Distributed Time-Sensitive Systems

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.

• Preface xixAcknowledgement xxiii1 Analytical Survey of AI Data Analysis Techniques 1Divyansh Singhal, Roohi Sille, Tanupriya Choudhury, Thinagaran Perumal and Ashutosh Sharma1.1 Introduction 21.2 Survey on Various AI Techniques in Multiple Data Inputs 21.2.1 AI Techniques in E-Commerce 21.2.1.1 Benefits of Using AI in Ecommerce Companies 41.2.1.2 AI Use Cases in E-Commerce 51.2.2 AI Techniques in Healthcare 101.2.2.1 Machine Learning 111.2.2.2 Natural Language Processing (NLP) 131.2.2.3 Rule Based Expert Systems 141.2.2.4 Physical Robots 141.2.2.5 Robotic Process Automation 151.2.2.6 Administrative Applications 151.2.2.7 AR/VR 161.2.2.8 Ways on How AI Will Create an Impact in Healthcare Industry 181.3 Conclusion 20References 222 Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions 25Rimjhim Gupta, Roohi Sille and Tanupriya Choudhury2.1 Introduction 262.2 Literature Review 282.2.1 ARIMA (Auto Regressive Integrated Moving Average) 292.2.2 Linear Regression 292.2.3 KNN (K-Nearest Neighbor) 292.2.4 Decision Tree 302.2.5 Random Forest 312.2.6 Support Vector Regression 312.2.7 Support Vector Machine 322.2.8 Long Short-Term Memory Network Model 322.2.9 Extreme Gradient Boosting (XGBoost) 332.3 Applications of Machine Learning (ML) and Deep Learning (DL) Model 352.4 Conclusions and Future Perspective 36References 373 Implementation of High Speed Adders for Image Blending Applications 43P. Vanjipriya, K. N. Vijeyakumar, E. Udayakumar and S. Vishnushree3.1 Introduction 433.2 Area and Delay Analysis of Addition Algorithm 453.2.1 Carry Select Addition 453.2.2 Carry Lookahead Addition 453.2.3 Kogge Stone Addition 463.3 Design of High Speed Adder 483.3.1 Carry Select Adder 493.3.2 Carry Lookahead Adder 493.3.3 Kogge Stone Adder 513.4 Results and Discussion 533.4.1 ASIC Implementation Results 533.5 FPGA Implementation in Digital Image Processing 583.5.1 Image Blending 583.6 Conclusion 61References 614 Smart Factories and Energy Efficiency in Industry 4.0 63S.C. Vetrivel, T.P. Saravanan and R. Maheswari4.1 Introduction 644.1.1 Background of Industry [4.0] and Its Impact on Manufacturing 644.1.2 Importance of Energy Efficiency in Smart Factories 654.1.3 Objectives and Scope of the Paper 664.1.3.1 Objectives 664.1.3.2 Scope 664.2 Industry 4.0: Concepts and Technologies 674.2.1 Overview of Industry 4.0 and its Key Principles 674.2.2 Smart Factories and their Role in Industry 4.0 694.2.3 Technologies Enabling Smart Factories (e.g., IoT, Bigdata, AI) 694.3 Energy Efficiency in Manufacturing 714.3.1 Significance of Energy Efficiency in the Manufacturing Sector 714.3.2 Opportunities and Obstacles to Enhancing Energy Efficiency 734.3.3 Benefits of Energy-Efficient Practices in Smart Factories 754.4 Integration of Energy Management Systems in Smart Factories 764.4.1 Introduction to Energy Management Systems (EMS) 764.4.1.1 Key Components of an Energy Management System 774.4.1.2 Benefits of Energy Management Systems 774.4.2 Role of EMS in Achieving Energy Efficiency in Smart Factories 784.4.3 Key Components and Functionalities of EMS in Industry 4.0 804.5 Energy Monitoring and Optimization in Smart Factories 824.5.1 Importance of Real-Time Energy Monitoring in Smart Factories 824.5.2 Sensor Technologies and Data Collection for Energy Monitoring 824.5.3 Optimization Techniques for Energy Consumption in Manufacturing Processes 834.6 Intelligent Control Systems for Energy Efficiency 854.6.1 Application of AI & AL in Energy Management 854.6.2 