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Distributed Time-Sensitive Systems

Edited by Tanupriya Choudhury, Rahul Kumar Singh, Ravi Tomar, S. Balamurugan, J. C. Patni
Series: Industry 5.0 Transformation Applications
Copyright: 2025   |   Expected Pub Date:2025/03/30
ISBN: 9781394197729  |  Hardcover  |  
384 pages

One Line 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.

Audience
Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0

Description
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.

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Author / Editor Details
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.

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Table of Contents
Preface
Acknowledgement
1. Analytical Survey of AI Data Analysis Techniques
Divyansh Singhal, Roohi Sille, Tanupriya Choudhury, Thinagaran Perumal and Ashutosh Sharma
1.1 Introduction
1.2 Survey on Various AI Techniques in Multiple Data Inputs
1.2.1 AI Techniques in E-Commerce
1.2.1.1 Benefits of Using AI in Ecommerce Companies
1.2.1.2 AI Use Cases in E-Commerce
1.2.2 AI Techniques in Healthcare
1.2.2.1 Machine Learning
1.2.2.2 Natural Language Processing (NLP)
1.2.2.3 Rule Based Expert Systems
1.2.2.4 Physical Robots
1.2.2.5 Robotic Process Automation
1.2.2.6 Administrative Applications
1.2.2.7 AR/VR
1.2.2.8 Ways on How AI Will Create an Impact in Healthcare Industry
1.3 Conclusion
References
2. Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions
Rimjhim Gupta, Roohi Sille and Tanupriya Choudhury
2.1 Introduction
2.2 Literature Review
2.2.1 ARIMA (Auto Regressive Integrated Moving Average)
2.2.2 Linear Regression
2.2.3 KNN (K-Nearest Neighbor)
2.2.4 Decision Tree
2.2.5 Random Forest
2.2.6 Support Vector Regression
2.2.7 Support Vector Machine
2.2.8 Long Short-Term Memory Network Model
2.2.9 Extreme Gradient Boosting (XGBoost)
2.3 Applications of Machine Learning (ML) and Deep Learning (DL) Model
2.4 Conclusions and Future Perspective
References
3. Implementation of High Speed Adders for Image Blending Applications
P. Vanjipriya, K. N. Vijeyakumar, E. Udayakumar and S. Vishnushree
3.1 Introduction
3.2 Area and Delay Analysis of Addition Algorithm
3.2.1 Carry Select Addition
3.2.2 Carry Lookahead Addition
3.2.3 Kogge Stone Addition
3.3 Design of High Speed Adder
3.3.1 Carry Select Adder
3.3.2 Carry Lookahead Adder
3.3.3 Kogge Stone Adder
3.4 Results and Discussion
3.4.1 ASIC Implementation Results
3.5 FPGA Implementation in Digital Image Processing
3.5.1 Image Blending
3.6 Conclusion
References
4. Smart Factories and Energy Efficiency in Industry 4.0
S.C. Vetrivel, T.P. Saravanan and R. Maheswari
4.1 Introduction
4.1.1 Background of Industry [4.0] and Its Impact on Manufacturing
4.1.2 Importance of Energy Efficiency in Smart Factories
4.1.3 Objectives and Scope of the Paper
4.1.3.1 Objectives
4.1.3.2 Scope
4.2 Industry 4.0: Concepts and Technologies
4.2.1 Overview of Industry 4.0 and its Key Principles
4.2.2 Smart Factories and their Role in Industry 4.0
4.2.3 Technologies Enabling Smart Factories (e.g., IoT, Bigdata, AI)
4.3 Energy Efficiency in Manufacturing
4.3.1 Significance of Energy Efficiency in the Manufacturing Sector
4.3.2 Opportunities and Obstacles to Enhancing Energy Efficiency
4.3.3 Benefits of Energy-Efficient Practices in Smart Factories
4.4 Integration of Energy Management Systems in Smart Factories
4.4.1 Introduction to Energy Management Systems (EMS)
4.4.1.1 Key Components of an Energy Management System
4.4.1.2 Benefits of Energy Management Systems
