing IoT Applications by Utilizing Blockchain 99 6.8.2 Blockchain Based on Permission 101 6.8.3 Blockchain Improvements in IoT 101 6.8.3.1 Blockchain Can Store Information Coming from IoT Devices 101 6.8.3.2 Secure Data Storage with Blockchain Distribution 101 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners 102 6.8.3.4 Spoofing Attacks and Data Loss Prevention 102 6.8.3.5 Unauthorized Access Prevention Using Blockchain 103 6.8.3.6 Exclusion of Centralized Cloud Servers 103 6.9 Conclusions 103 References 104 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111 Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi 7.1 Introduction 112 7.2 Related Work 113 7.3 Methodology 114 7.3.1 Splitting of Data (Test/Train) 116 7.3.2 Prophet Model 116 7.3.3 Data Cleaning 119 7.3.4 Model Implementation 119 7.4 Results 120 7.4.1 Comparing Forecast to Actuals 121 7.4.2 Adding Holidays 122 7.4.3 Comparing Forecast to Actuals with the Cleaned Data 122 7.5 Conclusion and Future Scope 122 References 125 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127 Manas Kumar Yogi and A.S.N. Chakravarthy 8.1 Introduction 128 8.2 Related Work 131 8.3 Proposed Mechanism 133 8.4 Experimental Results 135 8.5 Future Directions 137 8.6 Conclusion 138 References 138 9 Environmental and Industrial Applications Using Internet of Things (IoT) 141 Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah 9.1 Introduction 142 9.2 IoT-Based Environmental Applications 146 9.3 Smart Environmental Monitoring 147 9.3.1 Air Quality Assessment 147 9.3.2 Water Quality Assessment 148 9.3.3 Soil Quality Assessment 150 9.3.4 Environmental Health-Related to COVID- 19 Monitoring 150 9.4 Applications of Sensors Network in Agro-Industrial System 151 9.5 Applications of IoT in Industry 153 9.5.1 Application of IoT in the Autonomous Field 153 9.5.2 Applications of IoT in Software Industries 155 9.5.3 Sensors in Industry 156 9.6 Challenges of IoT Applications in Environmental and Industrial Applications 157 9.7 Conclusions and Recommendations 159 Acknowledgments 159 References 159 10 An Introduction to Security in Internet of Things (IoT) and Big Data 169 Sushree Bibhuprada B. Priyadarshini, Suraj Kumar Dash, Amrit Sahani, Brojo Kishore Mishra and Mahendra Prasad Nath 10.1 Introduction 170 10.2 Allusion Design of IoT 172 10.2.1 Stage 1—Edge Tool 172 10.2.2 Stage 2—Connectivity 172 10.2.3 Stage 3—Fog Computing 173 10.2.4 Stage 4—Data Collection 173 10.2.5 Stage 5—Data Abstraction 173 10.2.6 Stage 6—Applications 173 10.2.7 Stage 7—Cooperation and Processes 174 10.3 Vulnerabilities of IoT 174 10.3.1 The Properties and Relationships of Various IoT Networks 174 10.3.2 Device Attacks 175 10.3.3 Attacks on Network 175 10.3.4 Some Other Issues 175 10.3.4.1 Customer Delivery Value 175 10.3.4.2 Compatibility Problems With Equipment 176 10.3.4.3 Compatibility and Maintenance 176 10.3.4.4 Connectivity Issues in the Field of Data 176 10.3.4.5 Incorrect Data Collection and Difficulties 177 10.3.4.6 Security Concern 177 10.3.4.7 Problems in Computer Confidentiality 177 10.4 Challenges in Technology 178 10.4.1 Skepticism of Consumers 178 10.5 Analysis of IoT Security 179 10.5.1 Sensing Layer Security Threats 180 10.5.1.1 Node Capturing 180 10.5.1.2 Malicious Attack by Code Injection 180 10.5.1.3 Attack by Fake Data Injection 180 10.5.1.4 Sidelines Assaults 181 10.5.1.5 Attacks During Booting Process 181 10.5.2 Network Layer Safety Issues 181 10.5.2.1 Attack on Phishing Page 181 10.5.2.2 Attacks on Access 182 10.5.2.3 Attacks on Data Transmission 182 10.5.2.4 Attacks on Routing 182 10.5.3 Middleware Layer Safety Issues 182 10.5.3.1 Attack by SQL Injection 183 10.5.3.2 Attack by Signature Wrapping 183 10.5.3.3 Cloud Attack Injection with Malware 183 10.5.3.4 Cloud Flooding Attack 183 10.5.4 Gateways Safety Issues 184 10.5.4.1 On-Boarding Safely 184 10.5.4.2 Additional Interfaces 184 10.5.4.3 Encrypting End-to-End 184 10.5.5 Application Layer Safety Issues 185 10.5.5.1 Theft of Data 185 10.5.5.2 Attacks at Interruption in Service 185 10.5.5.3 Malicious Code