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Submit a ProposalArtificial Intelligence and Machine Learning for Industry 4.0
 | Edited by M. Thirunavukkarasan, S.A. Sahaaya Arul Mary, Sathiyaraj R., G.S. Pradeep Ghantasala, and Mudassir Khan Copyright: 2025 | Expected Pub Date:2025// ISBN: 9781394275045 | Hardcover | 328 pages Price: $225 USD  |
One Line Description
Artificial Intelligence and Machine Learning for Industry 4.0 is essential for any leader seeking to understand how to leverage intelligent automation and predictive maintenance to drive innovation, enhance productivity, and minimize downtime in their manufacturing processes.
Audience
Students, educators, researchers, and industry professionals working with computer science and engineering, electronic engineering, and mechatronics engineering
Students, educators, researchers, and industry professionals working with computer science and engineering, electronic engineering, and mechatronics engineering
Description
Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. Artificial and Machine Learning for Industry 4.0 emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms.
Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial and Machine Learning for Industry 4.0 offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.
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Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. Artificial and Machine Learning for Industry 4.0 emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms.
Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial and Machine Learning for Industry 4.0 offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.
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Author / Editor Details
M. Thirunavukkarasan, PhD is an assistant professor in the School of Computer Science and Engineering at the Vellore Institute of Technology with over 15 years of research and teaching experience. He has published papers in several international conferences and journals and given keynote speeches at many international conferences. His research interests include Internet of Things (IoT), wireless sensor networks, wireless communication, cloud computing, artificial intelligence, and machine learning.
M. Thirunavukkarasan, PhD is an assistant professor in the School of Computer Science and Engineering at the Vellore Institute of Technology with over 15 years of research and teaching experience. He has published papers in several international conferences and journals and given keynote speeches at many international conferences. His research interests include Internet of Things (IoT), wireless sensor networks, wireless communication, cloud computing, artificial intelligence, and machine learning.
S. A. Sahaaya Arul Mary, PhD is a professor in the School of Computer Science and Engineering, Vellore Institute of Technology with over 29 years of teaching and over 15 years of research experience. She has over 70 publications in various reputed journals and conferences and reviewed over 35 papers in addition to mentoring aspiring PhD students. Her research includes software engineering, data mining, machine learning, and artificial intelligence.
R. Sathiyaraj, PhD is an assistant professor in the Department of Computer Science and Engineering at Gandhi Institute of Technology and Management University in Bangalore, India. He has contributed to two books, served as lead editor for an additional two books, and published five patents and over 20 articles in various international journals and conferences. His research interests include machine learning, big data analytics, and intelligent systems.
G. S. Pradeep Ghantasala, PhD is a professor in the Department of Computer Science and Engineering, at Alliance University with over 16 years of academic experience. He has contributed to internationally published books, chapters, patents, and numerous papers in journals and conferences. He also serves as an editor and reviewer for several journals. His research interests include machine learning, deep learning, healthcare applications, and software engineering applications.
Mudassir Khan, PhD is an assistant professor in the Department of Computer Science at King Khalid University with over ten years of teaching experience. He has published over 25 papers in international journals and conferences and one patent. He is a member of various technical and professional societies including the Institute for Electrical and Electronics Engineers and Computer Science Teachers Association. His research interests include big data, deep learning, machine learning, eLearning, fuzzy logic, image processing, and cyber security.
