{"product_id":"the-new-advanced-society-9781119824473","title":"The New Advanced Society","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eTHE NEW ADVANCED SOCIETY\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eIncluded in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial Internet of Things, featuring their working principles and application in different sectors.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eA 360-degree view of the different dimensions of the digital revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead, and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart trans\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Post Pandemic: The New Advanced Society 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSujata Priyambada Dash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1 Themes 2\u003c\/p\u003e \u003cp\u003e1.1.1.1 Theme: Areas of Management 2\u003c\/p\u003e \u003cp\u003e1.1.1.2 Theme: Financial Institutions Cyber Crime 3\u003c\/p\u003e \u003cp\u003e1.1.1.3 Theme: Economic Notion 4\u003c\/p\u003e \u003cp\u003e1.1.1.4 Theme: Human Depression 6\u003c\/p\u003e \u003cp\u003e1.1.1.5 Theme: Migrant Labor 7\u003c\/p\u003e \u003cp\u003e1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions 9\u003c\/p\u003e \u003cp\u003e1.1.1.7 School and Colleges Closures 11\u003c\/p\u003e \u003cp\u003e1.2 Conclusions 12\u003c\/p\u003e \u003cp\u003eReferences 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Distributed Ledger Technology in the Construction Industry Using Corda 15\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSandeep Kumar Panda, Shanmukhi Priya Daliyet, Shagun S. Lokre and Vihas Naman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 16\u003c\/p\u003e \u003cp\u003e2.2 Prerequisites 16\u003c\/p\u003e \u003cp\u003e2.2.1 DLT vs Blockchain 17\u003c\/p\u003e \u003cp\u003e2.3 Key Points of Corda 18\u003c\/p\u003e \u003cp\u003e2.3.1 Some Salient Features of Corda 20\u003c\/p\u003e \u003cp\u003e2.3.2 States 20\u003c\/p\u003e \u003cp\u003e2.3.3 Contract 22\u003c\/p\u003e \u003cp\u003e2.3.3.1 Create and Assign Task (CAT) Contract 22\u003c\/p\u003e \u003cp\u003e2.3.3.2 Request for Cash (RT) Contract 23\u003c\/p\u003e \u003cp\u003e2.3.3.3 Transfer of Cash (TT) Contract 24\u003c\/p\u003e \u003cp\u003e2.3.3.4 Updation of the Task (UOT) Contract 24\u003c\/p\u003e \u003cp\u003e2.3.4 Flows 25\u003c\/p\u003e \u003cp\u003e2.3.4.1 Flow Associated With CAT Contract 25\u003c\/p\u003e \u003cp\u003e2.3.4.2 Flow Associated With RT Contract 26\u003c\/p\u003e \u003cp\u003e2.3.4.3 Flow Associated With TT Contract 26\u003c\/p\u003e \u003cp\u003e2.3.4.4 Flow Associated With UOT Contract 26\u003c\/p\u003e \u003cp\u003e2.4 Implementation 26\u003c\/p\u003e \u003cp\u003e2.4.1 System Overview 27\u003c\/p\u003e \u003cp\u003e2.4.2 Working Flowchart 28\u003c\/p\u003e \u003cp\u003e2.4.3 Experimental Demonstration 29\u003c\/p\u003e \u003cp\u003e2.5 Future Work 35\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 36\u003c\/p\u003e \u003cp\u003eReferences 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Identity and Access Management for Internet of Things Cloud 43\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoumya Prakash Otta and Subhrakanta Panda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 44\u003c\/p\u003e \u003cp\u003e3.2 Internet of Things (IoT) Security 45\u003c\/p\u003e \u003cp\u003e3.2.1 IoT Security Overview 45\u003c\/p\u003e \u003cp\u003e3.2.2 IoT Security Requirements 46\u003c\/p\u003e \u003cp\u003e3.2.3 Securing the IoT Infrastructure 49\u003c\/p\u003e \u003cp\u003e3.3 IoT Cloud 49\u003c\/p\u003e \u003cp\u003e3.3.1 Cloudification of IoT 50\u003c\/p\u003e \u003cp\u003e3.3.2 Commercial IoT Clouds 52\u003c\/p\u003e \u003cp\u003e3.3.3 IAM of IoT Clouds 54\u003c\/p\u003e \u003cp\u003e3.4 IoT Cloud Related Developments 55\u003c\/p\u003e \u003cp\u003e3.5 Proposed Method for IoT Cloud IAM 58\u003c\/p\u003e \u003cp\u003e3.5.1 Distributed Ledger Approach for IoT Security 59\u003c\/p\u003e \u003cp\u003e3.5.2 Blockchain for IoT Security Solution 60\u003c\/p\u003e \u003cp\u003e3.