Injection Attack 185 10.6 Improvements and Enhancements Needed for IoT Applications in the Future 186 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 189 10.8 Conclusion 192 References 193 11 Potential, Scope, and Challenges of Industry 4.0 201 Roshan Raman and Aayush Kumar 11.1 Introduction 202 11.2 Key Aspects for a Successful Production 202 11.3 Opportunities with Industry 4.0 204 11.4 Issues in Implementation of Industry 4.0 206 11.5 Potential Tools Utilized in Industry 4.0 207 11.6 Conclusion 210 References 210 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215 Roshan Raman and Aditya Ranjan 12.1 Introduction 216 12.2 Changing Market Demands 217 12.2.1 Individualization 218 12.2.2 Volatility 218 12.2.3 Efficiency in Terms of Energy Resources 218 12.3 Recent Technological Advancements 219 12.4 Industrial Revolution 4.0 221 12.5 Challenges to Industry 4.0 224 12.6 Conclusion 225 References 226 13 The Role of Multiagent System in Industry 4.0 227 Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha 13.1 Introduction 228 13.2 Characteristics and Goals of Industry 4.0 Conception 228 13.3 Artificial Intelligence 231 13.3.1 Knowledge-Based Systems 232 13.4 Multiagent Systems 234 13.4.1 Agent Architectures 234 13.4.2 Jade 238 13.4.3 System Requirements Definition 239 13.4.4 HMI Development 240 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 240 13.5.1 Agent Supervision 240 13.5.2 Documents Dispatching Agents 241 13.5.3 Agent Rescheduling 242 13.5.4 Agent of Executive 242 13.5.5 Primary Roles of High-Availability Agent 243 13.6 Conclusion 244 References 244 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security 247 Akeem Femi Kadri, Micheal Olaolu Arowolo, Ayisat Wuraola Yusuf-Asaju, Kafayat Odunayo Tajudeen and Kazeem Alagbe Gbolagade 14.1 Introduction 248 14.2 Reviews of Related Works 250 14.3 Materials and Methods 258 14.3.1 Multimedia 258 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence 261 14.3.3 Cryptography 262 14.3.4 Encryption and Decryption 265 14.3.5 Residue Number System 266 14.4 Discussion and Conclusion 268 References 268 15 Market Trends with Cryptocurrency Trading in Industry 4.0 275 Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma 15.1 Introduction 276 15.2 Industry Overview 276 15.2.1 History (From Barter to Cryptocurrency) 276 15.2.2 In the Beginning Was Bitcoin 278 15.3 Cryptocurrency Market 279 15.3.1 Blockchain 279 15.3.1.1 Introduction to Blockchain Technology 279 15.3.1.2 Mining 280 15.3.1.3 From Blockchain to Cryptocurrency 281 15.3.2 Introduction to Cryptocurrency Market 281 15.3.2.1 What is a Cryptocurrency? 281 15.3.2.2 Cryptocurrency Exchanges 283 15.4 Cryptocurrency Trading 283 15.4.1 Definition 283 15.4.2 Advantages 283 15.4.3 Disadvantages 284 15.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 285 15.5.1 Need for a Fee-Driven System 285 15.5.2 Ethereum Structure 286 15.5.3 How is the Gas Fee Calculated? 287 15.5.3.1 Why are Ethereum Gas Prices so High? 287 15.5.3.2 Carbon Neutrality 287 15.6 Conclusion 291 References 292 16 Blockchain and Its Applications in Industry 4.0 295 Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale 16.1 Introduction 296 16.2 About Cryptocurrency 296 16.3 History of Blockchain and Cryptocurrency 298 16.4 Background of Industrial Revolution 300 16.4.1 The First Industrial Revolution 301 16.4.2 The Second Industrial Revolution 301 16.4.3 The Third Industrial Revolution 302 16.4.4 The Fourth Industrial Revolution 302 16.5 Trends of Blockchain 303 16.6 Applications of Blockchain in Industry 4.0 304 16.6.1 Blockchain and the Government 304 16.6.2 Blockchain in the Healthcare Sector 304 16.6.3 Blockchain in Logistics and Supply Chain 306 16.6.4 Blockchain in the Automotive Sector 307 16.6.5 Blockchain in the Education Sector 308 16.7 Conclusion 309 References 310 Index 315
£153.00
St. Martin's Publishing Group The Intelligence Explosion
Book Synopsis
£22.50
John Wiley & Sons Data Science First Using Language Models in AIEn abled Applications
a huge range and FREE tracked UK delivery on ALL orders.