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Table of Contents
Preface
1. Industry 4.0 and the AI/ML Era: Revolutionizing Manufacturing
Balusamy Nachiappan, C. Viji, N. Rajkumar, A. Mohanraj, N. Karthikeyan, Judeson Antony Kovilpillai J. and Pellakuri Vidyullatha
1.1 Introduction
1.1.1 Key Traits of Industry 4.0
1.2 Literature Survey
1.2.1 Foundations of Industry 4.0
1.2.2 Integration of AI and ML
1.2.3 Smart Automation and Human-Robotic Collaboration
1.2.4 Cognitive Manufacturing
1.2.5 Disturbing Situations and Opportunities
1.3 The AI/ML Era Within the Industrial Revolution
1.3.1 The Role of AI and ML
1.3.2 Opportunities
1.4 The Nexus of Industry 4.0 and the AI/ML Era: A Symbiotic Evolution
1.5 Challenges and Opportunities in the Integration of Industry 4.0 and the AI/ML Era
1.6 Implementation Techniques
1.6.1 Future Suggestions
1.7 Conclusion
References
2. Business Intelligence and Big Data Analytics for Industry 4.0
N. Rajkumar, C. Viji, Balusamy Nachiappan, A. Mohanraj, N. Karthikeyan, Judeson Antony Kovilpillai J. and Sathiyaraj. R
2.1 Introduction
2.1.1 The Biggest Challenge of Industry 4.0
2.2 Literature Review
2.3 Business Intelligence
2.3.1 Challenges of Business Intelligence in Industry 4.0
2.4 Big Data Analytics
2.4.1 Five Pillars of Big Data
2.4.2 Big Data to the Rescue
2.4.3 Challenges in Big Data Analytics for Industry 4.0
2.4.4 Advantage in Big Data Analysis for Industry 4.0
2.5 Result and Discussion
2.6 Conclusion
References
3. “AI-Powered Mental Health Innovations”: Handling the Effects of Industry 4.0 on Health
U Ananthanagu and Pooja Agarwal
3.1 Introduction
3.1.1 An Overview of Industry 4.0’s Development in Healthcare Over Time
3.1.2 The Advancement of AI in Mental Health
3.2 Related Work
3.2.1 Recognizing AI’s Place in Healthcare
3.2.2 Comprehending AI’s Impact on Mental Health
3.3 Machine Learning in Healthcare
3.3.1 SML-Supervised Machine Learning
3.3.2 Unsupervised Machine Learning (UML)
3.3.3 Deep Learning (DL)
3.3.4 NLP - Natural Language Processing
3.4 Genetics and Machine Learning for Understanding and Prediction of Complicated Illnesses
3.5 AI-Driven Virtual Healthcare Support for Patient Care
3.6 AI’s Advantages for Mental Health Treatment
3.7 AI’s Predictive Capabilities: Revolutionizing Mental Health Treatment
3.8 AI’s Limitations and Research on Mental Health
3.9 Ethical Issues and Difficulties with AI-Powered Mental Health
3.10 Healthcare AI Governance
3.11 Artificial Intelligence in Augmented and Virtual Reality (AR & VR)
3.12 Methodology
3.13 Results and Discussions
3.13.1 Synopsis of AI Research in Mental Health
3.13.2 AI-Driven Intervention as the Future of Mental Healthcare
3.14 Conclusion
References
4. AI ML Empowered Smart Buildings and Factories
Akey Sungheetha, Rajesh Sharma R., R. Chinnaiyan and G. S. Pradeep Ghantasala
4.1 Introduction
4.1.1 An Account of How Machine Learning Contributes to Task Automation
4.1.2 A Description of How Mobile Phones and Computers Facilitate the Completion of Tasks in Intelligent Buildings
4.1.3 Intelligent Buildings as well as IoT
4.1.4 Utilizing the Ubiquitous Internet of Things Plus the Global Web to Link Buildings
4.2 The Advancement of Computational Intelligence within Smart Building Technology and its Worldwide Consequences
4.2.1 Industrial 4.0 Along with IoT
4.2.2 An Exploration of the Web of Things and its Role in Making 4.0
4.3 An Examination on ML, DL and AI Algorithms Used for Engineering and Construction
4.3.1 Utilization in Intelligent Structures
4.3.2 A Few Examples of the Numerous Uses in Smart Buildings are Automation, Material Efficiency, Off-Site Production, Designing Buildings, and the Combination
of Big Data
4.3.3 Detectors, and Computational AI Enabling Intelligent Management and Energy Efficiency
4.4 Conclusion
4.5 Future Advances in Urban Energy Efficiency and Smart Building Technologies
References
5. Applications of Artificial Intelligence and Machine Learning in Industry 4.0
Tina Babu, Rekha R. Nair and Kishore S.
