Distributed Time-Sensitive Systems
1st Edition
Published on 9. May 2025
Book
Hardback
400 pages
978-1-394-19772-9 (ISBN)
Description
The book provides invaluable insights into cutting-edge advancements across multiple sectors of Society 5.0, where contemporary concepts and interdisciplinary applications empower you to understand and engage with the transformative technologies shaping our future.
Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices.
The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape.
Audience
Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0
Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices.
The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape.
Audience
Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Weight
680 gr
ISBN-13
978-1-394-19772-9 (9781394197729)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Other editions
Additional editions
Tanupriya Choudhury | Rahul Kumar Singh | Ravi Tomar
Distributed Time-Sensitive Systems
E-Book
05/2025
1st Edition
Wiley
€168.99
Available for download
Tanupriya Choudhury | Rahul Kumar Singh | Ravi Tomar
Distributed Time-Sensitive Systems
E-Book
04/2025
1st Edition
Wiley
€177.99
Available for download
Persons
Tanupriya Choudhury, PhD, is a professor and an Associate Dean of Research at Graphic Era University, Dehradun, India with over 15 years of teaching and research experience. He has published 15 books, filed 25 patents, and developed 16 software programs for India's Ministry of Human Resource Development in addition to serving as a reviewer and editor for several international journals and conferences. His areas of expertise include human computing, soft computing, cloud computing, and data mining.
Rahul Kumar Singh, PhD, is an assistant professor at the University of Petroleum and Energy Studies in Dehradun, India, with a decade of teaching experience. He has published numerous papers in international journals, presented at international conferences, and serves as a reviewer for leading AI journals. His research interests include neurosymbolic AI for enhancing natural language processing for sentiment analysis, dialogue systems, and financial forecasting, affective computing, and theoretical computer science.
Ravi Tomar, PhD, is a senior architect at Persistent Systems, India with a history in the higher education industry. He has delivered training to national and international corporations on confluent Apache Kafka, stream processing, CordaApp, J2EE, and IoT to clients like KeyBank, Accenture, Union Bank of Philippines, Ernst and Young, and Deloitte. His research interests include wireless sensor networks, image processing, data mining and warehousing, computer networks, and big data.
S. Balamurugan, PhD, is the Director of Research and Development at Intelligent Research Consultancy Services in Coimbatore, India with a history in higher education. He has contributed 45 books and over 200 papers to international journals and conferences, secured 35 patents, and holds editorial roles across more than 500 international scientific publications. His research interests include Artificial Intelligence, wearable computing, machine learning, and deep learning.
J. C. Patni, PhD, is a professor at the Symbiosis Institute of Technology Nagpur, Symbiosis International University, India with more than 18 years of teaching and administrative experience. He has authored over 80 research articles in national and international journals and conferences and over ten books and book chapters, has been granted seven Indian patents, and has filed an additional 21 patents. His research interests include Artificial Intelligence, machine learning, deep learning, high-performance computing, and software engineering.
Rahul Kumar Singh, PhD, is an assistant professor at the University of Petroleum and Energy Studies in Dehradun, India, with a decade of teaching experience. He has published numerous papers in international journals, presented at international conferences, and serves as a reviewer for leading AI journals. His research interests include neurosymbolic AI for enhancing natural language processing for sentiment analysis, dialogue systems, and financial forecasting, affective computing, and theoretical computer science.
Ravi Tomar, PhD, is a senior architect at Persistent Systems, India with a history in the higher education industry. He has delivered training to national and international corporations on confluent Apache Kafka, stream processing, CordaApp, J2EE, and IoT to clients like KeyBank, Accenture, Union Bank of Philippines, Ernst and Young, and Deloitte. His research interests include wireless sensor networks, image processing, data mining and warehousing, computer networks, and big data.
S. Balamurugan, PhD, is the Director of Research and Development at Intelligent Research Consultancy Services in Coimbatore, India with a history in higher education. He has contributed 45 books and over 200 papers to international journals and conferences, secured 35 patents, and holds editorial roles across more than 500 international scientific publications. His research interests include Artificial Intelligence, wearable computing, machine learning, and deep learning.
J. C. Patni, PhD, is a professor at the Symbiosis Institute of Technology Nagpur, Symbiosis International University, India with more than 18 years of teaching and administrative experience. He has authored over 80 research articles in national and international journals and conferences and over ten books and book chapters, has been granted seven Indian patents, and has filed an additional 21 patents. His research interests include Artificial Intelligence, machine learning, deep learning, high-performance computing, and software engineering.
