Topics in Artificial Intelligence Applied to Industry 4.0

✍ Scribed by Mahmoud Ragab AL-Refaey, Amit Kumar Tyagi, Abdullah Saad AL-Malaise AL-Ghamdi, Swetta Kukreja

• Publisher: John Wiley & Sons
• Year: 2024
• Tongue: English
• Leaves: 319
• Category: Library

✦ Synopsis

Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market, and become more responsive in how they produce products and services for their consumers, such as by investing in flexible production lines. Written by highly qualified authors, Topics in Artificial Intelligence Applied to Industry 4.0 explores sample topics such as: Quantum machine learning, neural network implementation, and cloud and data analytics for effective analysis of industrial data Computer vision, emerging networking technologies, industrial data spaces, and an industry vision for 2030 in both developing and developed nations Novel or improved nature-inspired optimization algorithms in enhancing Industry 5.0 and the connectivity of any components for smart environment Future professions in agriculture, medicine, education, fitness, R&D, and transport and communication as a result of new technologies Aimed at researchers and students in the interdisciplinary fields of Smart Manufacturing and Smart Applications, Topics in Artificial Intelligence Applied to Industry 4.0 provides the perfect overview of technology from the perspective of modern society and operational environment.

✦ Table of Contents

Cover
Title Page
Copyright Page
Contents
About the Editors
List of Contributors
Preface
Acknowledgment
Chapter 1 Introduction to Industry 4.0 and Its Impacts on Society
1.1 Introduction
1.1.1 Overview of the Major Technological Advancements Driving the Revolution
1.1.2 Importance of Studying and Understanding the Impacts of the Fourth Industrial Revolution
1.2 The Technological Advancements of the Fourth Industrial Revolution
1.2.1 Discussion of the Potential Benefits and Drawbacks of Each Technology
1.2.2 Examples of How These Technologies Are Already Being Used in Various Industries
1.3 Impacts on the Economy
1.3.1 The Potential Impacts of the Fourth Industrial Revolution on the Economy
1.3.2 Analysis of Fourth Industrial Revolution Is Changing the Nature of Work and Engages in Economic Activities
1.3.3 Discussion of How Businesses and Governments Can Prepare for and Adapt to These Changes
1.4 Impacts on Society
1.4.1 Discussion of How the Fourth Industrial Revolution Is Impacting Society
1.4.2 Analysis of How Individuals and Communities Can Best Adapt to These Changes
1.4.3 Examples of How the Fourth Industrial Revolution Is Being Used to Address Social Challenges and Promote Social Good
1.5 Ethics and Governance
1.5.1 Discussion of the Ethical and Governance Challenges Posed by the Fourth Industrial Revolution
1.5.2 Analysis of the Role of Government, Businesses, and Individuals in Addressing These Challenges
1.5.3 Examples of Best Practices in Ethical and Responsible Technology Development and Deployment
1.6 Future Directions
1.6.1 Potential Future Directions of the Fourth Industrial Revolution
1.6.2 Analysis of the Fourth Industrial Revolution
1.6.3 Discussion of the Fourth Industrial Revolution in a Positive and Equitable Way
1.7 Conclusion
1.7.1 Reflection on the Importance of Studying the Fourth Industrial Revolution and Its Impacts on Society
1.7.2 Fourth Industrial Revolution in a Way That Benefits All Members of Society
References
Chapter 2 Digital Transformation Using Industry 4.0 and Artificial Intelligence
2.1 Introduction
2.2 Industry 4.0 Technologies
2.3 AI Features in Industry 4.0
2.3.1 Machine Learning
2.3.2 Deep Learning
2.3.3 Natural Language Processing
2.3.4 Computer Vision
2.4 Industry 4.0 and XAI
2.5 Industry 4.0 Integration Using an XAI-Based Methodology with AI
2.5.1 Intelligent Communities
2.5.2 Smart Industries
2.5.3 Adaptive Production
2.5.4 Intelligent Health Care
2.5.5 Human-Computer Interaction
2.5.6 Prevention-based Maintenance
2.5.7 Intuitive Assistance
2.5.8 Smart Gadgets
2.5.9 Commercial Robotics
2.5.10 Digital-Privacy and Security
2.5.11 Intelligent Transportation
2.6 Case Studies for Industry 4.0
2.6.1 Analytics of Big Data
2.6.2 Intelligent Robotics
2.6.3 Cloud-Based Computing
2.6.4 Digital Industry 4.0 and IoT
2.6.5 Standard Product Lifecycle Management Packages and Industry 4.0