Intelligent Control Systems for Optimizing Energy Usage 864.6.3 Case Studies Showcasing the Effectiveness of Intelligent Control Systems 874.7 Energy Storage and Renewable Energy Integration 884.7.1 Utilization of Energy Storage Systems in Smart Factories 884.7.2 Integration of Renewable Energy Sources in Manufacturing Processes 884.7.3 Benefits and Challenges of Incorporating Energy Storage and Renewable 894.7.3.1 Benefits of Incorporating Energy Storage and Renewables 894.7.3.2 Challenges of Incorporating Energy Storage and Renewables 904.8 Smart Grid Integration and Demand Response 914.8.1 Smart Grids’ Contribution to Smart Industries’ Increased Energy Efficiency 914.8.2 Demand Response Strategies for Managing Energy Consumption 934.8.3 Synergies Between Smart Factories and Smart Grids 944.9 Case Studies and Best Practices 954.9.1 Case Studies Highlighting Successful Implementation of Energy Efficiency Measures in Smart Factories 954.9.2 Best Practices for Achieving Energy Efficiency in Industry 4.0 in Indian Scenario 964.10 Challenges and Future Directions 984.10.1 Challenges and Barriers to Implementing Energy Efficiency in Smart Factories 984.10.2 Emerging Trends and Future Directions in Smart Factories and Energy Efficiency 994.10.3 Policy Implications and Recommendations for Industry Stakeholders 1004.11 Conclusion 101References 1025 AI in Computer Vision with Emerging Techniques and Their Scope 105Pawan K. Mishra, Shalini Verma, Jagdish C. Patni and Rajat Dubey5.1 Brief Introduction of Computer Vision 1065.1.1 Define Computer Vision 1065.1.2 A Brief History 1065.1.3 Chapter Overview 1075.2 A Pictorial Summary of Image Formation 1085.2.1 Image Formation 1085.2.2 Geometric Primitives and Transformations 1115.2.3 Photometric Image Formation 1145.2.4 The Digital Camera 1145.3 Sampling and Aliasing 1155.3.1 Sampling of Pitch 1165.3.2 Fill Factor 1165.4 Feature Detection 1165.4.1 Points and Patches of the Image 1185.5 Image Segmentation 1195.5.1 Active Contour Level Sets 1205.6 Computational Photography 1225.6.1 Radiometric Response Function Value 1225.6.2 Vignetting of the View 1245.6.3 Optical Blur (Spatial Response) Estimation 1245.7 Recognition 1255.7.1 High Dynamic Range Imaging 1265.7.1.1 Tone Mapping 1265.7.1.2 Super-Resolution and Blur Removal 1265.7.2 Face Detection 1275.8 Visual Tracking of the Object 1285.9 Conclusion 129References 1306 Revolutionizing Car Manufacturing the Power of Intelligent Robotic Process Automation 133Amit K. Nerurkar and G. T. Thampi6.1 Introduction 1346.1.1 Differences Between RPA vs IPA? 1356.1.2 AI Enabled Robots 1356.1.3 Artificially Intelligent Robots 1356.1.4 Ethical Issues Involved in Integration of AI Technologies and Robotics in Assembly Line 1366.1.5 Current State of Car Manufacturing in India 1376.2 Literature Survey 1396.3 Exploratory Analysis 1436.4 The Manufacturing Process in India 1466.5 Degree of Integration for Using Robotic Process Automation Automotive Sector 1476.6 Complexities and Solution to Integrate AI in Current RPA 1486.7 What Next in Indian Car Manufacturing? 