5.1 Introduction
5.1.1 Overview of Industry 4.0
5.1.2 Key Components and Technologies
5.2 Smart Manufacturing and Predictive Maintenance
5.2.1 Integration of AI/ML in Manufacturing Process
5.2.2 Predictive Maintenance Strategies
5.3 Supply Chain Optimization
5.3.1 AI/ ML for Supply Chain Management
5.3.2 Optimizing Logistics and Inventory
5.4 Quality Control and Defect Detection
5.4.1 AI/ ML for Quality Assurance
5.4.2 Automated Defect Detection System
5.5 Robotics and Automation
5.5.1 Robotics in Smart Factories
5.5.2 AI-Driven Automation Process
5.6 Data Analytics and Decision Support
5.6.1 Big Data Analytics in Industry 4.0
5.6.2 Decision Support System with AI/ML
5.7 Cybersecurity in Industry 4.0
5.7.1 Challenges and Threats
5.7.2 AI-Enhanced Cybersecurity Solutions
5.8 Human-Machine Collaboration
5.8.1 Human-Centric AI Applications
5.8.2 Collaboration Interfaces in Smart Manufacturing
5.9 Energy Efficiency and Sustainability
5.9.1 Role of AI ML in Energy Management
5.9.2 Sustainable Practices in Industry 4.0
5.10 Emerging Trends and Future Prospects
Conclusion
References
6. Application of Machine Learning in Moisture Content Prediction of Coffee Drying Process
Tuan M. Le, Thuy T. Tran, Hieu M. Tran and Son V.T. Dao
6.1 Introduction
6.2 Literature Reviews
6.2.1 Related Works
6.2.2 Background of Machine Learning and Credit Risk Prediction Techniques
6.2.2.1 Non-Linear Regression
6.2.2.2 Artificial Neural Networks (ANN)
6.2.2.3 Adaptive Network-Based Fuzzy Inference System (ANFIS)
6.3 Methodology
6.3.1 Data Collection
6.3.2 Data Preprocessing
6.3.2.1 Missing Value Detection and Attribute Visualization
6.3.2.2 Normalization
6.3.2.3 Standardization
6.3.2.4 Cross-Validation
6.3.3 Research Methodology
6.3.3.1 Multi-Layer Perceptron (MLP) Regression
6.3.3.2 Adaptive Neuro-Fuzzy Inference System – ANFIS
6.3.3.3 Feature Selection Techniques
6.4 Results and Analysis
6.4.1 Model Evaluation
6.4.2 Analysis Results
6.4.3 Analysis Results with Feature Selection
6.4.3.1 Feature Selection with ANN
6.4.3.2 Feature Selection with ANFIS
6.5 Conclusion
References
7. Survivable AI for Defense Strategies in Industry 4.0
Anuradha Reddy, G. S. Pradeep Ghantasala, Ochin Sharma, Mamatha Kurra, Kumar Dilip and Pellakuri Vidyullatha
7.1 Introduction
7.2 Purpose
7.3 Scope
7.4 History of AI for Defense Strategies in Industry 4.0
7.4.1 AI in Defense
7.4.2 AI in Defense Strategies in Industry 4.0
7.5 AI Applications in Defense Strategies in Industry 4.0
7.6 Era of AI in Industry