4.4.2 Role of EMS in Achieving Energy Efficiency in Smart Factories
4.4.3 Key Components and Functionalities of EMS in Industry 4.0
4.5 Energy Monitoring and Optimization in Smart Factories
4.5.1 Importance of Real-Time Energy Monitoring in Smart Factories
4.5.2 Sensor Technologies and Data Collection for Energy Monitoring
4.5.3 Optimization Techniques for Energy Consumption in Manufacturing Processes
4.6 Intelligent Control Systems for Energy Efficiency
4.6.1 Application of AI & AL in Energy Management
4.6.2 Intelligent Control Systems for Optimizing Energy Usage
4.6.3 Case Studies Showcasing the Effectiveness of Intelligent Control Systems
4.7 Energy Storage and Renewable Energy Integration
4.7.1 Utilization of Energy Storage Systems in Smart Factories
4.7.2 Integration of Renewable Energy Sources in Manufacturing Processes
4.7.3 Benefits and Challenges of Incorporating Energy Storage and Renewable
4.7.3.1 Benefits of Incorporating Energy Storage and Renewables
4.7.3.2 Challenges of Incorporating Energy Storage and Renewables
4.8 Smart Grid Integration and Demand Response
4.8.1 Smart Grids’ Contribution to Smart Industries’ Increased Energy Efficiency
4.8.2 Demand Response Strategies for Managing Energy Consumption
4.8.3 Synergies Between Smart Factories and Smart Grids
4.9 Case Studies and Best Practices
4.9.1 Case Studies Highlighting Successful Implementation of Energy Efficiency Measures in Smart Factories
4.9.2 Best Practices for Achieving Energy Efficiency in Industry 4.0 in Indian Scenario
4.10 Challenges and Future Directions
4.10.1 Challenges and Barriers to Implementing Energy Efficiency in Smart Factories
4.10.2 Emerging Trends and Future Directions in Smart Factories and Energy Efficiency
4.10.3 Policy Implications and Recommendations for Industry Stakeholders
4.11 Conclusion
References
5. AI in Computer Vision with Emerging Techniques and Their Scope
Pawan K. Mishra, Shalini Verma, Jagdish C. Patni and Rajat Dubey
5.1 Brief Introduction of Computer Vision
5.1.1 Define Computer Vision
5.1.2 A Brief History
5.1.3 Chapter Overview
5.2 A Pictorial Summary of Image Formation
5.2.1 Image Formation
5.2.2 Geometric Primitives and Transformations
5.2.3 Photometric Image Formation
5.2.4 The Digital Camera
5.3 Sampling and Aliasing
5.3.1 Sampling of Pitch
5.3.2 Fill Factor
5.4 Feature Detection
5.4.1 Points and Patches of the Image
5.5 Image Segmentation
5.5.1 Active Contour Level Sets
5.6 Computational Photography
5.6.1 Radiometric Response Function Value
5.6.2 Vignetting of the View
5.6.3 Optical Blur (Spatial Response) Estimation
5.7 Recognition
5.7.1 High Dynamic Range Imaging
5.7.1.1 Tone Mapping
5.7.1.2 Super-Resolution and Blur Removal
5.7.2 Face Detection
5.8 Visual Tracking of the Object
5.9 Conclusion
References
6. Revolutionizing Car Manufacturing the Power of Intelligent Robotic Process Automation
Amit K. Nerurkar and G. T. Thampi
6.1 Introduction
6.1.1 Differences Between RPA vs IPA?
6.1.2 AI Enabled Robots
6.1.3 Artificially Intelligent Robots
6.1.4 Ethical Issues Involved in Integration of AI Technologies and Robotics in Assembly Line
6.1.5 Current State of Car Manufacturing in India
6.2 Literature Survey
6.3 Exploratory Analysis
6.4 The Manufacturing Process in India
6.5 Degree of Integration for Using Robotic Process Automation Automotive Sector
6.6 Complexities and Solution to Integrate AI in Current RPA
6.7 What Next in Indian Car Manufacturing?
6.8 Conclusion
References
7. Industry 5.0 and Artificial Intelligence: A Match Made in Technology Heaven? Unleashing the Potential of Artificial Intelligence in Industry 5.0
