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Submit a ProposalSmart Factories for Industry 5.0 Transformation
 | Edited by R. Nidhya, Manish Kumar, S. Karthik, Rishabh Anand and S. Balamurugan Series: Industry 5.0 Transformation Applications Copyright: 2025 | Status: Published ISBN: 9781394199952 | Hardcover | 356 pages Price: $$225 USD  |
One Line Description
This book serves as a comprehensive guide, exploring the technologies, design principles, and operational strategies behind smart factories.
Audience
Researchers, industry engineers, and technologists working in artificial intelligence and Industry 5.0 application areas such as healthcare, transportation, manufacturing, and more.
Researchers, industry engineers, and technologists working in artificial intelligence and Industry 5.0 application areas such as healthcare, transportation, manufacturing, and more.
Description
In an era where industrial expertise meets digital innovation, the “smart factory” symbolizes a new wave of efficiency and advancement. Industry 5.0 represents a paradigm shift, integrating technologies like robotics, AI, IoT, and big data to enhance human-machine collaboration while improving sustainability, quality, and efficiency. It offers businesses valuable insights and real-world examples to navigate the opportunities and challenges of Industry 5.0.
This book goes beyond technical explanations to examine the broader impact of the Industry 5.0 revolution on global supply chains and socioeconomic change, encouraging readers to view technology as a force for good. It appeals to all levels of expertise, providing valuable insights for experienced professionals while serving as an introduction for newcomers. Above all, it invites readers to embrace the collaborative spirit and creativity of Industry 5.0, joining in the effort to build the smart factories that will drive the future of innovation.
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In an era where industrial expertise meets digital innovation, the “smart factory” symbolizes a new wave of efficiency and advancement. Industry 5.0 represents a paradigm shift, integrating technologies like robotics, AI, IoT, and big data to enhance human-machine collaboration while improving sustainability, quality, and efficiency. It offers businesses valuable insights and real-world examples to navigate the opportunities and challenges of Industry 5.0.
This book goes beyond technical explanations to examine the broader impact of the Industry 5.0 revolution on global supply chains and socioeconomic change, encouraging readers to view technology as a force for good. It appeals to all levels of expertise, providing valuable insights for experienced professionals while serving as an introduction for newcomers. Above all, it invites readers to embrace the collaborative spirit and creativity of Industry 5.0, joining in the effort to build the smart factories that will drive the future of innovation.
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Author / Editor Details
R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering at the Madanapalle Institute of Technology & Science, affiliated with Jawaharlal Nehru Technical University, Anantapuram, India. Her research interests include wireless body area networks, machine learning, and IoT.
R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering at the Madanapalle Institute of Technology & Science, affiliated with Jawaharlal Nehru Technical University, Anantapuram, India. Her research interests include wireless body area networks, machine learning, and IoT.
Manish Kumar, PhD, is an assistant professor in the Department of Computer Science & Engineering at the Thapar Institute of Engineering and Technology, Patiala, Punjab, India. His research interests include soft computing applications for bioinformatics problems and computational intelligence.
S. Karthik, PhD, is a professor and dean in the Department of Computer Science & Engineering at SNS College of Technology, Coimbatore, Tamil Nadu, India. His research interests include network security, web services, and wireless systems.
Rishabh Anand, PhD, is a Global Service Delivery Manager with HCL Technologies Ltd. He earned his MBA in 2020 and is a certified DevOps project manager.
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 50+ books, 200+ international journals/ conferences, and 35 patents.