2.6.6 Internet of Things and Product Lifecycle Management
2.6.7 Leveraging 3D Printing
2.6.8 Technologies for Cybersecurity
2.7 Challenges of Industry 4.0
2.8 Advantages of Intelligent Factory
2.8.1 Positive Effects of Industrial Revolution 4.0
2.9 Discussion and Emerging Trends
2.10 Conclusion
References
Chapter 3 Industry 4.0: Design Principles, Challenges, and Applications
3.1 Introduction
3.2 Organization of Chapter
3.3 Industrial Revolutions
3.4 Generations of Industrial Revolutions
3.4.1 First Industrial Revolution
3.4.2 Second Industrial Revolution
3.4.3 Third Industrial Revolution
3.5 Transformation to Industry 4.0
3.6 Characteristics of Industry 4.0
3.7 Technologies Under Industry 4.0
3.7.1 Cyber-Physical Systems
3.7.2 Internet of Things
3.7.3 Big Data and Analytics
3.7.4 Cloud Technology
3.7.5 Artificial Intelligence
3.7.6 Blockchain
3.7.7 Simulation and Modeling
3.7.8 Visualization Technology
3.7.9 Automation and Industrial Robots
3.7.10 Additive Manufacturing
3.8 Design Principles of Industry 4.0
3.8.1 Interoperability
3.8.2 Virtualization
3.8.3 Real-Time Capability
3.8.4 Service Orientation
3.8.5 Modularity
3.8.6 Information Transparency
3.8.7 Decentralization
3.8.8 Smart Product
3.8.9 Corporate Social Responsibility
3.8.10 Technical Assistance
3.8.11 Resource Efficiency
3.9 Applications of Industry 4.0
3.10 Trends in Industry 4.0
3.11 Challenges of Industry 4.0
3.12 Related Works
3.13 Paradigm Shift Toward Industry 5.0
3.14 Future Challenges and Research
3.15 Conclusion
References
Chapter 4 Detection from Chest X-Ray Images Based on Modified Deep Learning Approach
4.1 Introduction
4.2 Related Works
4.2.1 Significance of the Study
4.3 Research Methodology
4.3.1 Dataset Description
4.3.2 Data Preprocessing
4.3.3 Transfer Learning
4.3.4 Experimental Approaches
4.3.5 Evaluation Metrics
4.4 Results and Discussions
4.4.1 Data Preparation
4.4.2 Image Enhancements
4.4.3 Image Augmentation
4.4.4 Pretrained Model Fine-tuning
4.4.5 Model Hyperparameter Selection
4.4.6 Model Callbacks
4.4.7 Model Training
4.4.8 Ensemble Model Development
4.4.9 Model Evaluation
4.4.10 U-Net Model Performance
4.4.11 Performance of Fine-Tuned Model
4.4.12 EfficientNet-B0 Model Performance
4.4.13 ChexNet Model Performance
4.4.14 SqueezeNet Model Performance
4.4.15 Performances of Ensemble Models
4.4.16 Performance Analysis of Sum of Probabilities Ensemble Model
4.4.17 Performance Analysis of Stacked Generalization Model
4.4.18 Overall Results of Model Building
4.4.19 Selection of Best Model
4.4.20 Comparison of Research Findings on TBX11K Dataset
4.4.21 Comparison of Model Performance with Other Binary Classification Models
4.5 Conclusions
References
Chapter 5 Smart Technologies in Manufacturing Industries: A Useful Perspective
5.1 Introduction
5.2 Literature Review
5.3 Materials and Methods
5.3.1 Manufacturing-Led Design
5.3.2 3D Printing
5.3.3 CNC Machining
5.3.4 Cloud Computing and Storage
5.3.5 Internet of Things
5.3.6 Cyber-Physical Production Systems
5.3.7 Sensors and Automatic Identification
5.3.8 Big Data Analytics
5.3.9 Blockchain Technology
5.3.10 Artificial Intelligence
5.4 Discussion
5.4.1 Present and Future Challenges
5.5 Conclusion
References
Chapter 6 Blockchain Technology for Industry 4.0
6.1 Introduction
6.1.1 Definition and Overview of Industry 4.0
6.1.2 Introduction to Blockchain Technology and Its Key Characteristics
6.1.3 Significance and Rationale for Integrating Blockchain in Industry 4.0
6.2 Key Concepts of Blockchain
6.2.1 Distributed Ledger Technology and Decentralized Consensus Mechanisms
6.2.2 Smart Contracts and Their Role in Automating Processes
6.2.3 Public Versus Private Blockchains: Trade-Offs and Considerations
6.2.4 Interoperability and Standardization Challenges
6.3 Blockchain in Data Privacy and Security
6.3.1 Ensuring Data Privacy in a Decentralized Ecosystem
6.3.2 Immutable and Tamper-Proof Records for Enhanced Data Integrity
6.3.3 Secure Peer-to-Peer Communication and Encryption Protocols
6.3.4 Self-Sovereign Identity and User-Controlled Data Sharing
6.4 Cybersecurity in the Era of Industry 4.0
6.4.1 Blockchain as a Security Layer for IoT Devices and Networks
6.4.2 Prevention of Unauthorized Access and Tampering of Data
6.5 Supply Chain Management and Traceability
6.6 Blockchain-Enabled Smart Manufacturing