Bhanu Priya, Vivek Sharma and Rahul Sharma
7.1 Introduction
7.2 Review of Literature
7.2.1 Background of Industry 5.0
7.2.2 Definition of Industry 5.0
7.2.3 Artificial Intelligence and Industry 5.0
7.3 Research Model of How AI Works in Industry 5.0
7.3.1 Artificial Intelligence Tools
7.3.1.1 Machine Learning
7.3.1.2 Robotics
7.3.1.3 Conversational Interfaces
7.3.1.4 Intelligent Agents
7.3.1.5 Edge Computing
7.3.2 Integration of AI with Other Advanced Technologies
7.3.2.1 Digital Twins
7.3.2.2 6G Technology
7.3.2.3 Explainable Artificial Intelligence
7.3.2.4 Blockchain
7.3.2.5 Security Cover by AI
7.4 Smart Factories and Manufacturing Processes
7.4.1 Predictive Maintenance, Quality, and Supply Chain Synergy
7.4.1.1 Predictive Maintenance
7.4.1.2 Quality Control and Defect Detection
7.4.1.3 Supply Chain Optimization
7.4.2 Industrial Internet of Things (IIoT) and Data Analytics
7.4.2.1 Real-Time Monitoring and Analysis
7.4.2.2 Predictive Modeling and Forecasting
7.4.2.3 Asset Tracking and Management
7.4.3 Robotics and Automation
7.4.3.1 Collaborative Robots (Cobots)
7.4.3.2 Autonomous Vehicles and Drones
7.4.3.3 Human-Robot Collaboration
7.5 Outcomes of AI in Industry 5.0
7.5.1 Sustainability
7.5.1.1 Environmental Sustainability
7.5.1.2 Society 5.0
7.5.2 Resilience and IR5
7.5.3 New Business Models
7.6 Challenges of Industry 5.0
7.7 Conclusion
References
8. A VLSI-Based Multi-Level ECG Compression Scheme with RL and VL Encoding
P. Balasubramani, S. Swathi Krishna and E. Udayakumar
8.1 Introduction
8.2 Literature Survey
8.3 Proposed System
8.4 Proposed Multi-Level ECG Compression Architecture
8.5 Results and Analysis
8.6 Conclusion
References
9. Using Reinforcement Learning in Unity Environments for Training AI-Agent
Geetika Munjal and Monika Lamba
9.1 Introduction
9.2 Literature Review
9.3 Machine Learning
9.3.1 Categorization of Machine Learning
9.3.1.1 Supervised Learning
9.3.1.2 Unsupervised Learning
9.3.1.3 Reinforcement Learning
9.3.2 Classifying on the Basis of Envisioned Output
9.3.2.1 Classification
9.3.2.2 Regression
9.3.2.3 Clustering
9.3.3 Artificial Intelligence
9.4 Unity
9.4.1 Unity Hub
9.4.2 Unity Editor
9.4.3 Inspector
9.4.4 Game View
9.4.5 Scene View
9.4.6 Hierarchy
9.4.7 Project Window
9.5 Reinforcement Learning and Supervised Learning
9.5.1 Positive Reinforcement
9.5.2 Negative Reinforcement
9.5.3 Model-Free and Model-Based RL
9.6 Proposed Model
9.6.1 Setting Up a Virtual Environment
9.6.2 Setting Up of the Environment
9.6.2.1 Creating and Allocating Scripts for the Environment
9.6.2.2 Creating a Goal for the Agent
9.6.2.3 Reward Driven Behavior
9.7 Markov Decision Process
9.8 Model Based RL
9.9 Experimental Results
9.9.1 Machine Learning Models Used for the Environments
9.9.2 PushBlock
9.9.3 Hallway
9.9.4 Screenshots of the PushBlock Environment
9.9.5 Screenshots of the Hallway Environment
9.10 Conclusion
References
10. A Review of Digital Transformation and Sustainable International Agricultural Businesses in Africa
Shadreck Matindike, Stephen Mago, Flora Modiba and Amanda Van den Berg
10.1 Introduction
10.1.1 Background
10.1.1.1 Digitalization in Agriculture and SDGs
10.1.1.2 International Agricultural Businesses and Sustainable Development
10.1.1.3 Research Questions and Objectives
10.1.1.4 Significance of the Study
10.2 Methodology
10.2.1 Research Strategy
10.2.2 Search Strategy
10.2.2.1 Database Identification
10.2.2.2 Search Strings
10.2.2.3 Exclusion and Inclusion Criteria
10.3 Findings