£45.12
APress MATLAB Deep Learning
Book SynopsisGet started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you''ll be able to tackle some of today''s real world big data, smart bots, and other complex data problems. You''ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You''ll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers WhoTable of Contents1. Machine Learning2. Neural Network3. Training of Multi-Layer Neural Network4. Neural Network and Classification5. Deep Learning6. Convolutional Neural Network
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APress Deep Learning with Swift for TensorFlow
Book SynopsisAbout this bookDiscover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both with support for modern hardware accelerators and more. This book covers the deep learning concepts from fundamentals to advanced research. It also introduces the Swift language for beginners in programming. This book is well suited for newcomers and experts in programming and deep learning alike. After reading this book you should be able to program various state-of-the-art deep learning algorithms yourself. The book covers foundational concepts of machine learning. It also introduces the mathematics required to understand deep learning. Swift language is introduced such that it allows beginners and researchers to understand programming and easily transit to Swift for TensorTable of ContentsChapter 1: Machine Learning Basics Chapter 2: Essential Math Chapter 3: Differential Programming Chapter 4: TensorFlow BasicsChapter 5: Neural NetworksChapter 6: Computer Vision
£49.49
John Wiley and Sons Ltd An Introduction to Communication and Artificial
Book SynopsisCommunication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.Trade Review“Gunkel’s book is an accessible but technically savvy monograph introducing students and scholars of communication and computer science to the intersections between AI and communication. … Gunkel’s book will also be a particularly useful resource to instructors, not only due to its accessible language and wide- reaching scope, but also thanks to the five ‘Maker exercises’ included in the last section. These provide useful entry points for students that are not versed in computer programming for experimenting with simple computer programs.”Communication Theory “An introduction to communication and artificial intelligence aims and succeeds in making sense of AI for students and scholars in social sciences.”CommunicationsTable of ContentsPreface Part I: Introduction and Orientation 1 Introduction 2 Communication and AI 3 Basic Concepts and Terminology Part II: Applications 4 Machine Translation 5 Natural Language Processing 6 Computational Creativity 7 Social Robots Part III: Impact and Consequences 8 Social Issues 9 Social Responsibility and Ethics Part IV: Maker Exercises Introduction Exercise 1 – Demystifying ELIZA Exercise 2 – Algorithms Exercise 3 – Machine Translation Exercise 4 – Chatbot and Quasi-Loebner Prize Exercise 5 – Template NLG Notes References Index
£52.25
IGI Global AI and Big Data's Potential for Disruptive
Book SynopsisBig data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly.AI and Big Data's Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions.
£186.00
Austin Macauley Publishers Big Brain Revolution: Artificial Intelligence -
Book Synopsis
£999.99
Hodder & Stoughton The 22 Murders Of Madison May: A gripping
Book SynopsisFrom the critically acclaimed author of Jennifer Government and Lexicon comes mind-bending speculative psychological suspense about a serial killer pursuing his victim across time and space, and the woman who is determined to stop him, even if it upends her own reality. "I love you. In every world." Young real estate agent Madison May is shocked when a client at an open house says these words to her. The man, a stranger, seems to know far too much about her, and professes his love - shortly before he murders her. Felicity Staples hates reporting on murders. As a journalist for a mid-size New York City paper, she knows she must take on the assignment to research Madison May's shocking murder, but the crime seems random and the suspect is in the wind. That is, until Felicity spots the killer on the subway, right before he vanishes. Soon, Felicity senses her entire universe has shifted. No one remembers Madison May, or Felicity's encounter with the mysterious man. And her cat is missing. Felicity realizes that in her pursuit of Madison's killer, she followed him into a different dimension - one where everything about her existence is slightly altered. At first, she is determined to return to the reality she knows, but when Madison May - in this world, a struggling actress - is murdered again, Felicity decides she must find the killer - and learns that she is not the only one hunting him. Traveling through different realities, Felicity uncovers the opportunity - and danger - of living more than one life.Trade ReviewWith unrelenting tension, Max Barry weaves a complex tapestry where a sociopath's insatiable obsession knows no bounds, not even time and space, and only two things are certain - he will kill her again because he's killed her before. I devoured this novel in one sitting * J.D. Barker, author of A Caller's Game *Original, intelligent, unputdownable * The Guardian *Gripping . . . this is the sort of tricksiness Barry was put on Earth for * The Times *A very enjoyable thriller with a very satisfying ending * Mystery and Suspense Magazine *There are the familiar dislocations and dilemmas of a quest that flings its characters across separate but related realities, but these are seasoned with the wit, menace and pacy excitement of a nicely controlled, neatly structured crime thriller * ParSec Magazine *Barry strives to paint equally compelling portraits of the two women and comes pretty darn close. Each character assumes a fully rounded and weighty resonance . . . Dynamic, suspenseful action with a light frosting of metaphysics * Washington Post *Mr. Barry avoids the obvious sci-fi option of thinking up lots of exciting worlds, and takes the grittier route of imagining intrusions into the underbelly of this one . . . Unnerving. He navigates the multiverse and the concepts of string theory and chaos theory with frightening conviction. Let's hope he just made it all up * Wall Street Journal *Sci-fi wizard Barry sets a serial killer loose in the multiverse with mind-bending results * People *Beware Max Barry. Once his story grabs hold, you will forget to eat, sleep, and bathe until you're left with the world's worst book hangover. An exhilarating rocket shot of a thriller tempered with Barry's trademark wit and warmth * David Yoon, author of Version Zero and Frankly in Love *
