John Wiley & Sons The New Advanced Society Cover THE NEW ADVANCED SOCIETY Included in this book are the fundamentals of Society 5.0, artificial inte.. Product #: 978-1-119-82447-3 Regular price: $195.33 $195.33 In Stock

The New Advanced Society

Artificial Intelligence and Industrial Internet of Things Paradigm

Panda, Sandeep Kumar / Mohapatra, Ramesh Kumar / Panda, Subhrakanta / Balamurugan, S. (Editor)

Wiley-Scrivener

Cover

1. Edition April 2022
512 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-82447-3
John Wiley & Sons

Buy now

Price: 209,00 €

Price incl. VAT, excl. Shipping

Further versions

epubmobipdf

THE NEW ADVANCED SOCIETY

Included in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial Internet of Things, featuring their working principles and application in different sectors.

A 360-degree view of the different dimensions of the digital revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead, and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart 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.

Preface xvii

Acknowledgments xxiii

1 Post Pandemic: The New Advanced Society 1
Sujata 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 15
Sandeep 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 43
Soumya 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 67
Banhi 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 91
Anjanna 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 103
Rohini 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 123
D. 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 163
Prashant 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 191
Shounak Chakraborty, Nikumani Choudhury and Indrajit Kalita

9.1 Introduction 192

9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images 194

9.3 Deep Learning-Based Agriculture Monitoring 196

9.4 Adaptive Approaches for Multi-Modal Classification 197

9.4.1 Unsupervised DA 199

9.4.2 Semi-Supervised DA 200

9.4.3 Active Learning-Based DA 201

9.5 System Model 202

9.6 IEEE 802.15.4 204

9.6.1 802.15.4 MAC 204

9.6.2 DSME MAC 205

9.6.3 TSCH MAC 206

9.7 Analysis of IEEE 802.15.4 for Smart Agriculture 207

9.7.1 Effect of Device Specification 207

9.7.1.1 Low-Power 208

9.7.2 Effect of MAC Protocols 208

9.8 Experimental Results 209

9.9 Conclusion & Future Directions 212

References 212

10 Car Buying Criteria Evaluation Using Machine Learning Approach 223
Samdeep 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 247
Md. 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 263
Amrutanshu 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 289
Shubhangi 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 307
Poonam 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 323
Abhishek 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 351
Vinutha 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 373
N. 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 395
Sasmita 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 447
Manoj 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
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.

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.