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