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Table of Contents
Preface
1. Evolution of Industrial Revolution: Industry 5.0 and Beyond
S. Balamurugan and B. Surya
Brief History of Industrial Revolution
Acknowledgement
Bibliography and Further Reading
2. Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept
S. Pradeep, R. Sathish Kumar, M. Jagadesh and A. Karthikeyan
Nomenclature
2.1 Introduction
2.2 Literature Survey
2.3 Digitization, Data Sources, and AI in Healthcare
2.4 AI Mainstreaming in Healthcare
2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation
2.6 Prerequisites for Radical Transformation in Healthcare
2.7 Personalized Healthcare Transformation Using MSOM-Based TOA
2.8 Results
2.9 Conclusion
References
3. A Survey on Security in Data Transmission Using Wireless Communication Methods for IoT Edge Devices
V. Maruthi Prasad and B. Bharathi
3.1 Introduction
3.2 Literature Survey
3.3 Description of Data Protocols for IoT System
3.4 IoT Communication Parameters
3.5 Comparative of Communication Protocols for IoT Systems
3.6 Conclusion
References
4. Innovative Application of Conditional Deep Convolutional Generative Adversarial Networks to Enhance Chronic Kidney Disease Diagnosis with Uneven Datasets
Lakshmi Ramani Burra, Praveen Tumuluru, Janakiramaiah Bonam, S. Hrushikesava Raju, Sunanda Nalajala and Surya Prasada Rao Borra
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.3.1 Data Preprocessing
4.3.2 Conditional Deep Convolutional Generative Adversarial Network
4.3.3 Bidirectional Long-Term Memory (Bi-LSTM) Method
4.4 Result Analysis
4.5 Conclusion
References
5. A Comprehensive Hybrid Implicit and Explicit Item-Based Collaborative Filtering Approach with Bayesian Personalized Ranking for Enhancing Book Recommendations
Adidam Surekha, Radhika Gouni, Satya Keerthi Gorripati, Venubabu Rachapudi, S. Anjali Devi and Anupama Angadi
5.1 Introduction
5.2 Related Work
5.3 Methodology
5.4 Experimental Results and Analysis
5.5 Conclusion
References
6. An Efficient Cluster-Based Deep Learning Model for Multi-Attack Classification in IDS Across Diverse Datasets
Rajesh Bingu, G. Harsha Vardhan Reddy, U. Jyothi Naga Pavan, S. Sneha Sai Sri
and N. V. Praveen Kumar
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Model Design
6.4 Results and Discussion
6.5 Conclusion
References
7. Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification
G. Kiran Kumar, Anila M., Naga Raju Hari Manikyam, Venkata Nagaraju Thatha, R. Vijaya Kumar Reddy and Krishna Reddy Papana
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Methodology
7.4 Results and Discussion
7.5 Conclusion
References
8. Optimal Power Allocation in Cognitive Radio Networks Using Teaching-Learning-Based Optimization
N. Lakshman Pratap, N. Sunanda and V. Suryanarayana Reddy
8.1 Introduction
8.2 Teaching-Learning-Based Optimization
8.2.1 Teacher Phase
8.2.2 Learner Phase
8.3 Proposed Power Allocation Algorithm
8.4 Numerical Results
8.5 Conclusion
References
9. Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market
K. Sri Niharika, C.H. Srisai Naga Satya Mani Pavan, T. Baby Aparna, Dinesh Kumar Anguraj, S. Saathvik and Hari Kiran Vege
9.1 Introduction
9.1.1 Background
9.1.2 Problem Description
9.1.3 Purposes of the Research
9.1.4 Objectives of the Research
9.2 Literature Review
9.2.1 Review Based on Reference Research Paper
9.3 Methodology
9.3.1 Overview of the Hybrid Method
9.3.2 Sentiment Analysis
9.3.3 News Classification Using NLP Techniques
9.3.4 Algorithms for Historical Pattern Matching
9.3.5 Integration
9.4 Data Sources and Collection
9.4.1 Sources of Financial News
9.4.2 Market Data Historical Overview
9.4.3 Cleaning and Pre-Processing Data
9.5 Experimental Setup
9.5.1 Datasets for Training and Testing
9.5.2 Metrics for Evaluation
9.5.3 Optimization and Tuning of Hyperparameters
9.6 Discussion
9.6.1 Comparison of Model Performance
9.6.2 NLP and Pattern Matching’s Effectiveness
9.6.3 Restrictions and Perspectives
9.6.4 Consequences and Prospective Courses
9.7 Results
9.8 Conclusion
References
10. An Intelligent Framework for IoT-Based Health Care Monitoring Using Fuzzy-Supported Machine Learning Algorithm
Mohanapriya M., Bharanidharan R., R. Santhosh and R. Reshma
10.1 Introduction
10.2 Literature Analysis
10.3 Integrated IoT-Based Healthcare Decision Making Model Using Machine Learning (IHM-ML)
10.4 Result and Discussion
10.5 Conclusion and the Future Scope
References
11. Design Strategy for Narrowband Internet of Things with Its Scope and Challenges of Security Solutions
R. Reshma, N. Mohanasundaram and R. Santhosh
11.1 Prologue Study
11.2 Fundamentals of NB-IoT Network Design
11.3 Security Challenges and Vulnerabilities in NB-IoT Systems
11.4 Scope of Machine Intelligence in NB-IoT Security
11.5 Conclusion and the Future Scope
References
12. Machine Learning in Healthcare: Unlocking Precision Diagnosis and Continuous Monitoring Through Voice Analysis
Smilarubavathy G., Keerthana S. M., Nidhya R., Thanga Priscilla and Pavithra D.