10.3.1 Literature Landscape without Filters
10.3.1.1 Publications Output
10.3.1.2 Academic Impact (Citations)
10.3.1.3 Major Sources of Literature on the Topic
10.3.1.4 Major Authors of Literature on the Topic
10.3.2 Literature Landscape with Filters
10.3.2.1 Bibliometric Analysis of Publication Output
10.3.2.2 Bibliometric Analysis of Keywords
10.3.2.3 Bibliometric Analysis of Themes of Topics
10.3.2.4 Bibliometric Analysis of Citations Across Countries
10.3.3 Digital Transformation, Sustainability and International Businesses in African Agriculture
10.3.3.1 Plant Monitoring
10.3.3.2 Phenotyping
10.3.3.3 Weeding
10.3.3.4 Seeding
10.3.3.5 Disease Detection
10.3.4 Potential of International Businesses in African Agriculture
10.4 Recommendations
10.5 Conclusion
References
11. Developing a Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context: A Case of Zimbabwe
Samuel Musungwini
11.1 Introduction
11.2 Background and Context
11.3 Methodology
11.3.1 Design Science
11.4 Literature Review
11.4.1 Current State of Disruptive Emerging Technologies in Health Care Delivery
11.4.2 The Current State of DETs in SSA
11.4.3 Healthcare Challenges Currently Prevalent in SSA Lack Proper Medical Attention
11.4.4 Opportunities for Implementing DETs in Health in SSA
11.5 Empirical Data
11.5.1 Potential Benefits of Implementing Disruptive Emerging Technologies in Health Care Delivery in a Developing Country like Zimbabwe
11.5.2 Challenges and Opportunities Associated with Harnessing these Technologies for the Benefit of Society 5.0 in Zimbabwe
11.6 Discussion
11.7 A Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context
11.7.1 Layer 1: Environmental Scanning and Diagnostic Analysis
11.7.2 Layer 2: Strategic Planning Roadmap
11.7.3 Layer 3: Integrate, Implement, and Operationalise D.E.TS in Select Healthcare Facilities
11.7.4 Layer 4: Evaluation and Review
11.7.5 Layer 5: Roll Out D.E.TS in All Healthcare Services and Processes
11.8 Conclusions and Recommendations
References
12. IT Innovation: Driving Digital Transformation
Sruthy S.
12.1 Introduction
12.2 The IT Innovation Ecosystem
12.3 Types of IT Innovations
12.4 IT Innovation Frameworks
12.5 Challenges and Risks of IT Innovation
12.6 Case Study: Uber - Disrupting the Transportation Industry with Innovative Technology
12.7 Future Directions of IT Innovation
References
13. Strategic Convergence of Advanced Technologies in Modern Warfare
Ayan Sar, Tanupriya Choudhury, Jung-Sup Um, Rahul Kumar Singh and Ketan Kotecha
13.1 Introduction
13.2 Quantum Computing and Cryptography
13.2.1 Quantum Computing for Secure Communication
13.2.2 Quantum Key Distribution in Military Networks
13.2.3 Potential Impact of Quantum Computing on Cybersecurity
13.3 Blockchain Technology in Military Operations
13.3.1 Immutable Record-Keeping and Supply Chain Management
13.3.2 Smart Contracts for Streamlining Military Processes
13.3.3 Enhanced Security and Transparent Transactions
13.4 Case-Studies and Real-World Applications
13.4.1 Autonomous Aerial Reconnaissance - Predator and Reaper Drones (U.S.A)
13.4.2 Blockchain in Military Supply Chain Management
13.4.3 AI-Driven Decision Support Systems
13.4.4 Aegis Combat System (U.S. Navy)
13.4.5 Adaptation in Response to Threats: Stuxnet Worm
13.5 Challenges and Risks
13.5.1 Ethical Dilemmas in the Use of Disruptive Technologies
13.5.2 Vulnerabilities and Exploits in Cyber-Physical Systems
13.5.3 International Cooperation and Regulations
13.6 Conclusion
References
Index

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