£15.29
Hodder & Stoughton The 22 Murders Of Madison May: A gripping
Book SynopsisFrom the critically acclaimed author of Jennifer Government and Lexicon comes mind-bending speculative psychological suspense about a serial killer pursuing his victim across time and space, and the woman who is determined to stop him, even if it upends her own reality."I love you. In every world."Young real estate agent Madison May is shocked when a client at an open house says these words to her. The man, a stranger, seems to know far too much about her, and professes his love - shortly before he murders her.Felicity Staples hates reporting on murders. As a journalist for a mid-size New York City paper, she knows she must take on the assignment to research Madison May's shocking murder, but the crime seems random and the suspect is in the wind. That is, until Felicity spots the killer on the subway, right before he vanishes.Soon, Felicity senses her entire universe has shifted. No one remembers Madison May, or Felicity's encounter with the mysterious man. And her cat is missing. Felicity realizes that in her pursuit of Madison's killer, she followed him into a different dimension - one where everything about her existence is slightly altered. At first, she is determined to return to the reality she knows, but when Madison May - in this world, a struggling actress - is murdered again, Felicity decides she must find the killer - and learns that she is not the only one hunting him.Traveling through different realities, Felicity uncovers the opportunity - and danger - of living more than one life.Trade ReviewWith unrelenting tension, Max Barry weaves a complex tapestry where a sociopath's insatiable obsession knows no bounds, not even time and space, and only two things are certain - he will kill her again because he's killed her before. I devoured this novel in one sitting * J.D. Barker, author of A Caller's Game *Original, intelligent, unputdownable * The Guardian *Gripping . . . this is the sort of tricksiness Barry was put on Earth for * The Times *A very enjoyable thriller with a very satisfying ending * Mystery and Suspense Magazine *There are the familiar dislocations and dilemmas of a quest that flings its characters across separate but related realities, but these are seasoned with the wit, menace and pacy excitement of a nicely controlled, neatly structured crime thriller * ParSec Magazine *Barry strives to paint equally compelling portraits of the two women and comes pretty darn close. Each character assumes a fully rounded and weighty resonance . . . Dynamic, suspenseful action with a light frosting of metaphysics * Washington Post *Mr. Barry avoids the obvious sci-fi option of thinking up lots of exciting worlds, and takes the grittier route of imagining intrusions into the underbelly of this one . . . Unnerving. He navigates the multiverse and the concepts of string theory and chaos theory with frightening conviction. Let's hope he just made it all up * Wall Street Journal *Sci-fi wizard Barry sets a serial killer loose in the multiverse with mind-bending results * People *Beware Max Barry. Once his story grabs hold, you will forget to eat, sleep, and bathe until you're left with the world's worst book hangover. An exhilarating rocket shot of a thriller tempered with Barry's trademark wit and warmth * David Yoon, author of Version Zero and Frankly in Love *
£9.49
IGI Global Architectural Design of Multi-agent Systems:
Book SynopsisCompiles advanced research results focusing on architecture and modeling issues of multi-agent systems. This book serves as a reference for further research on system models, architectural design languages, formal methods and reasoning.
£123.00
IGI Global Computational Intelligence in Archaeology
Book SynopsisThe vast quantity of archaeological data coming from excavations is now well beyond the traditional data processing tools. Computational archaeology creates an exhaustive analysis of technical and analytical needs in the archaeological sciences.Computational Intelligence in Archaeology provides analytical theories offered by new and innovative artificial intelligence computing methods in the archaeological domain. This stimulating, must-have title is full of archaeological examples that allow academicians, researchers, and students to understand a complex but very useful data analysis technique to the field of archaeology.
£161.10
IOS Press Towards a KnowledgeAware AI
Book SynopsisSemantic systems lie at the heart of modern computing, interlinking with areas as diverse as AI, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, enterprise vocabulary management, machine learning, logic programming, content engineering, social computing, and the Semantic Web. This book presents the proceedings of SEMANTiCS 2022, the 18th International Conference on Semantic Systems, held as a hybrid event live in Vienna, Austria and online from 12 to 15 September 2022. The SEMANTiCS conference is an annual meeting place for the professionals and researchers who make semantic computing work, who understand its benefits and encounter its limitations, and is attended by information managers, IT architects, software engineers, and researchers from organizations ranging from research facilities and NPOs, through public administrations to the largest companies in the world. The theme and subtitle of the 2022 conference was Towards A Knowledge-Aware AI, and the book contains 15 papers, selected on the basis of quality, impact and scientific merit following a rigorous review process which resulted in an acceptance rate of 29%. The book is divided into four chapters: semantics in data quality, standards and protection; representation learning and reasoning for downstream AI tasks; ontology development; and learning over complementary knowledge. Providing an overview of emerging trends and topics in the wide area of semantic computing, the book will be of interest to anyone involved in the development and deployment of computer technology and AI systems.