1506.8 Conclusion 150References 1517 Industry 5.0 and Artificial Intelligence: A Match Made in Technology Heaven? Unleashing the Potential of Artificial Intelligence in Industry 5.0 153Bhanu Priya, Vivek Sharma and Rahul Sharma7.1 Introduction 1547.2 Review of Literature 1557.2.1 Background of Industry 5.0 1557.2.2 Definition of Industry 5.0 1577.2.3 Artificial Intelligence and Industry 5.0 1587.3 Research Model of How AI Works in Industry 5.0 1597.3.1 Artificial Intelligence Tools 1597.3.1.1 Machine Learning 1607.3.1.2 Robotics 1627.3.1.3 Conversational Interfaces 1647.3.1.4 Intelligent Agents 1657.3.1.5 Edge Computing 1677.3.2 Integration of AI with Other Advanced Technologies 1697.3.2.1 Digital Twins 1697.3.2.2 6G Technology 1697.3.2.3 Explainable Artificial Intelligence 1707.3.2.4 Blockchain 1717.3.2.5 Security Cover by AI 1727.4 Smart Factories and Manufacturing Processes 1737.4.1 Predictive Maintenance, Quality, and Supply Chain Synergy 1747.4.1.1 Predictive Maintenance 1747.4.1.2 Quality Control and Defect Detection 1757.4.1.3 Supply Chain Optimization 1767.4.2 Industrial Internet of Things (IIoT) and Data Analytics 1767.4.2.1 Real-Time Monitoring and Analysis 1777.4.2.2 Predictive Modeling and Forecasting 1777.4.2.3 Asset Tracking and Management 1787.4.3 Robotics and Automation 1787.4.3.1 Collaborative Robots (Cobots) 1787.4.3.2 Autonomous Vehicles and Drones 1797.4.3.3 Human-Robot Collaboration 1807.5 Outcomes of AI in Industry 5.0 1817.5.1 Sustainability 1817.5.1.1 Environmental Sustainability 1827.5.1.2 Society 5.0 1837.5.2 Resilience and IR 5 1877.5.3 New Business Models 1887.6 Challenges of Industry 5.0 1897.7 Conclusion 190References 1918 A VLSI-Based Multi-Level ECG Compression Scheme with RL and VL Encoding 203P. Balasubramani, S. Swathi Krishna and E. Udayakumar8.1 Introduction 2048.2 Literature Survey 2048.3 Proposed System 2058.4 Proposed Multi-Level ECG Compression Architecture 2078.5 Results and Analysis 2128.6 Conclusion 216References 2169 Using Reinforcement Learning in Unity Environments for Training AI-Agent 219Geetika Munjal and Monika Lamba9.1 Introduction 2199.2 Literature Review 2219.3 Machine Learning 2219.3.1 Categorization of Machine Learning 2229.3.1.1 Supervised Learning 2229.3.1.2 Unsupervised Learning 2229.3.1.3 Reinforcement Learning 2239.3.2 Classifying on the Basis of Envisioned Output 2249.3.2.1 Classification 2249.3.2.2 Regression 2249.3.2.3 Clustering 2249.3.3 Artificial Intelligence 2249.4 Unity 2259.4.1 Unity Hub 2259.4.2 Unity Editor 2259.4.3 Inspector 2259.4.4 Game View 2259.4.5 Scene View 2269.4.6 Hierarchy 2269.4.7 Project Window 2269.5 Reinforcement Learning and Supervised Learning 2279.5.1 Positive Reinforcement 2289.5.2 Negative Reinforcement 2289.5.3 Model-Free and Model-Based RL 2289.6 Proposed Model 2309.6.1 Setting Up a Virtual Environment 2319.6.2 Setting Up of the Environment 2319.6.2.1 Creating and Allocating Scripts for the Environment 2329.6.2.2 Creating a Goal for the Agent 2329.6.2.3 Reward Driven Behavior 2339.7 Markov Decision Process 2349.8 Model Based RL 2349.9 Experimental Results 2359.9.1 Machine Learning Models Used for the Environments 2359.9.2 PushBlock 2369.9.3 Hallway 2369.9.4 Screenshots of the PushBlock Environment 2369.9.5 Screenshots of the Hallway Environment 2429.10 Conclusion 245References 24510 A Review of Digital Transformation and Sustainable International Agricultural Businesses in Africa 249Shadreck Matindike, Stephen Mago, Flora Modiba and Amanda Van den Berg10.1 Introduction 24910.1.1 Background 25110.1.1.1 Digitalization in Agriculture and SDGs 25310.1.1.2 International Agricultural Businesses and Sustainable Development 25410.1.1.3 Research Questions and Objectives 25510.1.1.4 Significance of the Study 25610.2 Methodology 25610.2.1 Research Strategy 25610.2.2 Search Strategy 25710.2.2.1 Database Identification 25710.2.2.2 Search Strings 25810.2.2.3 Exclusion and Inclusion Criteria 25910.3 Findings 26010.3.1 Literature Landscape without Filters 26010.3.1.1 Publications Output 26010.3.1.2 Academic Impact (Citations) 26110.3.1.3 