Editor
University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
University of Petroleum and Energy Studies, Dehradun, India
Persistent Systems, India
Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamil Nadu, India
Department of CSE, Alliance School of Advance Computing, Alliance University Bengaluru, India
Content
Preface xix
Acknowledgement xxiii
1 Analytical Survey of AI Data Analysis Techniques 1
Divyansh Singhal, Roohi Sille, Tanupriya Choudhury, Thinagaran Perumal and Ashutosh Sharma
1.1 Introduction 2
1.2 Survey on Various AI Techniques in Multiple Data Inputs 2
1.2.1 AI Techniques in E-Commerce 2
1.2.1.1 Benefits of Using AI in Ecommerce Companies 4
1.2.1.2 AI Use Cases in E-Commerce 5
1.2.2 AI Techniques in Healthcare 10
1.2.2.1 Machine Learning 11
1.2.2.2 Natural Language Processing (NLP) 13
1.2.2.3 Rule Based Expert Systems 14
1.2.2.4 Physical Robots 14
1.2.2.5 Robotic Process Automation 15
1.2.2.6 Administrative Applications 15
1.2.2.7 AR/VR 16
1.2.2.8 Ways on How AI Will Create an Impact in Healthcare Industry 18
1.3 Conclusion 20
References 22
2 Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions 25
Rimjhim Gupta, Roohi Sille and Tanupriya Choudhury
2.1 Introduction 26
2.2 Literature Review 28
2.2.1 ARIMA (Auto Regressive Integrated Moving Average) 29
2.2.2 Linear Regression 29
2.2.3 KNN (K-Nearest Neighbor) 29
2.2.4 Decision Tree 30
2.2.5 Random Forest 31
2.2.6 Support Vector Regression 31
2.2.7 Support Vector Machine 32
2.2.8 Long Short-Term Memory Network Model 32
2.2.9 Extreme Gradient Boosting (XGBoost) 33
2.3 Applications of Machine Learning (ML) and Deep Learning (DL) Model 35
2.4 Conclusions and Future Perspective 36
References 37
3 Implementation of High Speed Adders for Image Blending Applications 43
P. Vanjipriya, K. N. Vijeyakumar, E. Udayakumar and S. Vishnushree
3.1 Introduction 43
3.2 Area and Delay Analysis of Addition Algorithm 45
3.2.1 Carry Select Addition 45
3.2.2 Carry Lookahead Addition 45
3.2.3 Kogge Stone Addition 46
3.3 Design of High Speed Adder 48
3.3.1 Carry Select Adder 49
3.3.2 Carry Lookahead Adder 49
3.3.3 Kogge Stone Adder 51
3.4 Results and Discussion 53
3.4.1 ASIC Implementation Results 53
3.5 FPGA Implementation in Digital Image Processing 58
3.5.1 Image Blending 58
3.6 Conclusion 61
References 61
4 Smart Factories and Energy Efficiency in Industry 4.0 63
S.C. Vetrivel, T.P. Saravanan and R. Maheswari
4.1 Introduction 64
4.1.1 Background of Industry [4.0] and Its Impact on Manufacturing 64
4.1.2 Importance of Energy Efficiency in Smart Factories 65
4.1.3 Objectives and Scope of the Paper 66
4.1.3.1 Objectives 66
4.1.3.2 Scope 66
4.2 Industry 4.0: Concepts and Technologies 67
4.2.1 Overview of Industry 4.0 and its Key Principles 67
4.2.2 Smart Factories and their Role in Industry 4.0 69
4.2.3 Technologies Enabling Smart Factories (e.g., IoT, Bigdata, AI) 69
4.3 Energy Efficiency in Manufacturing 71
4.3.1 Significance of Energy Efficiency in the Manufacturing Sector 71
4.3.2 Opportunities and Obstacles to Enhancing Energy Efficiency 73
4.3.3 Benefits of Energy-Efficient Practices in Smart Factories 75
4.4 Integration of Energy Management Systems in Smart Factories 76
4.4.1 Introduction to Energy Management Systems (EMS) 76
4.4.1.1 Key Components of an Energy Management System 77
4.4.1.2 Benefits of Energy Management Systems 77
4.4.2 Role of EMS in Achieving Energy Efficiency in Smart Factories 78
4.4.3 Key Components and Functionalities of EMS in Industry 4.0 80
4.5 Energy Monitoring and Optimization in Smart Factories 82
4.5.1 Importance of Real-Time Energy Monitoring in Smart Factories 82
4.5.2 Sensor Technologies and Data Collection for Energy Monitoring 82
4.5.3 Optimization Techniques for Energy Consumption in Manufacturing Processes 83
4.6 Intelligent Control Systems for Energy Efficiency 85
4.6.1 Application of AI & AL in Energy Management 85
4.6.2 Intelligent Control Systems for Optimizing Energy Usage 86
4.6.3 Case Studies Showcasing the Effectiveness of Intelligent Control Systems 87
4.7 Energy Storage and Renewable Energy Integration 88
4.7.1 Utilization of Energy Storage Systems in Smart Factories 88
4.7.2 Integration of Renewable Energy Sources in Manufacturing Processes 88
4.7.3 Benefits and Challenges of Incorporating Energy Storage and Renewable 89
4.7.3.1 Benefits of Incorporating Energy Storage and Renewables 89
4.7.3.2 Challenges of Incorporating Energy Storage and Renewables 90