7.6.1 Era of AI Applications in Industry 4.0
7.7 Importance of AI in the Defense Industry
7.8 Future of AI in the Defense Industry
7.8.1 Cyberattacks in Defense Industry
7.8.2 Trade-Offs of AI in Industry 4.0
7.8.3 Cyberattacks in Defense Industry 4.0
7.9 Conclusion
References
8. Industry 4.0 Based Turbofan Performance Prediction
M. Sai Narayan, Prajakta P. Nandanwar, Annabathini Lokesh, Bathula Lakshmi Narayana, Varun Revadigar, Judeson Antony Kovilpillai J., Neelapala Anil Kumar and D.M. Deepak Raj
8.1 Introduction
8.2 Search Methodology
8.2.1 Sensor-Based Technique
8.2.2 Data-Driven Approaches
8.2.3 Benefits and Challenges of Machine Learning for PdM
8.2.4 Challenges
8.3 Literature Review
8.3.1 Identification of Problem
8.3.2 Objectives
8.4 Methodology
8.5 Experimental Results
8.5.1 Data Preprocessing
8.5.2 Developing Models
8.5.3 Training and Validation
8.5.4 Evaluation
8.5.5 Comparison with Baseline
8.5.6 Sensitivity Analysis
8.6 Conclusion and Future Work
8.7 Additional Considerations
References
9. Industrial Predictive Maintenance for Sustainable Manufacturing
Mohammed Rihan, Ethiswar Muchherla, Shwejit Shri, Kushagra Jasoria, Judeson Antony Kovilpillai J. and G. S. Pradeep Ghantasala
9.1 Introduction
9.1.1 IoT Internet of Things
9.1.2 Industry 4.0
9.2 Search Methodology
9.3 Methodology
9.3.1 Types of Maintenance
9.3.2 IoT Technologies for Predictive Maintenance
9.3.3 Predictive Maintenance Workflow
9.3.4 Predictive Maintenance Model
9.3.5 Data Collection Techniques
9.3.6 Data Analysis Techniques
9.3.7 Predictive Analytics Algorithms
9.3.8 Machine Learning Techniques in PdM
9.3.9 Comparative Analysis
9.3.10 Limitations and Considerations
9.4 Conclusion
References
10. Enhanced Security Framework with Blockchain for Industry 4.0 Cyber Physical Systems, Exploring IoT Integration Challenges and Applications
P. Vijayalakshmi, B. Selvalakshmi, K. Subashini, Sudhakar G., Kavin Francis Xavier and Pradeepa K.
10.1 Introduction
10.2 Related Works
10.3 Industry 4.0 Elements
10.3.1 CPS in Critical Industry 4.0
10.3.2 Challenges in IoT Integration
10.3.3 Security Provided Through Blockchain
10.3.4 Blockchain Replaces the Certificate Authority
10.4 Results and Discussions
10.5 Conclusions
References
11. Integrating Artificial Intelligence and Machine Learning for Enhanced Cyber Security in Industry 4.0: Designing a Smart Factory with IoT and CPS
Kavin Francis Xavier, Subashini K., Vijayalakshmi P., Selvalakshmi B., Sudhakar G. and Pradeepa K.