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM 62\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 64\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Automated TSR Using DNN Approach for Intelligent Vehicles 67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBanhi Sanyal, Piyush R. Biswal, R.K. Mohapatra, Ratnakar Dash and Ankush Agarwalla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 68\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 69\u003c\/p\u003e \u003cp\u003e4.3 Neural Network (NN) 70\u003c\/p\u003e \u003cp\u003e4.4 Methodology 71\u003c\/p\u003e \u003cp\u003e4.4.1 System Architecture 71\u003c\/p\u003e \u003cp\u003e4.4.2 Database 71\u003c\/p\u003e \u003cp\u003e4.5 Experiments and Results 71\u003c\/p\u003e \u003cp\u003e4.5.1 FFNN 74\u003c\/p\u003e \u003cp\u003e4.5.2 RNN 76\u003c\/p\u003e \u003cp\u003e4.5.3 CNN 76\u003c\/p\u003e \u003cp\u003e4.5.4 CNN 76\u003c\/p\u003e \u003cp\u003e4.5.5 Pre-Trained Models 79\u003c\/p\u003e \u003cp\u003e4.6 Discussion 79\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 80\u003c\/p\u003e \u003cp\u003eReferences 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Honeypot: A Trap for Attackers 91\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnjanna Matta, G. Sucharitha, Bandlamudi Greeshmanjali, Manji Prashanth Kumar and Mathi Naga Sarath Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 92\u003c\/p\u003e \u003cp\u003e5.1.1 Research Honeypots 93\u003c\/p\u003e \u003cp\u003e5.1.2 Production Honeypots 93\u003c\/p\u003e \u003cp\u003e5.2 Method 94\u003c\/p\u003e \u003cp\u003e5.2.1 Low-Interaction Honeypots 94\u003c\/p\u003e \u003cp\u003e5.2.2 Medium-Interaction Honeypots 95\u003c\/p\u003e \u003cp\u003e5.2.3 High-Interaction Honeypots 95\u003c\/p\u003e \u003cp\u003e5.3 Cryptanalysis 96\u003c\/p\u003e \u003cp\u003e5.3.1 System Architecture 96\u003c\/p\u003e \u003cp\u003e5.3.2 Possible Attacks on Honeypot 97\u003c\/p\u003e \u003cp\u003e5.3.3 Advantages of Honeypots 98\u003c\/p\u003e \u003cp\u003e5.3.4 Disadvantages of Honeypots 99\u003c\/p\u003e \u003cp\u003e5.4 Conclusions 99\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Examining Security Aspect in Industrial-Based Internet of Things 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRohini Jha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 104\u003c\/p\u003e \u003cp\u003e6.2 Process Frame of IoT Before Security 105\u003c\/p\u003e \u003cp\u003e6.2.1 Cyber Attack 107\u003c\/p\u003e \u003cp\u003e6.2.2 Security Assessment in IoT 107\u003c\/p\u003e \u003cp\u003e6.2.2.1 Security in Perception and Network Frame 108\u003c\/p\u003e \u003cp\u003e6.3 Attacks and Security Assessments in IIoT 111\u003c\/p\u003e \u003cp\u003e6.3.1 IoT Security Techniques Analysis Based on its Merits 111\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 116\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm 123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eD. Chandrasekhar Rao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 124\u003c\/p\u003e \u003cp\u003e7.2 Related Works 126\u003c\/p\u003e \u003cp\u003e7.3 Problem Formulation 