12.1 Introduction
12.2 Background
12.3 Methodology
12.4 Results
12.5 Discussion
Conclusion
References
13. Introduction of Advanced and Improved Transposition Algorithm
Dipesh Kumar, Nirupama Mandal and Yugal Kumar
13.1 Introduction
13.2 Literature Study
13.3 Implementation
13.3.1 Algorithm for Encryption
13.3.2 Algorithm for Decryption
13.4 Result
13.4.1 Experimental Setup
13.4.2 Experiment Result
13.4.2.1 Encryption Process
13.4.2.2 Decryption Process
13.5 Conclusion and Future Direction
References
14. Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning
Rathiya R., Kalamani M., Narmadha R. P., Sreenivasa Perumal L. and Kalpana R.
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.4 Performance Analysis
14.5 Result Analysis
14.6 Conclusion
14.7 Future Work
References
15. Advanced Aquaculture Management: A Smart System for Optimizing Oxygen Levels, Shrimp Health Monitoring
Prathyusha Kuncha, J. Manoranjini, Sirisha J., Suneetha Bandeela, Naveen Kumar Penjarla and Simhadri Subhash Goud
15.1 Introduction
15.2 Literature Survey
15.3 System Model
15.4 Results and Discussion
15.5 Conclusion
References
16. Farming Revolution: Precision Agriculture and IoT for Sustainable Growth
Arepalli Gopi, Sudha L. R. and Iwin Thanakumar Joseph S.
16.1 Introduction
16.2 Data Storage and Analysis on Cloud Data
16.3 Architecture IoT with Agriculture
16.4 Results and Performance Validation
16.5 Conclusion
References
17. Comparative Analysis of the Identification and Categorization of the Malaria Parasite Employing Recent Amalgamated Machine Learning Methodologies
Tamal Kumar Kundu, Dinesh Kumar Anguraj, R. Nidhya and V. Maruthi Prasad
Introduction
Dataset Acquisition
Methodology
Literature Survey
Methodology
Results and Discussion
Conclusion
References
Index
Back to Top
Preface
1. Evolution of Industrial Revolution: Industry 5.0 and Beyond
S. Balamurugan and B. Surya
Brief History of Industrial Revolution
Acknowledgement
Bibliography and Further Reading
2. Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept
S. Pradeep, R. Sathish Kumar, M. Jagadesh and A. Karthikeyan
Nomenclature
2.1 Introduction
2.2 Literature Survey
2.3 Digitization, Data Sources, and AI in Healthcare
2.4 AI Mainstreaming in Healthcare
2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation
2.6 Prerequisites for Radical Transformation in Healthcare
2.7 Personalized Healthcare Transformation Using MSOM-Based TOA
2.8 Results
2.9 Conclusion
References
3. A Survey on Security in Data Transmission Using Wireless Communication Methods for IoT Edge Devices
V. Maruthi Prasad and B. Bharathi
3.1 Introduction
3.2 Literature Survey
3.3 Description of Data Protocols for IoT System
3.4 IoT Communication Parameters
3.5 Comparative of Communication Protocols for IoT Systems
3.6 Conclusion
References
4. Innovative Application of Conditional Deep Convolutional Generative Adversarial Networks to Enhance Chronic Kidney Disease Diagnosis with Uneven Datasets
Lakshmi Ramani Burra, Praveen Tumuluru, Janakiramaiah Bonam, S. Hrushikesava Raju, Sunanda Nalajala and Surya Prasada Rao Borra
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.3.1 Data Preprocessing
4.3.2 Conditional Deep Convolutional Generative Adversarial Network
4.3.3 Bidirectional Long-Term Memory (Bi-LSTM) Method
4.4 Result Analysis
4.5 Conclusion
References
5. A Comprehensive Hybrid Implicit and Explicit Item-Based Collaborative Filtering Approach with Bayesian Personalized Ranking for Enhancing Book Recommendations
Adidam Surekha, Radhika Gouni, Satya Keerthi Gorripati, Venubabu Rachapudi, S. Anjali Devi and Anupama Angadi
5.1 Introduction
5.2 Related Work
5.3 Methodology
5.4 Experimental Results and Analysis
5.5 Conclusion
References
6. An Efficient Cluster-Based Deep Learning Model for Multi-Attack Classification in IDS Across Diverse Datasets
Rajesh Bingu, G. Harsha Vardhan Reddy, U. Jyothi Naga Pavan, S. Sneha Sai Sri
and N. V. Praveen Kumar
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Model Design
6.4 Results and Discussion
6.5 Conclusion
References
7. Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification
G. Kiran Kumar, Anila M., Naga Raju Hari Manikyam, Venkata Nagaraju Thatha, R. Vijaya Kumar Reddy and Krishna Reddy Papana
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Methodology