11.1 Introduction
11.2 Related Works
11.3 Proposed Model
11.3.1 Smart Factory
11.3.2 The Mechanical Design
11.3.3 Proposed IDS Architecture
11.3.4 IDS in CCPS
11.4 Results and Discussions
11.5 Conclusions
References
12. Application of AI and ML in Industry 4.0
V. Vinaya Kumari, G. S. Pradeep Ghantasala, S. A. Sahaaya Arul Mary, M. Thirunavukkarasan and Sathiyaraj. R
12.1 Introduction
12.2 Application of AI and ML in Industry 4.0
12.3 Benefits of AI and ML in Industry 4.0
12.4 Challenges and Considerations in Adopting AI and ML in Industry 4.0
12.5 Case Studies and Examples of AI and ML in Industry 4.0
12.6 Emerging AI and ML Technologies in Industry 4.0
12.7 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. Industry 4.0 and the AI/ML Era: Revolutionizing Manufacturing
Balusamy Nachiappan, C. Viji, N. Rajkumar, A. Mohanraj, N. Karthikeyan, Judeson Antony Kovilpillai J. and Pellakuri Vidyullatha
1.1 Introduction
1.1.1 Key Traits of Industry 4.0
1.2 Literature Survey
1.2.1 Foundations of Industry 4.0
1.2.2 Integration of AI and ML
1.2.3 Smart Automation and Human-Robotic Collaboration
1.2.4 Cognitive Manufacturing
1.2.5 Disturbing Situations and Opportunities
1.3 The AI/ML Era Within the Industrial Revolution
1.3.1 The Role of AI and ML
1.3.2 Opportunities
1.4 The Nexus of Industry 4.0 and the AI/ML Era: A Symbiotic Evolution
1.5 Challenges and Opportunities in the Integration of Industry 4.0 and the AI/ML Era
1.6 Implementation Techniques
1.6.1 Future Suggestions
1.7 Conclusion
References
2. Business Intelligence and Big Data Analytics for Industry 4.0
N. Rajkumar, C. Viji, Balusamy Nachiappan, A. Mohanraj, N. Karthikeyan, Judeson Antony Kovilpillai J. and Sathiyaraj. R
2.1 Introduction
2.1.1 The Biggest Challenge of Industry 4.0
2.2 Literature Review
2.3 Business Intelligence
2.3.1 Challenges of Business Intelligence in Industry 4.0
2.4 Big Data Analytics
2.4.1 Five Pillars of Big Data
2.4.2 Big Data to the Rescue
2.4.3 Challenges in Big Data Analytics for Industry 4.0
2.4.4 Advantage in Big Data Analysis for Industry 4.0
2.5 Result and Discussion
2.6 Conclusion
References
3. “AI-Powered Mental Health Innovations”: Handling the Effects of Industry 4.0 on Health
U Ananthanagu and Pooja Agarwal
3.1 Introduction
3.1.1 An Overview of Industry 4.0’s Development in Healthcare Over Time
3.1.2 The Advancement of AI in Mental Health
3.2 Related Work
3.2.1 Recognizing AI’s Place in Healthcare
3.2.2 Comprehending AI’s Impact on Mental Health
3.3 Machine Learning in Healthcare
3.3.1 SML-Supervised Machine Learning
3.3.2 Unsupervised Machine Learning (UML)
3.3.3 Deep Learning (DL)
3.3.4 NLP - Natural Language Processing
3.4 Genetics and Machine Learning for Understanding and Prediction of Complicated Illnesses
3.5 AI-Driven Virtual Healthcare Support for Patient Care
3.6 AI’s Advantages for Mental Health Treatment
3.7 AI’s Predictive Capabilities: Revolutionizing Mental Health Treatment
3.8 AI’s Limitations and Research on Mental Health
3.9 Ethical Issues and Difficulties with AI-Powered Mental Health
3.10 Healthcare AI Governance
3.11 Artificial Intelligence in Augmented and Virtual Reality (AR & VR)
3.12 Methodology
3.13 Results and Discussions
3.13.1 Synopsis of AI Research in Mental Health
3.13.2 AI-Driven Intervention as the Future of Mental Healthcare
3.14 Conclusion
References
4. AI ML Empowered Smart Buildings and Factories
Akey Sungheetha, Rajesh Sharma R., R. Chinnaiyan and G. S. Pradeep Ghantasala
4.1 Introduction
4.1.1 An Account of How Machine Learning Contributes to Task Automation
4.1.2 A Description of How Mobile Phones and Computers Facilitate the Completion of Tasks in Intelligent Buildings
4.1.3 Intelligent Buildings as well as IoT
4.1.4 Utilizing the Ubiquitous Internet of Things Plus the Global Web to Link Buildings
4.2 The Advancement of Computational Intelligence within Smart Building Technology and its Worldwide Consequences
4.2.1 Industrial 4.0 Along with IoT
4.2.2 An Exploration of the Web of Things and its Role in Making 4.0
4.3 An Examination on ML, DL and AI Algorithms Used for Engineering and Construction
4.3.1 Utilization in Intelligent Structures
4.3.2 A Few Examples of the Numerous Uses in Smart Buildings are Automation, Material Efficiency, Off-Site Production, Designing Buildings, and the Combination
of Big Data
4.3.3 Detectors, and Computational AI Enabling Intelligent Management and Energy Efficiency
4.4 Conclusion
4.5 Future Advances in Urban Energy Efficiency and Smart Building Technologies
References
5. Applications of Artificial Intelligence and Machine Learning in Industry 4.0
Tina Babu, Rekha R. Nair and Kishore S.