£83.30
IOS Press Digitalization and Management Innovation
Book SynopsisThe digital era has brought about important changes that continue to affect all our lives. Efficient management and storage of digital information has become crucial, as has the ability to access that information quickly and efficiently, and priorities are to allow for the saving of digital data in many different ways, and to avoid the loss of information in the event of a malfunction. This book presents the 65 papers presented at DMI2022, the first in the new annual conference series Digitalization and Management Innovation (DMI), held as a hybrid event in Beijing, China, on 26 November 2022. A total of 190 submissions were received for the conference, and the papers presented here were selected after careful and conscientious review, bearing in mind the breadth and depth of the research topics falling within the scope of digital and management innovation and resulting in an acceptance rate of 34%. Topics covered include digital transformation, supply chains, business models, and block chain, enterprises, banking, and sustainability, as well as policy in artificial intelligence, the gig economy, the post-epidemic era, green supply, citizenship behavior, human resource management, human relationships, agriculture, and environmental matters. Presenting original ideas and results of general significance and supported by clear reasoning, and compelling evidence and methods, the book will be of interest to all those whose work involves the management of digital data.
£146.20
Bravex Publications Machine Learning: An Essential Guide to Machine
Book Synopsis
£999.99
IGI Global Machine Learning and AI Techniques in Interactive
Book SynopsisThe healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.
£319.60
IGI Global Meta-Learning Frameworks for Imaging Applications
Book Synopsis
£241.20
IGI Global Meta-Learning Frameworks for Imaging Applications
Book Synopsis
£182.70
IGI Global Perspectives on Artificial Intelligence in Times
Book SynopsisPerspectives on Artificial Intelligence in Times of Turbulence: Theoretical Background to Applications offers a comprehensive exploration of the intricate relationship between artificial intelligence (AI) and the ever-changing landscape of our society. The book defines AI as machines capable of performing tasks that were once exclusive to human cognition. However, it emphasizes the current limitations of AI, dispelling the notion of sophisticated cyborgs depicted in popular culture. These machines lack self-awareness, struggle with understanding context—especially in language—and are constrained by historical data and predefined parameters. This distinction sets the stage for examining AI's impact on the job market and the evolving roles of humans and machines. Rather than portraying AI as a threat, this book highlights the symbiotic relationship between humans and machines. It recognizes that while certain jobs may become obsolete, new opportunities will emerge. The unique abilities of human beings—such as relational skills, emotional intelligence, adaptability, and understanding of differences—will continue to be indispensable in a rapidly transforming society. The book further explores key objectives and strategies for organizations navigating the AI-driven landscape. From maintaining focus on strategic goals to adapting to new productivity paradigms, from fostering effective communication to promoting feedback and continuous improvement, the chapters provide practical insights and methodologies for managing change and harnessing AI's potential. Its perspectives cover a wide range of topics such as business sustainability, change management, cybersecurity, digital economy and transformation, information systems management, management models and tools, and continuous improvement are comprehensively addressed. Additionally, the book delves into healthcare, telemedicine, Health 4.0, privacy and security, knowledge management, learning, and presents real-world case studies. Designed for researchers and professionals seeking to enhance their knowledge and research capabilities, this book offers a consistent theoretical and practical foundation. It serves as a springboard for further studies, supports change management initiatives within organizations, and facilitates knowledge sharing among experts. This book is an essential companion for colleges with master's and Ph.D. degree investigators, and researchers across a wide range of disciplines.
£267.30
O'Reilly Media mBots for Makers
Book SynopsisThe mBot robotics platform is a hugely popular kit because of the quality of components and price. With hundreds of thousands of these kits out there in homes, schools and makerspaces, there is much untapped potential. Getting Started with mBots is for non-technical parents, kids and teachers who want to start with a robust robotics platform and then take it to the next level. The heart of the mBot, the mCore is a powerful Arduino based microcontroller that can do many things without soldering or breadboarding.