130\u003c\/p\u003e \u003cp\u003e7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm 134\u003c\/p\u003e \u003cp\u003e7.4.1 Basic Jaya Algorithm 134\u003c\/p\u003e \u003cp\u003e7.5 Hybrid Jaya-DE 136\u003c\/p\u003e \u003cp\u003e7.5.1 Mutation 136\u003c\/p\u003e \u003cp\u003e7.5.2 Crossover 136\u003c\/p\u003e \u003cp\u003e7.5.3 Selection 137\u003c\/p\u003e \u003cp\u003e7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm 139\u003c\/p\u003e \u003cp\u003e7.7 Total Navigation Path Deviation (TNPD) 147\u003c\/p\u003e \u003cp\u003e7.8 Average Unexplored Goal Distance (AUGD) 148\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 159\u003c\/p\u003e \u003cp\u003eReferences 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Categorization Model for Parkinson’s Disease Occurrence and Severity Prediction 163\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrashant Kumar Shrivastava, Ashish Chaturvedi, Megha Kamble and Megha Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 164\u003c\/p\u003e \u003cp\u003e8.2 Applications 166\u003c\/p\u003e \u003cp\u003e8.2.1 Machine Learning in PD Diagnosis 166\u003c\/p\u003e \u003cp\u003e8.2.2 Challenges of PD Detection 169\u003c\/p\u003e \u003cp\u003e8.2.3 Structuring of UPDRS Score 170\u003c\/p\u003e \u003cp\u003e8.3 Methodology 173\u003c\/p\u003e \u003cp\u003e8.3.1 Overview of Data Driven Intelligence 173\u003c\/p\u003e \u003cp\u003e8.3.2 Comparison Between Deep Learning and Traditional Machine 175\u003c\/p\u003e \u003cp\u003e8.3.3 Deep Learning for PD Diagnosis 176\u003c\/p\u003e \u003cp\u003e8.3.4 Convolution Neural Network for PD Diagnosis 176\u003c\/p\u003e \u003cp\u003e8.4 Proposed Models 178\u003c\/p\u003e \u003cp\u003e8.4.1 Classification of Patient and Healthy Controls 178\u003c\/p\u003e \u003cp\u003e8.4.2 Severity Score Classification 181\u003c\/p\u003e \u003cp\u003e8.5 Results and Discussion 184\u003c\/p\u003e \u003cp\u003e8.5.1 Performance Measures 185\u003c\/p\u003e \u003cp\u003e8.5.2 Graphical Results 187\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 187\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images 191\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShounak Chakraborty, Nikumani Choudhury and Indrajit Kalita\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 192\u003c\/p\u003e \u003cp\u003e9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images 194\u003c\/p\u003e \u003cp\u003e9.3 Deep Learning-Based Agriculture Monitoring 196\u003c\/p\u003e \u003cp\u003e9.4 Adaptive Approaches for Multi-Modal Classification 197\u003c\/p\u003e \u003cp\u003e9.4.1 Unsupervised DA 199\u003c\/p\u003e \u003cp\u003e9.4.2 Semi-Supervised DA 200\u003c\/p\u003e \u003cp\u003e9.4.3 Active Learning-Based DA 201\u003c\/p\u003e \u003cp\u003e9.5 System Model 202\u003c\/p\u003e \u003cp\u003e9.6 IEEE 802.15.4 204\u003c\/p\u003e \u003cp\u003e9.6.1 802.15.4 MAC 204\u003c\/p\u003e \u003cp\u003e9.6.2 DSME MAC 205\u003c\/p\u003e \u003cp\u003e9.6.3 TSCH MAC 206\u003c\/p\u003e \u003cp\u003e9.7 Analysis of IEEE 802.15.4 for Smart Agriculture 207\u003c\/p\u003e \u003cp\u003e9.7.1 Effect of Device Specification 207\u003c\/p\u003e \u003cp\u003e9.7.1.1 Low-Power 208\u003c\/p\u003e \u003cp\u003e9.7.2 Effect of MAC Protocols 208\u003c\/p\u003e \u003cp\u003e9.8 Experimental Results 209\u003c\/p\u003e \u003cp\u003e9.9 Conclusion \u0026amp; Future Directions 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Car Buying Criteria Evaluation Using Machine Learning Approach 223\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSamdeep Kumar Panda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 224\u003c\/p\u003e \u003cp\u003e10.2 Literature Survey 225\u003c\/p\u003e \u003cp\u003e10.3 Proposed Method 226\u003c\/p\u003e \u003cp\u003e10.4 Dataset 227\u003c\/p\u003e \u003cp\u003e10.5 Exploratory Data Analysis 227\u003c\/p\u003e \u003cp\u003e10.6 Splitting of Data Into Training Data and Test Data 230\u003c\/p\u003e \u003cp\u003e10.7 Pre-Processing 232\u003c\/p\u003e \u003cp\u003e10.8 Training of Our Models 232\u003c\/p\u003e \u003cp\u003e10.8.1 Gaussian Naïve Bayes 233\u003c\/p\u003e \u003cp\u003e10.8.2 Decision Tree Classifier 234\u003c\/p\u003e \u003cp\u003e10.8.3 Tuning the Model 235\u003c\/p\u003e \u003cp\u003e10.8.4 Karnough Nearest Neighbor Classifier 236\u003c\/p\u003e \u003cp\u003e10.8.5 Tuning the Model 237\u003c\/p\u003e \u003cp\u003e10.8.6 Neural Network 238\u003c\/p\u003e \u003cp\u003e10.8.7 Tuning the Model 239\u003c\/p\u003e \u003cp\u003e10.9 Result Analysis 240\u003c\/p\u003e \u003cp\u003e10.9.1 Confusion Matrix 240\u003c\/p\u003e \u003cp\u003e10.9.2 Gaussian Naïve Bayes 241\u003c\/p\u003e \u003cp\u003e10.9.3 Decision Tree Classifier 242\u003c\/p\u003e \u003cp\u003e10.9.4 Karnough Nearest Neighbor Classifier 242\u003c\/p\u003e \u003cp\u003e10.9.5 Neural Network 242\u003c\/p\u003e \u003cp\u003e10.9.6 Accuracy Scores 243\u003c\/p\u003e \u003cp\u003e10.10 Conclusion and Future Work 244\u003c\/p\u003e \u003cp\u003eReferences 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns 247\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMd. Safiullah and Neha Parveen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 248\u003c\/p\u003e \u003cp\u003e11.2 Big Data Reveals the Voters’ Preference 249\u003c\/p\u003e \u003cp\u003e11.2.1 Use of Software Applications in Election Campaigns 251\u003c\/p\u003e \u003cp\u003e11.2.1.1 Team Joe App 252\u003c\/p\u003e \u003cp\u003e11.2.1.2 Trump 2020 252\u003c\/p\u003e \u003cp\u003e11.2.1.3 Modi App 253\u003c\/p\u003e \u003cp\u003e11.3 Deep Fakes and Election Campaigns 254\u003c\/p\u003e \u003cp\u003e11.3.1 Deep Fake in Delhi Elections 254\u003c\/p\u003e \u003cp\u003e11.4 Social Media Bots 256\u003c\/p\u003e \u003cp\u003e11.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns 259\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Impact of Optimized Segment Routing in Software Defined Network 263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmrutanshu Panigrahi, Bibhuprasad Sahu, Satya Sobhan Panigrahi, Ajay Kumar Jena and Md. Sahil Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 264\u003c\/p\u003e \u003cp\u003e12.2 Software-Defined Network 266\u003c\/p\u003e \u003cp\u003e12.3 SDN Architecture 268\u003c\/p\u003e \u003cp\u003e12.4 Segment Routing 270\u003c\/p\u003e \u003cp\u003e12.5 Segment Routing in SDN 272\u003c\/p\u003e \u003cp\u003e12.6 Traffic Engineering in SDN 274\u003c\/p\u003e \u003cp\u003e12.7 Segment Routing Protocol 275\u003c\/p\u003e \u003cp\u003e12.8 Simulation and Result 277\u003c\/p\u003e \u003cp\u003e12.9 Conclusion and Future Work 278\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 An Investigation into COVID-19 Pandemic in India 289\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShubhangi V. Urkude, Vijaykumar R. Urkude, S. Vairachilai and Sandeep Kumar Panda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 289\u003c\/p\u003e \u003cp\u003e13.1.1 Symptoms of COVID-19 292\u003c\/p\u003e \u003cp\u003e13.1.2 Precautionary Measures 292\u003c\/p\u003e \u003cp\u003e13.1.3 Ways of Spreading the Coronavirus 294\u003c\/p\u003e \u003cp\u003e13.2 Literature Survey 295\u003c\/p\u003e \u003cp\u003e13.3 Technologies Used to Fight COVID-19 296\u003c\/p\u003e \u003cp\u003e13.3.1 