4.8 Smart Grid Integration and Demand Response 91
4.8.1 Smart Grids' Contribution to Smart Industries' Increased Energy Efficiency 91
4.8.2 Demand Response Strategies for Managing Energy Consumption 93
4.8.3 Synergies Between Smart Factories and Smart Grids 94
4.9 Case Studies and Best Practices 95
4.9.1 Case Studies Highlighting Successful Implementation of Energy Efficiency Measures in Smart Factories 95
4.9.2 Best Practices for Achieving Energy Efficiency in Industry 4.0 in Indian Scenario 96
4.10 Challenges and Future Directions 98
4.10.1 Challenges and Barriers to Implementing Energy Efficiency in Smart Factories 98
4.10.2 Emerging Trends and Future Directions in Smart Factories and Energy Efficiency 99
4.10.3 Policy Implications and Recommendations for Industry Stakeholders 100
4.11 Conclusion 101
References 102
5 AI in Computer Vision with Emerging Techniques and Their Scope 105
Pawan K. Mishra, Shalini Verma, Jagdish C. Patni and Rajat Dubey
5.1 Brief Introduction of Computer Vision 106
5.1.1 Define Computer Vision 106
5.1.2 A Brief History 106
5.1.3 Chapter Overview 107
5.2 A Pictorial Summary of Image Formation 108
5.2.1 Image Formation 108
5.2.2 Geometric Primitives and Transformations 111
5.2.3 Photometric Image Formation 114
5.2.4 The Digital Camera 114
5.3 Sampling and Aliasing 115
5.3.1 Sampling of Pitch 116
5.3.2 Fill Factor 116
5.4 Feature Detection 116
5.4.1 Points and Patches of the Image 118
5.5 Image Segmentation 119
5.5.1 Active Contour Level Sets 120
5.6 Computational Photography 122
5.6.1 Radiometric Response Function Value 122
5.6.2 Vignetting of the View 124
5.6.3 Optical Blur (Spatial Response) Estimation 124
5.7 Recognition 125
5.7.1 High Dynamic Range Imaging 126
5.7.1.1 Tone Mapping 126
5.7.1.2 Super-Resolution and Blur Removal 126
5.7.2 Face Detection 127
5.8 Visual Tracking of the Object 128
5.9 Conclusion 129
References 130
6 Revolutionizing Car Manufacturing the Power of Intelligent Robotic Process Automation 133
Amit K. Nerurkar and G. T. Thampi
6.1 Introduction 134
6.1.1 Differences Between RPA vs IPA? 135
6.1.2 AI Enabled Robots 135
6.1.3 Artificially Intelligent Robots 135
6.1.4 Ethical Issues Involved in Integration of AI Technologies and Robotics in Assembly Line 136
6.1.5 Current State of Car Manufacturing in India 137
6.2 Literature Survey 139
6.3 Exploratory Analysis 143
6.4 The Manufacturing Process in India 146
6.5 Degree of Integration for Using Robotic Process Automation Automotive Sector 147
6.6 Complexities and Solution to Integrate AI in Current RPA 148
6.7 What Next in Indian Car Manufacturing? 150
6.8 Conclusion 150
References 151
7 Industry 5.0 and Artificial Intelligence: A Match Made in Technology Heaven? Unleashing the Potential of Artificial Intelligence in Industry 5.0 153
Bhanu Priya, Vivek Sharma and Rahul Sharma
7.1 Introduction 154
7.2 Review of Literature 155
7.2.1 Background of Industry 5.0 155
7.2.2 Definition of Industry 5.0 157
7.2.3 Artificial Intelligence and Industry 5.0 158
7.3 Research Model of How AI Works in Industry 5.0 159
7.3.1 Artificial Intelligence Tools 159
7.3.1.1 Machine Learning 160
7.3.1.2 Robotics 162
7.3.1.3 Conversational Interfaces 164
7.3.1.4 Intelligent Agents 165
7.3.1.5 Edge Computing 167
7.3.2 Integration of AI with Other Advanced Technologies 169
7.3.2.1 Digital Twins 169
7.3.2.2 6G Technology 169
7.3.2.3 Explainable Artificial Intelligence 170
7.3.2.4 Blockchain 171
7.3.2.5 Security Cover by AI 172
7.4 Smart Factories and Manufacturing Processes 173
7.4.1 Predictive Maintenance, Quality, and Supply Chain Synergy 174
7.4.1.1 Predictive Maintenance 174
7.4.1.2 Quality Control and Defect Detection 175
7.4.1.3 Supply Chain Optimization 176
7.4.2 Industrial Internet of Things (IIoT) and Data Analytics 176
7.4.2.1 Real-Time Monitoring and Analysis 177
7.4.2.2 Predictive Modeling and Forecasting 177
7.4.2.3 Asset Tracking and Management 178
7.4.3 Robotics and Automation 178
7.4.3.1 Collaborative Robots (Cobots) 178
7.4.3.2 Autonomous Vehicles and Drones 179
7.4.3.3 Human-Robot Collaboration 180
7.5 Outcomes of AI in Industry 5.0 181
7.5.1 Sustainability 181
7.5.1.1 Environmental Sustainability 182
7.5.1.2 Society 5.0 183
7.5.2 Resilience and IR 5 187
7.5.3 New Business Models 188
7.6 Challenges of Industry 5.0 189
7.7 Conclusion 190
References 191
8 A VLSI-Based Multi-Level ECG Compression Scheme with RL and VL Encoding 203
P. Balasubramani, S. Swathi Krishna and E. Udayakumar