7.4 Results and Discussion
7.5 Conclusion
References
8. Optimal Power Allocation in Cognitive Radio Networks Using Teaching-Learning-Based Optimization
N. Lakshman Pratap, N. Sunanda and V. Suryanarayana Reddy
8.1 Introduction
8.2 Teaching-Learning-Based Optimization
8.2.1 Teacher Phase
8.2.2 Learner Phase
8.3 Proposed Power Allocation Algorithm
8.4 Numerical Results
8.5 Conclusion
References
9. Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market
K. Sri Niharika, C.H. Srisai Naga Satya Mani Pavan, T. Baby Aparna, Dinesh Kumar Anguraj, S. Saathvik and Hari Kiran Vege
9.1 Introduction
9.1.1 Background
9.1.2 Problem Description
9.1.3 Purposes of the Research
9.1.4 Objectives of the Research
9.2 Literature Review
9.2.1 Review Based on Reference Research Paper
9.3 Methodology
9.3.1 Overview of the Hybrid Method
9.3.2 Sentiment Analysis
9.3.3 News Classification Using NLP Techniques
9.3.4 Algorithms for Historical Pattern Matching
9.3.5 Integration
9.4 Data Sources and Collection
9.4.1 Sources of Financial News
9.4.2 Market Data Historical Overview
9.4.3 Cleaning and Pre-Processing Data
9.5 Experimental Setup
9.5.1 Datasets for Training and Testing
9.5.2 Metrics for Evaluation
9.5.3 Optimization and Tuning of Hyperparameters
9.6 Discussion
9.6.1 Comparison of Model Performance
9.6.2 NLP and Pattern Matching’s Effectiveness
9.6.3 Restrictions and Perspectives
9.6.4 Consequences and Prospective Courses
9.7 Results
9.8 Conclusion
References
10. An Intelligent Framework for IoT-Based Health Care Monitoring Using Fuzzy-Supported Machine Learning Algorithm
Mohanapriya M., Bharanidharan R., R. Santhosh and R. Reshma
10.1 Introduction
10.2 Literature Analysis
10.3 Integrated IoT-Based Healthcare Decision Making Model Using Machine Learning (IHM-ML)
10.4 Result and Discussion
10.5 Conclusion and the Future Scope
References
11. Design Strategy for Narrowband Internet of Things with Its Scope and Challenges of Security Solutions
R. Reshma, N. Mohanasundaram and R. Santhosh
11.1 Prologue Study
11.2 Fundamentals of NB-IoT Network Design
11.3 Security Challenges and Vulnerabilities in NB-IoT Systems
11.4 Scope of Machine Intelligence in NB-IoT Security
11.5 Conclusion and the Future Scope
References
12. Machine Learning in Healthcare: Unlocking Precision Diagnosis and Continuous Monitoring Through Voice Analysis
Smilarubavathy G., Keerthana S. M., Nidhya R., Thanga Priscilla and Pavithra D.
12.1 Introduction
12.2 Background
12.3 Methodology
12.4 Results
12.5 Discussion
Conclusion
References
13. Introduction of Advanced and Improved Transposition Algorithm
Dipesh Kumar, Nirupama Mandal and Yugal Kumar
13.1 Introduction
13.2 Literature Study
13.3 Implementation
13.3.1 Algorithm for Encryption
13.3.2 Algorithm for Decryption
13.4 Result
13.4.1 Experimental Setup
13.4.2 Experiment Result
13.4.2.1 Encryption Process
13.4.2.2 Decryption Process
13.5 Conclusion and Future Direction
References
14. Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning
Rathiya R., Kalamani M., Narmadha R. P., Sreenivasa Perumal L. and Kalpana R.
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.4 Performance Analysis
14.5 Result Analysis
14.6 Conclusion
14.7 Future Work
References
15. Advanced Aquaculture Management: A Smart System for Optimizing Oxygen Levels, Shrimp Health Monitoring
Prathyusha Kuncha, J. Manoranjini, Sirisha J., Suneetha Bandeela, Naveen Kumar Penjarla and Simhadri Subhash Goud
15.1 Introduction
15.2 Literature Survey
15.3 System Model
15.4 Results and Discussion
15.5 Conclusion
References
16. Farming Revolution: Precision Agriculture and IoT for Sustainable Growth
Arepalli Gopi, Sudha L. R. and Iwin Thanakumar Joseph S.
16.1 Introduction
16.2 Data Storage and Analysis on Cloud Data
16.3 Architecture IoT with Agriculture
16.4 Results and Performance Validation
16.5 Conclusion
References
17. Comparative Analysis of the Identification and Categorization of the Malaria Parasite Employing Recent Amalgamated Machine Learning Methodologies
Tamal Kumar Kundu, Dinesh Kumar Anguraj, R. Nidhya and V. Maruthi Prasad
Introduction
Dataset Acquisition
Methodology
Literature Survey
Methodology
Results and Discussion
Conclusion
References
Index
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