Robots 296\u003c\/p\u003e \u003cp\u003e13.3.2 Drone Technology 297\u003c\/p\u003e \u003cp\u003e13.3.3 Crowd Surveillance 297\u003c\/p\u003e \u003cp\u003e13.3.4 Spraying the Disinfectant 298\u003c\/p\u003e \u003cp\u003e13.3.5 Sanitizing the Contaminated Areas 298\u003c\/p\u003e \u003cp\u003e13.3.6 Monitoring Temperature Using Thermal Camera 298\u003c\/p\u003e \u003cp\u003e13.3.7 Delivering the Essential Things 298\u003c\/p\u003e \u003cp\u003e13.3.8 Public Announcement in the Infected Areas 298\u003c\/p\u003e \u003cp\u003e13.4 Impact of COVID-19 on Business 299\u003c\/p\u003e \u003cp\u003e13.4.1 Impact on Financial Markets 299\u003c\/p\u003e \u003cp\u003e13.4.2 Impact on Supply Side 299\u003c\/p\u003e \u003cp\u003e13.4.3 Impact on Demand Side 300\u003c\/p\u003e \u003cp\u003e13.4.4 Impact on International Trade 300\u003c\/p\u003e \u003cp\u003e13.5 Impact of COVID-19 on Indian Economy 300\u003c\/p\u003e \u003cp\u003e13.6 Data and Result Analysis 300\u003c\/p\u003e \u003cp\u003e13.7 Conclusion and Future Scope 304\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy 307\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePoonam Biswal, Monali Saha, Nishtha Jaiswal and Minakhi Rout\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 307\u003c\/p\u003e \u003cp\u003e14.2 Literature Survey 308\u003c\/p\u003e \u003cp\u003e14.3 Methodology 310\u003c\/p\u003e \u003cp\u003e14.3.1 Dataset Preparation 310\u003c\/p\u003e \u003cp\u003e14.3.2 Dataset Loading and Data Pre-Processing 311\u003c\/p\u003e \u003cp\u003e14.3.3 Creating Models 312\u003c\/p\u003e \u003cp\u003e14.4 Models Used 312\u003c\/p\u003e \u003cp\u003e14.5 Simulation Results 313\u003c\/p\u003e \u003cp\u003e14.5.1 Changing Size of MaxPool2D(n,n) 314\u003c\/p\u003e \u003cp\u003e14.5.2 Changing Size of AveragePool2D(n,n) 314\u003c\/p\u003e \u003cp\u003e14.5.3 Changing Number of con2d(32n–64n) Layers 315\u003c\/p\u003e \u003cp\u003e14.5.4 Changing Number of con2d-32*n Layers 315\u003c\/p\u003e \u003cp\u003e14.5.5 ROC Curves and MSE Curves 318\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 321\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain 323\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAbhishek Kumar Kashyap, Anish Pandey and Dayal R. Parhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 324\u003c\/p\u003e \u003cp\u003e15.2 Design of Proposed Algorithm 328\u003c\/p\u003e \u003cp\u003e15.2.1 Mechanism of Artificial Potential Field 328\u003c\/p\u003e \u003cp\u003e15.2.1.1 Potential Field Generated by Attractive Force of Goal 329\u003c\/p\u003e \u003cp\u003e15.2.1.2 Potential Field Generated by Repulsive Force of Obstacle 331\u003c\/p\u003e \u003cp\u003e15.2.2 Mechanism of Firefly Algorithm 332\u003c\/p\u003e \u003cp\u003e15.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm 335\u003c\/p\u003e \u003cp\u003e15.2.3 Dining Philosopher Controller 337\u003c\/p\u003e \u003cp\u003e15.3 Hybridization Process of Proposed Algorithm 339\u003c\/p\u003e \u003cp\u003e15.4 Execution of Proposed Algorithm in Multiple Humanoid Robots 339\u003c\/p\u003e \u003cp\u003e15.5 Comparison 344\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 346\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society 351\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVinutha D.C., Kavyashree S., Vijay C.P. and G.T. Raju\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 352\u003c\/p\u003e \u003cp\u003e16.2 Literature Survey 353\u003c\/p\u003e \u003cp\u003e16.2.1 AI in Auto-Grading 354\u003c\/p\u003e \u003cp\u003e16.2.2 AI in Smart Content 356\u003c\/p\u003e \u003cp\u003e16.2.3 AI in Auto Analysis