8.1 Introduction 204
8.2 Literature Survey 204
8.3 Proposed System 205
8.4 Proposed Multi-Level ECG Compression Architecture 207
8.5 Results and Analysis 212
8.6 Conclusion 216
References 216
9 Using Reinforcement Learning in Unity Environments for Training AI-Agent 219
Geetika Munjal and Monika Lamba
9.1 Introduction 219
9.2 Literature Review 221
9.3 Machine Learning 221
9.3.1 Categorization of Machine Learning 222
9.3.1.1 Supervised Learning 222
9.3.1.2 Unsupervised Learning 222
9.3.1.3 Reinforcement Learning 223
9.3.2 Classifying on the Basis of Envisioned Output 224
9.3.2.1 Classification 224
9.3.2.2 Regression 224
9.3.2.3 Clustering 224
9.3.3 Artificial Intelligence 224
9.4 Unity 225
9.4.1 Unity Hub 225
9.4.2 Unity Editor 225
9.4.3 Inspector 225
9.4.4 Game View 225
9.4.5 Scene View 226
9.4.6 Hierarchy 226
9.4.7 Project Window 226
9.5 Reinforcement Learning and Supervised Learning 227
9.5.1 Positive Reinforcement 228
9.5.2 Negative Reinforcement 228
9.5.3 Model-Free and Model-Based RL 228
9.6 Proposed Model 230
9.6.1 Setting Up a Virtual Environment 231
9.6.2 Setting Up of the Environment 231
9.6.2.1 Creating and Allocating Scripts for the Environment 232
9.6.2.2 Creating a Goal for the Agent 232
9.6.2.3 Reward Driven Behavior 233
9.7 Markov Decision Process 234
9.8 Model Based RL 234
9.9 Experimental Results 235
9.9.1 Machine Learning Models Used for the Environments 235
9.9.2 PushBlock 236
9.9.3 Hallway 236
9.9.4 Screenshots of the PushBlock Environment 236
9.9.5 Screenshots of the Hallway Environment 242
9.10 Conclusion 245
References 245
10 A Review of Digital Transformation and Sustainable International Agricultural Businesses in Africa 249
Shadreck Matindike, Stephen Mago, Flora Modiba and Amanda Van den Berg
10.1 Introduction 249
10.1.1 Background 251
10.1.1.1 Digitalization in Agriculture and SDGs 253
10.1.1.2 International Agricultural Businesses and Sustainable Development 254
10.1.1.3 Research Questions and Objectives 255
10.1.1.4 Significance of the Study 256
10.2 Methodology 256
10.2.1 Research Strategy 256
10.2.2 Search Strategy 257
10.2.2.1 Database Identification 257
10.2.2.2 Search Strings 258
10.2.2.3 Exclusion and Inclusion Criteria 259
10.3 Findings 260
10.3.1 Literature Landscape without Filters 260
10.3.1.1 Publications Output 260
10.3.1.2 Academic Impact (Citations) 261
10.3.1.3 Major Sources of Literature on the Topic 261
10.3.1.4 Major Authors of Literature on the Topic 261
10.3.2 Literature Landscape with Filters 263
10.3.2.1 Bibliometric Analysis of Publication Output 263
10.3.2.2 Bibliometric Analysis of Keywords 263
10.3.2.3 Bibliometric Analysis of Themes of Topics 265
10.3.2.4 Bibliometric Analysis of Citations Across Countries 265
10.3.3 Digital Transformation, Sustainability and International Businesses in African Agriculture 267
10.3.3.1 Plant Monitoring 269
10.3.3.2 Phenotyping 269
10.3.3.3 Weeding 270
10.3.3.4 Seeding 271
10.3.3.5 Disease Detection 271
10.3.4 Potential of International Businesses in African Agriculture 272
10.4 Recommendations 275
10.5 Conclusion 276
References 278
11 Developing a Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context: A Case of Zimbabwe 283
Samuel Musungwini
11.1 Introduction 284
11.2 Background and Context 285
11.3 Methodology 288
11.3.1 Design Science 288
11.4 Literature Review 291
11.4.1 Current State of Disruptive Emerging Technologies in Health Care Delivery 292
11.4.2 The Current State of DETs in SSA 294
11.4.3 Healthcare Challenges Currently Prevalent in SSA Lack Proper Medical Attention 295
11.4.4 Opportunities for Implementing DETs in Health in SSA 296
11.5 Empirical Data 296
11.5.1 Potential Benefits of Implementing Disruptive Emerging Technologies in Health Care Delivery in a Developing Country like Zimbabwe 298
11.5.2 Challenges and Opportunities Associated with Harnessing these Technologies for the Benefit of Society 5.0 in Zimbabwe 300
11.6 Discussion 302
11.7 A Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context 304
11.7.1 Layer 1: Environmental Scanning and Diagnostic Analysis 305
11.7.2 Layer 2: Strategic Planning Roadmap 306
11.7.3 Layer 3: Integrate, Implement, and Operationalise D.E.TS in Select Healthcare Facilities 307
11.7.4 Layer 4: Evaluation and Review 308
11.7.5 Layer 5: Roll Out D.E.TS in All Healthcare Services and Processes 308
11.8 Conclusions and Recommendations 308
References 310
12 IT Innovation: Driving Digital Transformation 315
Sruthy S.