Major Sources of Literature on the Topic 26110.3.1.4 Major Authors of Literature on the Topic 26110.3.2 Literature Landscape with Filters 26310.3.2.1 Bibliometric Analysis of Publication Output 26310.3.2.2 Bibliometric Analysis of Keywords 26310.3.2.3 Bibliometric Analysis of Themes of Topics 26510.3.2.4 Bibliometric Analysis of Citations Across Countries 26510.3.3 Digital Transformation, Sustainability and International Businesses in African Agriculture 26710.3.3.1 Plant Monitoring 26910.3.3.2 Phenotyping 26910.3.3.3 Weeding 27010.3.3.4 Seeding 27110.3.3.5 Disease Detection 27110.3.4 Potential of International Businesses in African Agriculture 27210.4 Recommendations 27510.5 Conclusion 276References 27811 Developing a Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context: A Case of Zimbabwe 283Samuel Musungwini11.1 Introduction 28411.2 Background and Context 28511.3 Methodology 28811.3.1 Design Science 28811.4 Literature Review 29111.4.1 Current State of Disruptive Emerging Technologies in Health Care Delivery 29211.4.2 The Current State of DETs in SSA 29411.4.3 Healthcare Challenges Currently Prevalent in SSA Lack Proper Medical Attention 29511.4.4 Opportunities for Implementing DETs in Health in SSA 29611.5 Empirical Data 29611.5.1 Potential Benefits of Implementing Disruptive Emerging Technologies in Health Care Delivery in a Developing Country like Zimbabwe 29811.5.2 Challenges and Opportunities Associated with Harnessing these Technologies for the Benefit of Society 5.0 in Zimbabwe 30011.6 Discussion 30211.7 A Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context 30411.7.1 Layer 1: Environmental Scanning and Diagnostic Analysis 30511.7.2 Layer 2: Strategic Planning Roadmap 30611.7.3 Layer 3: Integrate, Implement, and Operationalise D.E.TS in Select Healthcare Facilities 30711.7.4 Layer 4: Evaluation and Review 30811.7.5 Layer 5: Roll Out D.E.TS in All Healthcare Services and Processes 30811.8 Conclusions and Recommendations 308References 31012 IT Innovation: Driving Digital Transformation 315Sruthy S.12.1 Introduction 31612.2 The IT Innovation Ecosystem 31812.3 Types of IT Innovations 32012.4 IT Innovation Frameworks 32412.5 Challenges and Risks of IT Innovation 32512.6 Case Study: Uber - Disrupting the Transportation Industry with Innovative Technology 32812.7 Future Directions of IT Innovation 335References 33913 Strategic Convergence of Advanced Technologies in Modern Warfare 341Ayan Sar, Tanupriya Choudhury, Jung-Sup Um, Rahul Kumar Singh and Ketan Kotecha13.1 Introduction 34213.2 Quantum Computing and Cryptography 34213.2.1 Quantum Computing for Secure Communication 34213.2.2 Quantum Key Distribution in Military Networks 34313.2.3 Potential Impact of Quantum Computing on Cybersecurity 34413.3 Blockchain Technology in Military Operations 34513.3.1 Immutable Record-Keeping and Supply Chain Management 34613.3.2 Smart Contracts for Streamlining Military Processes 34713.3.3 Enhanced Security and Transparent Transactions 34813.4 Case-Studies and Real-World Applications 34913.4.1 Autonomous Aerial Reconnaissance - Predator and Reaper Drones (U.S.A) 34913.4.2 Blockchain in Military Supply Chain Management 35013.4.3 AI-Driven Decision Support Systems 35013.4.4 Aegis Combat System (U.S. Navy) 35013.4.5 Adaptation in Response to Threats: Stuxnet Worm 35113.5 Challenges and Risks 35213.5.1 Ethical Dilemmas in the Use of Disruptive Technologies 35213.5.2 Vulnerabilities and Exploits in Cyber-Physical Systems 35213.5.3 International Cooperation and Regulations 35313.6 Conclusion 353References 354Index 355