£999.99
Nova Science Publishers Inc Understanding Pattern Analysis
Book Synopsis
£999.99
Apple Academic Press Inc. Computational Intelligence Applications for
Book SynopsisThis new volume explores the computational intelligence techniques necessary to carry out different software engineering tasks. Software undergoes various stages before deployment, such as requirements elicitation, software designing, software project planning, software coding, and software testing and maintenance. Every stage is bundled with a number of tasks or activities to be performed. Due to the large and complex nature of software, these tasks can become costly and error prone. This volume aims to help meet these challenges by presenting new research and practical applications in intelligent techniques in the field of software engineering. Computational Intelligence Applications for Software Engineering Problems discusses techniques and presents case studies to solve engineering challenges using machine learning, deep learning, fuzzy-logic-based computation, statistical modeling, invasive weed meta-heuristic algorithms, artificial intelligence, the DevOps model, time series forecasting models, and more.Table of Contents1. A Statistical Experimentation Approach for Software Quality Management and Defect Evaluations 2. Open Challenges in Software Measurements Using Machine Learning Techniques 3. Empirical Software Engineering and Its Challenges 4. Uncertain Multiobjective COTS Product Selection Problems for Modular Software System and Their Solutions by Genetic Algorithm 5. Fuzzy Logic Based Computational Technique for Analyzing Software Bug Repository 6. Software Measurements from Machine Learning to Deep Learning 7. Time Series Forecasting Using ARIMA Models: Systematic Literature Review of 2000s 8. Industry Maintenance Optimization Using AI 9. Comparative Study of Invasive Weed Optimization Algorithms 10. An Overview of Computational Tools 11. Enhanced Intelligence Architecture 12. Systematic Literature Review of Search-Based Software Engineering Techniques for Code Modularization/Remodularization 13. Automation of Framework Using DevOps Model to Deliver DDE Software
£124.45
Apple Academic Press Inc. Attacks on Artificial Intelligence
a huge range and FREE tracked UK delivery on ALL orders.
£142.50
Steve Christian Artificial Intelligence: The Most Updated and
Book Synopsis
£17.06
Rethink Press SeniorITy: How AI and tech can enhance senior
Book SynopsisDo you feel frustrated and left behind as every aspect of daily life from banking and shopping to health and communication becomes increasingly dependent on technology, the internet, and artificial intelligence (AI)?SeniorITy empowers us as we age, and those hesitant to engage with new technologies, by exploring the positives of becoming knowledgable and fully connected online. Accepting technological advances can help you live a long, healthy, and more rewarding life. Learn how to: Understand why you find it difficult to engage with the digital world Overcome frustration with the technology necessary for everyday life Make decisions about the best tech options for you Protect yourself and your data online Embrace digital advances that can increase independence and improve quality of life
£12.59
World Scientific Europe Ltd What Is Artificial Intelligence?: A Conversation
Book Synopsis'A light-hearted, but engaging conversation about one of the key technologies of our age.I recommend this book to anyone interested in the broader issues around Artificial Intelligence.'Richard HartleyAustralian National University, Australia This book engages with the title question: what is artificial intelligence (AI)? Instead of reiterating received definitions or surveying the field from a disciplinary perspective, the question is engaged here by putting two standpoints into conversation. The standpoints are different in their disciplinary groundings — i.e. technology and the humanities — and also in their approaches — i.e. applied and conceptual. Peter is an AI engineer: his approach is in terms of how to make AI work. Suman is a humanities researcher: his approach is in terms of what people and academics mean when they say 'AI'.A coherent argument, if not a consensus, develops by putting the two standpoints into conversation. The conversation is presented in 32 short chapters, in turn by Suman and Peter. There are two parts: Part 1, Questioning AI, and Part 2, AI and Government Policy. The first part covers issues such as the meaning of intelligence, automation, evolution, artificial and language. It outlines some of the processes through which these concepts may be technologically grounded as AI. The second part addresses policy considerations that underpin the development of AI and responds to the consequences. Themes taken up here include: rights and responsibilities; data usage and state-level strategies in the USA, UK and China; unemployment and policy futures.
£81.00
IGI Global Challenges and Applications for Implementing
Book SynopsisMachine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see.Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
£159.75
Business Science Reference IoT Architectures, Models, and Platforms for
Book SynopsisDeveloping countries are persistently looking for efficient and cost-effective methods for transforming their communities into smart cities. Unfortunately, energy crises have increased in these regions due to a lack of awareness and proper utilization of technological methods. These communities must explore and implement innovative solutions in order to enhance citizen enrollment, quality of government, and city intelligence.IoT Architectures, Models, and Platforms for Smart City Applications provides emerging research exploring the theoretical and practical aspects of transforming cities into intelligent systems using IoT-based design models and sustainable development projects. This publication looks at how cities can be built as smart cities within limited resources and existing advanced technologies. Featuring coverage on a broad range of topics such as cloud computing, human machine interface, and ad hoc networks, this book is ideally designed for urban planners, engineers, IT specialists, computer engineering students, research scientists, academicians, technology developers, policymakers, researchers, and designers seeking current research on smart applications within urban development.
£999.99
IGI Global Advanced Research and Real-World Applications of
Book SynopsisIndustry 5.0 is a growing field that has many potential future directions and opportunities for businesses and companies. To ensure society is prepared for this evolving technological world, further study is required on the potential challenges and pitfalls. Advanced Research and Real-World Applications of Industry 5.0 presents an overview of Industry 5.0 and related advanced research and real-world applications. The book also discusses several real-time issues, problems, and applications with corresponding solutions and suggestions. Covering critical topics such as optimization models, cybersecurity threats, and sustainability, this reference work is ideal for business owners, computer scientists, industry professionals, managers, researchers, scholars, academicians, practitioners, instructors, and students.