on Student’s Grade 356\u003c\/p\u003e \u003cp\u003e16.2.4 AI Extends Free Intelligent Tutoring 357\u003c\/p\u003e \u003cp\u003e16.2.5 AI in Predicting Student Admission and Drop-Out Rate 359\u003c\/p\u003e \u003cp\u003e16.3 Proposed System 359\u003c\/p\u003e \u003cp\u003e16.3.1 Data Collection Module 360\u003c\/p\u003e \u003cp\u003e16.3.2 Data Pre-Processing Module 364\u003c\/p\u003e \u003cp\u003e16.3.3 Clustering Module 364\u003c\/p\u003e \u003cp\u003e16.3.4 Partner Selection Module 366\u003c\/p\u003e \u003cp\u003e16.4 Results 368\u003c\/p\u003e \u003cp\u003e16.5 Future Enhancements 370\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 370\u003c\/p\u003e \u003cp\u003eReferences 371\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures 373\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eN. Yamuna, J. Antony Vijay and B. Gomathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 374\u003c\/p\u003e \u003cp\u003e17.2 Literature Survey 375\u003c\/p\u003e \u003cp\u003e17.2.1 Machine Learning 378\u003c\/p\u003e \u003cp\u003e17.3 Proposed System 379\u003c\/p\u003e \u003cp\u003e17.3.1 Load Aware Cloud Computing Model 379\u003c\/p\u003e \u003cp\u003e17.3.2 Wavelet Neural Network 379\u003c\/p\u003e \u003cp\u003e17.3.3 Evaluation Using LOOCV Model 380\u003c\/p\u003e \u003cp\u003e17.3.4 k-Nearest Neighbor (k-NN) Algorithm 381\u003c\/p\u003e \u003cp\u003e17.3.5 Particle Swarm Optimization (PSO) Algorithm 382\u003c\/p\u003e \u003cp\u003e17.3.6 HWkNN Optimization Algorithm Based on PSO 383\u003c\/p\u003e \u003cp\u003e17.3.7 PSO-Based HWkNN (PHWkNN) Load Prediction Algorithm 384\u003c\/p\u003e \u003cp\u003e17.4 Experimental Results 385\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 390\u003c\/p\u003e \u003cp\u003eReferences 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 An Extensive Survey on the Prediction of Bankruptcy 395\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSasmita Manjari Nayak and Minakhi Rout\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 395\u003c\/p\u003e \u003cp\u003e18.2 Literature Survey 397\u003c\/p\u003e \u003cp\u003e18.2.1 Data Pre-Processing 397\u003c\/p\u003e \u003cp\u003e18.2.1.1 Balancing of Imbalanced Dataset 397\u003c\/p\u003e \u003cp\u003e18.2.1.2 Outlier Data Handling 410\u003c\/p\u003e \u003cp\u003e18.2.2 Classifiers 418\u003c\/p\u003e \u003cp\u003e18.2.3 Ensemble Models 422\u003c\/p\u003e \u003cp\u003e18.3 System Architecture and Simulation Results 438\u003c\/p\u003e \u003cp\u003e18.4 Conclusion 438\u003c\/p\u003e \u003cp\u003eReferences 443\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques 447\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eManoj Kumar, Pratibha Maurya and Rinki Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 448\u003c\/p\u003e \u003cp\u003e19.2 Overview of AI and Machine Learning 450\u003c\/p\u003e \u003cp\u003e19.3 Review of Literature 452\u003c\/p\u003e \u003cp\u003e19.4 Application of AI \u0026amp; Machine Learning in Agriculture 456\u003c\/p\u003e \u003cp\u003e19.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector 460\u003c\/p\u003e \u003cp\u003e19.6 Opportunities for Agricultural Operations in India 465\u003c\/p\u003e \u003cp\u003e19.7 Conclusion 466\u003c\/p\u003e \u003cp\u003eReferences 467\u003c\/p\u003e \u003cp\u003eIndex 473\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407163662679,"sku":"9781119824473","price":168.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119824473.jpg?v=1730498394","url":"https:\/\/bookcurl.com\/products\/the-new-advanced-society-9781119824473","provider":"Book Curl","version":"1.0","type":"link"}