12.1 Introduction 316
12.2 The IT Innovation Ecosystem 318
12.3 Types of IT Innovations 320
12.4 IT Innovation Frameworks 324
12.5 Challenges and Risks of IT Innovation 325
12.6 Case Study: Uber - Disrupting the Transportation Industry with Innovative Technology 328
12.7 Future Directions of IT Innovation 335
References 339
13 Strategic Convergence of Advanced Technologies in Modern Warfare 341
Ayan Sar, Tanupriya Choudhury, Jung-Sup Um, Rahul Kumar Singh and Ketan Kotecha
13.1 Introduction 342
13.2 Quantum Computing and Cryptography 342
13.2.1 Quantum Computing for Secure Communication 342
13.2.2 Quantum Key Distribution in Military Networks 343
13.2.3 Potential Impact of Quantum Computing on Cybersecurity 344
13.3 Blockchain Technology in Military Operations 345
13.3.1 Immutable Record-Keeping and Supply Chain Management 346
13.3.2 Smart Contracts for Streamlining Military Processes 347
13.3.3 Enhanced Security and Transparent Transactions 348
13.4 Case-Studies and Real-World Applications 349
13.4.1 Autonomous Aerial Reconnaissance - Predator and Reaper Drones (U.S.A) 349
13.4.2 Blockchain in Military Supply Chain Management 350
13.4.3 AI-Driven Decision Support Systems 350
13.4.4 Aegis Combat System (U.S. Navy) 350
13.4.5 Adaptation in Response to Threats: Stuxnet Worm 351
13.5 Challenges and Risks 352
13.5.1 Ethical Dilemmas in the Use of Disruptive Technologies 352
13.5.2 Vulnerabilities and Exploits in Cyber-Physical Systems 352
13.5.3 International Cooperation and Regulations 353
13.6 Conclusion 353
References 354
Index 355
Acknowledgement xxiii
1 Analytical Survey of AI Data Analysis Techniques 1
Divyansh Singhal, Roohi Sille, Tanupriya Choudhury, Thinagaran Perumal and Ashutosh Sharma
1.1 Introduction 2
1.2 Survey on Various AI Techniques in Multiple Data Inputs 2
1.2.1 AI Techniques in E-Commerce 2
1.2.1.1 Benefits of Using AI in Ecommerce Companies 4
1.2.1.2 AI Use Cases in E-Commerce 5
1.2.2 AI Techniques in Healthcare 10
1.2.2.1 Machine Learning 11
1.2.2.2 Natural Language Processing (NLP) 13
1.2.2.3 Rule Based Expert Systems 14
1.2.2.4 Physical Robots 14
1.2.2.5 Robotic Process Automation 15
1.2.2.6 Administrative Applications 15
1.2.2.7 AR/VR 16
1.2.2.8 Ways on How AI Will Create an Impact in Healthcare Industry 18
1.3 Conclusion 20
References 22
2 Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions 25
Rimjhim Gupta, Roohi Sille and Tanupriya Choudhury
2.1 Introduction 26
2.2 Literature Review 28
2.2.1 ARIMA (Auto Regressive Integrated Moving Average) 29
2.2.2 Linear Regression 29
2.2.3 KNN (K-Nearest Neighbor) 29
2.2.4 Decision Tree 30
2.2.5 Random Forest 31
2.2.6 Support Vector Regression 31
2.2.7 Support Vector Machine 32
2.2.8 Long Short-Term Memory Network Model 32
2.2.9 Extreme Gradient Boosting (XGBoost) 33
2.3 Applications of Machine Learning (ML) and Deep Learning (DL) Model 35
2.4 Conclusions and Future Perspective 36
References 37
3 Implementation of High Speed Adders for Image Blending Applications 43
P. Vanjipriya, K. N. Vijeyakumar, E. Udayakumar and S. Vishnushree
3.1 Introduction 43
3.2 Area and Delay Analysis of Addition Algorithm 45
3.2.1 Carry Select Addition 45
3.2.2 Carry Lookahead Addition 45
3.2.3 Kogge Stone Addition 46
3.3 Design of High Speed Adder 48
3.3.1 Carry Select Adder 49
3.3.2 Carry Lookahead Adder 49
3.3.3 Kogge Stone Adder 51
3.4 Results and Discussion 53
3.4.1 ASIC Implementation Results 53
3.5 FPGA Implementation in Digital Image Processing 58
3.5.1 Image Blending 58
3.6 Conclusion 61
References 61
4 Smart Factories and Energy Efficiency in Industry 4.0 63
S.C. Vetrivel, T.P. Saravanan and R. Maheswari
4.1 Introduction 64
4.1.1 Background of Industry [4.0] and Its Impact on Manufacturing 64
4.1.2 Importance of Energy Efficiency in Smart Factories 65
4.1.3 Objectives and Scope of the Paper 66
4.1.3.1 Objectives 66
4.1.3.2 Scope 66
4.2 Industry 4.0: Concepts and Technologies 67
4.2.1 Overview of Industry 4.0 and its Key Principles 67
4.2.2 Smart Factories and their Role in Industry 4.0 69