£178.60
Springer London Ltd Robotics: Modelling, Planning and Control
Book SynopsisThe classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB® code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.Trade ReviewRobotics: Modelling, Planning and Control is a book that comprehensively covers all aspects of robotic fundamentals. It is particularly an excellent text for graduate educators, as it covers the fundamentals of the field with a rigorous formalism that is well blended with the technological aspects of robotics. The text covers in detail the theory of manipulators and wheeled robots starting with kinematics, dynamics and motion control, as well interaction with the environment through perception - force and vision sensors. The book is written by technical authorities in the field, and will be in invaluable addition to graduate education as well as a useful guide for industrial practitioners. Alexander Zelinsky, CSIRO, Australia Robotics is a diverse field bringing together disparate areas from computer science, electrical engineering and mechanical engineering. This book is an integrative but rigorous treatment of all the relevant concepts, with an eye toward modern, practical applications making it an excellent choice for a first year graduate course in robotics. Vijay Kumar, University of Pennsylvania This book provides rock-solid foundations for the study of classical mechanics and control of robots, with the authoritative character of a reference where you can surely find the correct expression and the rigorous derivation of the results you need. On top of this, new chapters on motion planning, visual servoing, and mobile robot control provide support to teaching wider and more interdisciplinary aspects of robotics, and open up vistas that will certainly inspire a new generation of scholars to embrace this incredibly rich and fertile research field. Antonio Bicchi, University of Pisa, Italy This book offers a well-balanced and intellectually satisfying treatment of robot mechanics, planning, and control – from the choice and sequence of topics, to the level of detail in the analysis, and the clear connections made between the latest technologies and the theoretical foundations of robotics, this book is an essential element in the library of every aspiring young robotics researcher. Frank Chongwoo Park, Seoul National University Robotics: Modeling, Planning and Control is a historiography from the materialistic view of robotics. Authors clearly explain physical and mathematical foundation to understand the most up-to-date robotics, so faithfully to bibliography and terminology in robotics. Unquestionably, the best textbook for senior students and graduate students and the closest reference book for engineers and scientists! Yoshihiko Nakamura, University of Tokyo Exceptional! A text with such a span of robotics fundamentals and advanced research in both manipulation and mobility, and a treatment that creatively balances mathematical depth and physical intuition – a fresh and certainly unique reference for researchers and engineers in the field of robotics. Oussama Khatib, Stanford University Certainly because of its youth, robotics is not always considered as a discipline as such. It is often introduced as a technological "area" integrating various aspects of mechanics, automatic control and computer science. Such a dispersed view is prejudicial for students. The book by Siciliano et al. achieves the introduction of the basic concepts in a coherent, self-contained and didactic way. In that sense, when reading Robotics: Modelling, Planning and Control the reader – from the undergraduate student to the researcher – understands that a new discipline is born, with its own foundations. Jean-Paul Laumond, LAAS-CNRSTable of ContentsKinematics.- Differential Kinematics and Statics.- Trajectory Planning.- Actuators and Sensors.- Control Architecture.- Dynamics.- Motion Control.- Force Control.- Visual Servoing.- Mobile Robots.- Motion Planning.
£85.49
Cornerstone Talk to Me: Amazon, Google, Apple and the Race
Book SynopsisThe gripping inside story of the race to build conversationally capable computersChat with the author: ask your Alexa device to ‘open the voice computing book’__________________The next great technological disruption is coming.The titans of Silicon Valley are racing to build the last, best computer that the world will ever need. Whoever successfully creates it will revolutionise our relationship with technology – and make billions of dollars in the process. It is known as conversational AI.For years, computers that can speak and think like humans have been on the verge of becoming a reality. Now, James Vlahos introduces the researchers at Google, Amazon and Apple who are leading the way to a voice tech revolution. And he reveals how their discoveries will transform every sector of society – from revolutionising how we use the internet, to transforming our understanding of consciousness.Vlahos’s research leads him to one fundamental question: What happens when our computers become as articulate, compassionate, and creative as we are?__________________‘Brilliant and essential . . . You’ll find insights and meaning on every page, and you’ll keep turning them. This book is dynamite.’ NICHOLAS THOMPSON, editor-in-chief of Wired‘Conversational AI is a genuine paradigm shift in our experience with technology. Vlahos brings the whole story to life . . . A thoughtful and enjoyable read.’ TOM GRUBER, co-creator of Siri‘The baton of disruption has been passed from the smart phone to voice, and Vlahos helps make sense of this tectonic shift.’ SCOTT GALLOWAY, author of The Four‘Voice computing is revolutionising the way we interact with our devices. Talk to Me offers a road map showing how we got to this point and the opportunities and risks that lie ahead.’ MARTIN FORD, author of The Rise of the Robots‘James Vlahos has written an excellent book on how voice computing has become more and more of a growing presence in our everyday world.’ RAY KURZWEIL, author of The Singularity Is NearTrade ReviewBrilliant and essential . . . You’ll find insights and meaning on every page, and you’ll keep turning them. This book is dynamite. -- NICHOLAS THOMPSON, editor-in-chief of WiredConversational AI is a genuine paradigm shift in our experience with technology. Vlahos brings the whole story to life . . . A thoughtful and enjoyable read. -- TOM GRUBER, co-creator of SiriThe baton of disruption has been passed from the smart phone to voice, and Vlahos helps make sense of this tectonic shift. -- SCOTT GALLOWAY, author of The FourVoice computing is revolutionising the way we interact with our devices. Talk to Me offers a road map showing how we got to this point and the opportunities and risks that lie ahead. -- MARTIN FORD, author of The Rise of the RobotsJames Vlahos has written an excellent book on how voice computing has become more and more of a growing presence in our everyday world. -- RAY KURZWEIL, author of The Singularity Is Near
£13.49
Cambridge Media Group Artificial Intelligence: Issues Series - PSHE &
Book Synopsis
£11.09
Bob Mather Artificial Intelligence Bundle: 3 Books in 1
Book Synopsis
£75.99
Springer Nature Switzerland AG Recent Advances in Ensembles for Feature Selection
a huge range and FREE tracked UK delivery on ALL orders.