5.1 Introduction
5.1.1 Overview of Industry 4.0
5.1.2 Key Components and Technologies
5.2 Smart Manufacturing and Predictive Maintenance
5.2.1 Integration of AI/ML in Manufacturing Process
5.2.2 Predictive Maintenance Strategies
5.3 Supply Chain Optimization
5.3.1 AI/ ML for Supply Chain Management
5.3.2 Optimizing Logistics and Inventory
5.4 Quality Control and Defect Detection
5.4.1 AI/ ML for Quality Assurance
5.4.2 Automated Defect Detection System
5.5 Robotics and Automation
5.5.1 Robotics in Smart Factories
5.5.2 AI-Driven Automation Process
5.6 Data Analytics and Decision Support
5.6.1 Big Data Analytics in Industry 4.0
5.6.2 Decision Support System with AI/ML
5.7 Cybersecurity in Industry 4.0
5.7.1 Challenges and Threats
5.7.2 AI-Enhanced Cybersecurity Solutions
5.8 Human-Machine Collaboration
5.8.1 Human-Centric AI Applications
5.8.2 Collaboration Interfaces in Smart Manufacturing
5.9 Energy Efficiency and Sustainability
5.9.1 Role of AI ML in Energy Management
5.9.2 Sustainable Practices in Industry 4.0
5.10 Emerging Trends and Future Prospects
Conclusion
References
6. Application of Machine Learning in Moisture Content Prediction of Coffee Drying Process
Tuan M. Le, Thuy T. Tran, Hieu M. Tran and Son V.T. Dao
6.1 Introduction
6.2 Literature Reviews
6.2.1 Related Works
6.2.2 Background of Machine Learning and Credit Risk Prediction Techniques
6.2.2.1 Non-Linear Regression
6.2.2.2 Artificial Neural Networks (ANN)
6.2.2.3 Adaptive Network-Based Fuzzy Inference System (ANFIS)
6.3 Methodology
6.3.1 Data Collection
6.3.2 Data Preprocessing
6.3.2.1 Missing Value Detection and Attribute Visualization
6.3.2.2 Normalization
6.3.2.3 Standardization
6.3.2.4 Cross-Validation
6.3.3 Research Methodology
6.3.3.1 Multi-Layer Perceptron (MLP) Regression
6.3.3.2 Adaptive Neuro-Fuzzy Inference System – ANFIS
6.3.3.3 Feature Selection Techniques
6.4 Results and Analysis
6.4.1 Model Evaluation
6.4.2 Analysis Results
6.4.3 Analysis Results with Feature Selection
6.4.3.1 Feature Selection with ANN
6.4.3.2 Feature Selection with ANFIS
6.5 Conclusion
References
7. Survivable AI for Defense Strategies in Industry 4.0
Anuradha Reddy, G. S. Pradeep Ghantasala, Ochin Sharma, Mamatha Kurra, Kumar Dilip and Pellakuri Vidyullatha
7.1 Introduction
7.2 Purpose
7.3 Scope
7.4 History of AI for Defense Strategies in Industry 4.0
7.4.1 AI in Defense
7.4.2 AI in Defense Strategies in Industry 4.0
7.5 AI Applications in Defense Strategies in Industry 4.0
7.6 Era of AI in Industry
7.6.1 Era of AI Applications in Industry 4.0
7.7 Importance of AI in the Defense Industry
7.8 Future of AI in the Defense Industry
7.8.1 Cyberattacks in Defense Industry
7.8.2 Trade-Offs of AI in Industry 4.0
7.8.3 Cyberattacks in Defense Industry 4.0
7.9 Conclusion
References
8. Industry 4.0 Based Turbofan Performance Prediction