4.2.3 Technologies Enabling Smart Factories (e.g., IoT, Bigdata, AI) 69
4.3 Energy Efficiency in Manufacturing 71
4.3.1 Significance of Energy Efficiency in the Manufacturing Sector 71
4.3.2 Opportunities and Obstacles to Enhancing Energy Efficiency 73
4.3.3 Benefits of Energy-Efficient Practices in Smart Factories 75
4.4 Integration of Energy Management Systems in Smart Factories 76
4.4.1 Introduction to Energy Management Systems (EMS) 76
4.4.1.1 Key Components of an Energy Management System 77
4.4.1.2 Benefits of Energy Management Systems 77
4.4.2 Role of EMS in Achieving Energy Efficiency in Smart Factories 78
4.4.3 Key Components and Functionalities of EMS in Industry 4.0 80
4.5 Energy Monitoring and Optimization in Smart Factories 82
4.5.1 Importance of Real-Time Energy Monitoring in Smart Factories 82
4.5.2 Sensor Technologies and Data Collection for Energy Monitoring 82
4.5.3 Optimization Techniques for Energy Consumption in Manufacturing Processes 83
4.6 Intelligent Control Systems for Energy Efficiency 85
4.6.1 Application of AI & AL in Energy Management 85
4.6.2 Intelligent Control Systems for Optimizing Energy Usage 86
4.6.3 Case Studies Showcasing the Effectiveness of Intelligent Control Systems 87
4.7 Energy Storage and Renewable Energy Integration 88
4.7.1 Utilization of Energy Storage Systems in Smart Factories 88
4.7.2 Integration of Renewable Energy Sources in Manufacturing Processes 88
4.7.3 Benefits and Challenges of Incorporating Energy Storage and Renewable 89
4.7.3.1 Benefits of Incorporating Energy Storage and Renewables 89
4.7.3.2 Challenges of Incorporating Energy Storage and Renewables 90
4.8 Smart Grid Integration and Demand Response 91
4.8.1 Smart Grids' Contribution to Smart Industries' Increased Energy Efficiency 91
4.8.2 Demand Response Strategies for Managing Energy Consumption 93
4.8.3 Synergies Between Smart Factories and Smart Grids 94
4.9 Case Studies and Best Practices 95
4.9.1 Case Studies Highlighting Successful Implementation of Energy Efficiency Measures in Smart Factories 95
4.9.2 Best Practices for Achieving Energy Efficiency in Industry 4.0 in Indian Scenario 96
4.10 Challenges and Future Directions 98
4.10.1 Challenges and Barriers to Implementing Energy Efficiency in Smart Factories 98
4.10.2 Emerging Trends and Future Directions in Smart Factories and Energy Efficiency 99
4.10.3 Policy Implications and Recommendations for Industry Stakeholders 100
4.11 Conclusion 101
References 102
5 AI in Computer Vision with Emerging Techniques and Their Scope 105
Pawan K. Mishra, Shalini Verma, Jagdish C. Patni and Rajat Dubey
5.1 Brief Introduction of Computer Vision 106
5.1.1 Define Computer Vision 106
5.1.2 A Brief History 106
5.1.3 Chapter Overview 107
5.2 A Pictorial Summary of Image Formation 108
5.2.1 Image Formation 108
5.2.2 Geometric Primitives and Transformations 111
5.2.3 Photometric Image Formation 114
5.2.4 The Digital Camera 114
5.3 Sampling and Aliasing 115
5.3.1 Sampling of Pitch 116
5.3.2 Fill Factor 116
5.4 Feature Detection 116
5.4.1 Points and Patches of the Image 118
5.5 Image Segmentation 119
5.5.1 Active Contour Level Sets 120
5.6 Computational Photography 122
5.6.1 Radiometric Response Function Value 122
5.6.2 Vignetting of the View 124
5.6.3 Optical Blur (Spatial Response) Estimation 124
5.7 Recognition 125
5.7.1 High Dynamic Range Imaging 126
5.7.1.1 Tone Mapping 126
5.7.1.2 Super-Resolution and Blur Removal 126
5.7.2 Face Detection 127
5.8 Visual Tracking of the Object 128
5.9 Conclusion 129
References 130
6 Revolutionizing Car Manufacturing the Power of Intelligent Robotic Process Automation 133
Amit K. Nerurkar and G. T. Thampi
6.1 Introduction 134
6.1.1 Differences Between RPA vs IPA? 135
6.1.2 AI Enabled Robots 135
6.1.3 Artificially Intelligent Robots 135
6.1.4 Ethical Issues Involved in Integration of AI Technologies and Robotics in Assembly Line 136
6.1.5 Current State of Car Manufacturing in India 137
6.2 Literature Survey 139
6.3 Exploratory Analysis 143
6.4 The Manufacturing Process in India 146