£80.99
Springer Nature Switzerland AG Automatic Syntactic Analysis Based on Selectional Preferences
a huge range and FREE tracked UK delivery on ALL orders.
£80.99
Springer Nature Switzerland AG Artificial Adaptive Systems Using Auto Contractive Maps: Theory, Applications and Extensions
Book SynopsisThis book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book’s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the “spin-net,” as a dynamic form of auto-associative memory.Table of ContentsAn Introduction.- Artificial Neural Networks.- Auto-Contractive Maps.- Visualization of Auto-CM Output.- Dataset Transformations and Auto-CM.- Comparison of Auto-CM to Various Other Data Understanding Approaches.
£80.99
Springer Nature Switzerland AG Lifetime Analysis by Aging Intensity Functions
a huge range and FREE tracked UK delivery on ALL orders.
£80.99
Springer Nature Switzerland AG Ubiquitous Computing and the Internet of Things: Prerequisites for the Development of ICT
a huge range and FREE tracked UK delivery on ALL orders.
£161.99
Springer Nature Switzerland AG Growth Poles of the Global Economy: Emergence, Changes and Future Perspectives
Book SynopsisThe book presents the best contributions from the international scientific conference “Growth Poles of the Global Economy: Emergence, Changes and Future,” which was organized by the Institute of Scientific Communications (Volgograd, Russia) together with the universities of Kyrgyzstan and various other cities in Russia. The 143 papers selected, focus on spatial and sectorial structures of the modern global economy according to the theory of growth poles. It is intended for representatives of the academic community: university and college staff developing study guides on socio-humanitarian disciplines in connection with the theory of growth poles, researchers, and undergraduates, masters, and postgraduates who are interested in the recent inventions and developments in the field. It is also a valuable resource for expert practitioners managing entrepreneurial structures in the existing and prospective growth poles of the global economy as well as those at international institutes that regulate growth poles.The first part of the book investigates the factors and conditions affecting the emergence of the growth poles of the modern global economy. The second part then discusses transformation processes in the traditional growth poles of the global economy under the influence of the technological progress. The third part examines how social factors affect the formation of new growth poles of the modern global economy. Lastly, the fourth part offers perspectives on the future growth of the global economy on the basis of the digital economy and Industry 4.0.Table of ContentsImprovement of the Structural Solution of Monolithic Reinforced Concrete Floors with the Use of Steel Profiled Flooring.- Increase in the Level of Structural Safety of Multistory Buildings and Structures.- Strategic Control as a Tool of Effective Management of Region’s Economy.- Modern Ways of Application of Innovative Teaching Methods for the Development of Creative Activity in the Teaching Process.
£999.99
Springer Nature Switzerland AG Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II
Book SynopsisThe three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.
£62.99
Springer Nature Switzerland AG Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover a wide range of topics and application areas, including: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.Table of ContentsDeep Learning Concepts for Evolutionary Art.- Adversarial Evolution and Deep Learning – How Does An Artist Play with Our Visual System.- Autonomy, Authenticity, Authorship and Intention in Computer Generated Art.- Camera Obscurer: Generative Art for Design Inspiration.- Swarm-Based Identification of Animation Key Points from 2D-medialness Maps.- Paintings, Polygons and Plant Propagation.- Evolutionary Games for Audiovisual Works: Exploring the Demographic Prisoner's Dilemma.- Emojinating: Evolving Emoji Blends.- Automatically Generating Engaging Presentation Slide Decks.- Tired of choosing? Just Add Structure and Virtual Reality.- EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts.- Comparing Models for Harmony Prediction in An Interactive Audio Looper.- Stochastic Synthesizer Patch Exploration in Edisyn.- Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection.- Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm.- Exploring Transfer Functions in Evolved CTRNNs for Music Generation.
£44.99