M. Sai Narayan, Prajakta P. Nandanwar, Annabathini Lokesh, Bathula Lakshmi Narayana, Varun Revadigar, Judeson Antony Kovilpillai J., Neelapala Anil Kumar and D.M. Deepak Raj
8.1 Introduction
8.2 Search Methodology
8.2.1 Sensor-Based Technique
8.2.2 Data-Driven Approaches
8.2.3 Benefits and Challenges of Machine Learning for PdM
8.2.4 Challenges
8.3 Literature Review
8.3.1 Identification of Problem
8.3.2 Objectives
8.4 Methodology
8.5 Experimental Results
8.5.1 Data Preprocessing
8.5.2 Developing Models
8.5.3 Training and Validation
8.5.4 Evaluation
8.5.5 Comparison with Baseline
8.5.6 Sensitivity Analysis
8.6 Conclusion and Future Work
8.7 Additional Considerations
References
9. Industrial Predictive Maintenance for Sustainable Manufacturing
Mohammed Rihan, Ethiswar Muchherla, Shwejit Shri, Kushagra Jasoria, Judeson Antony Kovilpillai J. and G. S. Pradeep Ghantasala
9.1 Introduction
9.1.1 IoT Internet of Things
9.1.2 Industry 4.0
9.2 Search Methodology
9.3 Methodology
9.3.1 Types of Maintenance
9.3.2 IoT Technologies for Predictive Maintenance
9.3.3 Predictive Maintenance Workflow
9.3.4 Predictive Maintenance Model
9.3.5 Data Collection Techniques
9.3.6 Data Analysis Techniques
9.3.7 Predictive Analytics Algorithms
9.3.8 Machine Learning Techniques in PdM
9.3.9 Comparative Analysis
9.3.10 Limitations and Considerations
9.4 Conclusion
References
10. Enhanced Security Framework with Blockchain for Industry 4.0 Cyber Physical Systems, Exploring IoT Integration Challenges and Applications
P. Vijayalakshmi, B. Selvalakshmi, K. Subashini, Sudhakar G., Kavin Francis Xavier and Pradeepa K.
10.1 Introduction
10.2 Related Works
10.3 Industry 4.0 Elements
10.3.1 CPS in Critical Industry 4.0
10.3.2 Challenges in IoT Integration
10.3.3 Security Provided Through Blockchain
10.3.4 Blockchain Replaces the Certificate Authority
10.4 Results and Discussions
10.5 Conclusions
References
11. Integrating Artificial Intelligence and Machine Learning for Enhanced Cyber Security in Industry 4.0: Designing a Smart Factory with IoT and CPS
Kavin Francis Xavier, Subashini K., Vijayalakshmi P., Selvalakshmi B., Sudhakar G. and Pradeepa K.
11.1 Introduction
11.2 Related Works
11.3 Proposed Model
11.3.1 Smart Factory
11.3.2 The Mechanical Design
11.3.3 Proposed IDS Architecture
11.3.4 IDS in CCPS
11.4 Results and Discussions
11.5 Conclusions
References
12. Application of AI and ML in Industry 4.0
V. Vinaya Kumari, G. S. Pradeep Ghantasala, S. A. Sahaaya Arul Mary, M. Thirunavukkarasan and Sathiyaraj. R
12.1 Introduction
12.2 Application of AI and ML in Industry 4.0
12.3 Benefits of AI and ML in Industry 4.0
12.4 Challenges and Considerations in Adopting AI and ML in Industry 4.0
12.5 Case Studies and Examples of AI and ML in Industry 4.0
12.6 Emerging AI and ML Technologies in Industry 4.0
12.7 Conclusion
References
About the Editors
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