6.5 Degree of Integration for Using Robotic Process Automation Automotive Sector 147
6.6 Complexities and Solution to Integrate AI in Current RPA 148
6.7 What Next in Indian Car Manufacturing? 150
6.8 Conclusion 150
References 151
7 Industry 5.0 and Artificial Intelligence: A Match Made in Technology Heaven? Unleashing the Potential of Artificial Intelligence in Industry 5.0 153
Bhanu Priya, Vivek Sharma and Rahul Sharma
7.1 Introduction 154
7.2 Review of Literature 155
7.2.1 Background of Industry 5.0 155
7.2.2 Definition of Industry 5.0 157
7.2.3 Artificial Intelligence and Industry 5.0 158
7.3 Research Model of How AI Works in Industry 5.0 159
7.3.1 Artificial Intelligence Tools 159
7.3.1.1 Machine Learning 160
7.3.1.2 Robotics 162
7.3.1.3 Conversational Interfaces 164
7.3.1.4 Intelligent Agents 165
7.3.1.5 Edge Computing 167
7.3.2 Integration of AI with Other Advanced Technologies 169
7.3.2.1 Digital Twins 169
7.3.2.2 6G Technology 169
7.3.2.3 Explainable Artificial Intelligence 170
7.3.2.4 Blockchain 171
7.3.2.5 Security Cover by AI 172
7.4 Smart Factories and Manufacturing Processes 173
7.4.1 Predictive Maintenance, Quality, and Supply Chain Synergy 174
7.4.1.1 Predictive Maintenance 174
7.4.1.2 Quality Control and Defect Detection 175
7.4.1.3 Supply Chain Optimization 176
7.4.2 Industrial Internet of Things (IIoT) and Data Analytics 176
7.4.2.1 Real-Time Monitoring and Analysis 177
7.4.2.2 Predictive Modeling and Forecasting 177
7.4.2.3 Asset Tracking and Management 178
7.4.3 Robotics and Automation 178
7.4.3.1 Collaborative Robots (Cobots) 178
7.4.3.2 Autonomous Vehicles and Drones 179
7.4.3.3 Human-Robot Collaboration 180
7.5 Outcomes of AI in Industry 5.0 181
7.5.1 Sustainability 181
7.5.1.1 Environmental Sustainability 182
7.5.1.2 Society 5.0 183
7.5.2 Resilience and IR 5 187
7.5.3 New Business Models 188
7.6 Challenges of Industry 5.0 189
7.7 Conclusion 190
References 191
8 A VLSI-Based Multi-Level ECG Compression Scheme with RL and VL Encoding 203
P. Balasubramani, S. Swathi Krishna and E. Udayakumar
8.1 Introduction 204
8.2 Literature Survey 204
8.3 Proposed System 205
8.4 Proposed Multi-Level ECG Compression Architecture 207
8.5 Results and Analysis 212
8.6 Conclusion 216
References 216
9 Using Reinforcement Learning in Unity Environments for Training AI-Agent 219
Geetika Munjal and Monika Lamba
9.1 Introduction 219
9.2 Literature Review 221
9.3 Machine Learning 221
9.3.1 Categorization of Machine Learning 222
9.3.1.1 Supervised Learning 222
9.3.1.2 Unsupervised Learning 222
9.3.1.3 Reinforcement Learning 223
9.3.2 Classifying on the Basis of Envisioned Output 224
9.3.2.1 Classification 224
9.3.2.2 Regression 224
9.3.2.3 Clustering 224
9.3.3 Artificial Intelligence 224
9.4 Unity 225
9.4.1 Unity Hub 225
9.4.2 Unity Editor 225
9.4.3 Inspector 225
9.4.4 Game View 225
9.4.5 Scene View 226
9.4.6 Hierarchy 226
9.4.7 Project Window 226
9.5 Reinforcement Learning and Supervised Learning 227
9.5.1 Positive Reinforcement 228
9.5.2 Negative Reinforcement 228
9.5.3 Model-Free and Model-Based RL 228
9.6 Proposed Model 230
9.6.1 Setting Up a Virtual Environment 231
9.6.2 Setting Up of the Environment 231
9.6.2.1 Creating and Allocating Scripts for the Environment 232
9.6.2.2 Creating a Goal for the Agent 232
9.6.2.3 Reward Driven Behavior 233
9.7 Markov Decision Process 234
9.8 Model Based RL 234
9.9 Experimental Results 235
9.9.1 Machine Learning Models Used for the Environments 235
9.9.2 PushBlock 236
9.9.3 Hallway 236
9.9.4 Screenshots of the PushBlock Environment 236
9.9.5 Screenshots of the Hallway Environment 242
9.10 Conclusion 245
References 245
10 A Review of Digital Transformation and Sustainable International Agricultural Businesses in Africa 249
Shadreck Matindike, Stephen Mago, Flora Modiba and Amanda Van den Berg
10.1 Introduction 249
10.1.1 Background 251
10.1.1.1 Digitalization in Agriculture and SDGs 253
10.1.1.2 International Agricultural Businesses and Sustainable Development 254
10.1.1.3 Research Questions and Objectives 255
10.1.1.4 Significance of the Study 256
10.2 Methodology 256
10.2.1 Research Strategy 256
10.2.2 Search Strategy 257
10.2.2.1 Database Identification 257
10.2.2.2 Search Strings 258
10.2.2.3 Exclusion and Inclusion Criteria 259
10.3 Findings 260
10.3.1 Literature Landscape without Filters 260
10.3.1.1 Publications Output 260
10.3.1.2 Academic Impact (Citations) 261
10.3.1.3 Major Sources of Literature on the Topic 261
10.3.1.4 Major Authors of Literature on the Topic 261
10.3.2 Literature Landscape with Filters 263
10.3.2.1 Bibliometric Analysis of Publication Output 263
10.3.2.2 Bibliometric Analysis of Keywords 263
10.3.2.3 Bibliometric Analysis of Themes of Topics 265
10.3.2.4 Bibliometric Analysis of Citations Across Countries 265
10.3.3 Digital Transformation, Sustainability and International Businesses in African Agriculture 267
10.3.3.1 Plant Monitoring 269
10.3.3.2 Phenotyping 269
10.3.3.3 Weeding 270
10.3.3.4 Seeding 271
10.3.3.5 Disease Detection 271
10.3.4 Potential of International Businesses in African Agriculture 272
10.4 Recommendations 275
10.5 Conclusion 276
References 278
11 Developing a Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context: A Case of Zimbabwe 283
Samuel Musungwini
11.1 Introduction 284
11.2 Background and Context 285
11.3 Methodology 288
11.3.1 Design Science 288
11.4 Literature Review 291
11.4.1 Current State of Disruptive Emerging Technologies in Health Care Delivery 292
11.4.2 The Current State of DETs in SSA 294
11.4.3 Healthcare Challenges Currently Prevalent in SSA Lack Proper Medical Attention 295
11.4.4 Opportunities for Implementing DETs in Health in SSA 296
11.5 Empirical Data 296
11.5.1 Potential Benefits of Implementing Disruptive Emerging Technologies in Health Care Delivery in a Developing Country like Zimbabwe 298
11.5.2 Challenges and Opportunities Associated with Harnessing these Technologies for the Benefit of Society 5.0 in Zimbabwe 300
11.6 Discussion 302
11.7 A Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context 304
11.7.1 Layer 1: Environmental Scanning and Diagnostic Analysis 305
11.7.2 Layer 2: Strategic Planning Roadmap 306
11.7.3 Layer 3: Integrate, Implement, and Operationalise D.E.TS in Select Healthcare Facilities 307
11.7.4 Layer 4: Evaluation and Review 308
11.7.5 Layer 5: Roll Out D.E.TS in All Healthcare Services and Processes 308
11.8 Conclusions and Recommendations 308
References 310
12 IT Innovation: Driving Digital Transformation 315
Sruthy S.
12.1 Introduction 316
12.2 The IT Innovation Ecosystem 318
12.3 Types of IT Innovations 320
12.4 IT Innovation Frameworks 324
12.5 Challenges and Risks of IT Innovation 325
12.6 Case Study: Uber - Disrupting the Transportation Industry with Innovative Technology 328
12.7 Future Directions of IT Innovation 335
References 339
13 Strategic Convergence of Advanced Technologies in Modern Warfare 341
Ayan Sar, Tanupriya Choudhury, Jung-Sup Um, Rahul Kumar Singh and Ketan Kotecha
13.1 Introduction 342
13.2 Quantum Computing and Cryptography 342
13.2.1 Quantum Computing for Secure Communication 342
13.2.2 Quantum Key Distribution in Military Networks 343
13.2.3 Potential Impact of Quantum Computing on Cybersecurity 344
13.3 Blockchain Technology in Military Operations 345
13.3.1 Immutable Record-Keeping and Supply Chain Management 346
13.3.2 Smart Contracts for Streamlining Military Processes 347
13.3.3 Enhanced Security and Transparent Transactions 348
13.4 Case-Studies and Real-World Applications 349
13.4.1 Autonomous Aerial Reconnaissance - Predator and Reaper Drones (U.S.A) 349
13.4.2 Blockchain in Military Supply Chain Management 350
13.4.3 AI-Driven Decision Support Systems 350
13.4.4 Aegis Combat System (U.S. Navy) 350
13.4.5 Adaptation in Response to Threats: Stuxnet Worm 351
13.5 Challenges and Risks 352
13.5.1 Ethical Dilemmas in the Use of Disruptive Technologies 352
13.5.2 Vulnerabilities and Exploits in Cyber-Physical Systems 352
13.5.3 International Cooperation and Regulations 353
13.6 Conclusion 353
References 354
Index 355