6.7 Overcoming Challenges in Blockchain Implementation
6.8 Real-World Applications of Blockchain in Industry 4.0
6.9 Future Trends
6.10 Conclusion
Declarations
References
Chapter 7 Unifying Technologies in Industry 4.0: Harnessing the Synergy of Internet of Things, Big Data, Augmented Reality/ Virtual Reality, and Blockchain Technologies
7.1 Introduction to Industry 4.0
7.1.1 Evolution of Industrial Revolutions
7.1.2 Components and Technologies of Industry 4.0
7.1.3 Industry 4.0 Architecture
7.1.4 Benefits and Opportunities of Industry 4.0
7.1.5 Challenges and Implications of Industry 4.0
7.2 Internet of Things
7.2.1 Notion of IoT
7.2.2 Role of IoT in Industry 4.0
7.2.3 IoT Applications in Manufacturing and Supply Chain
7.2.4 Challenges and Opportunities of IoT Implementation
7.3 Big Data
7.3.1 Understanding Big Data and Its Characteristics
7.3.2 Big Data Analytics in Industry 4.0
7.3.3 Utilizing Big Data for Predictive Maintenance and Optimization
7.4 Augmented Reality and Virtual Reality
7.4.1 Introduction to AR and VR
7.4.2 Role of AR and VR in Industry 4.0
7.5 Blockchain
7.5.1 Fundamentals of Blockchain Technology
7.5.2 Architecture of Blockchain
7.5.3 Enhancing Security and Trust in Industry 4.0 with Blockchain
7.6 Convergence of IoT, Big Data, AR/VR, and Blockchain in Industry 4.0
7.6.1 Interplay and Integration of Technologies
7.6.2 Use Cases and Examples of Combined Implementations
7.6.3 Benefits and Synergies of Technology Convergence
7.7 Conclusion
References
Chapter 8 Industry 4.0 in Manufacturing, Communication, Transportation, and Health Care
8.1 Introduction
8.1.1 Technological Trends of Industry 4.0
8.2 Diversified Applications of Industry 4.0
8.2.1 Background Analysis
8.2.2 Industry 4.0 in Manufacturing
8.2.3 Industry 4.0 and Communication Services
8.2.4 Industry 4.0 in Transportation
8.2.5 Industry 4.0 in Health Care
8.3 Conclusion
References
Chapter 9 Transforming Education Management in the Industry 4.0 Era: Harnessing the Power of Cloud-Based Blockchain
9.1 Introduction
9.1.1 Contribution
9.2 Revolutionizing Education Through Technology: The Power of Innovation and Connectivity
9.2.1 What Is a Chain of Blocks (Blockchain), and How Does It Work?
9.2.2 Blockchain Utilization
9.2.3 Blockchain in Education: Revolutionizing Learning, Credentialing, and Industry 4.0
9.2.4 Realities: Utilizing Blockchain in Education in the Industry 4.0 Era
9.2.5 Blockchain, Cloud Computing, and Industry 4.0: Transforming Education and Beyond
9.3 Blockchain Application in Education with Industry 4.0: Revolutionizing Learning, Credentialing, and Collaboration
9.4 Blockchain Solution Providers for Education in the Era of Industry 4.0
9.5 Navigating the Challenges: Implementing Blockchain in Education Within the Industry 4.0 Landscape
9.6 A Vision for the Future
9.7 Conclusion
References
Chapter 10 Future Professions in Agriculture, Medicine, Education, Fitness, Research and Development, Transport, and Communication
10.1 Introduction
10.1.1 Artificial Intelligence
10.1.2 Applications of AI
10.1.3 AI Roadmap in Industry 4.0
10.2 Literature Review
10.3 AI Impact on Future Professions
10.4 Role Model of AI in Industry 4.0
10.5 AI in Agriculture
10.5.1 Applying AI in Agriculture
10.5.2 Role of AI in Challenges of Agriculture
10.6 AI in Medicine
10.6.1 Applying AI in Medicine
10.7 Role of AI in Challenges of Medicine
10.8 AI in Education
10.8.1 Applying AI in Education
10.8.2 Role of AI in Challenges of Education
10.9 AI in Fitness
10.9.1 Applying AI in Fitness
10.9.2 Role of AI in Challenges of Fitness
10.10 AI in R&D
10.10.1 Applying AI in R&D
10.10.2 Role of AI in Challenges of R&D
10.11 AI in Transport
10.11.1 Applying AI in Transport
10.11.2 Role of AI in Challenges of Transport
10.11.3 AI in Communication
10.11.4 Applying AI in Communication
10.11.5 Role of AI in Challenges of Communication
10.12 AI Market Growth in Future Profession
10.13 Conclusion
References
Chapter 11 Cybersecurity Issues and Challenges in Quantum Computing
11.1 Introduction
11.2 Cybersecurity Issues and Challenges in Quantum Computer
11.2.1 Quantum Key Distribution
11.2.2 Post-Quantum Cryptography
11.2.3 Quantum Attacks
11.2.4 Algorithmic Design
11.2.5 Standardization
11.2.6 Quantum-Aware Infrastructure
11.2.7 Quantum Security Standards and Policy
11.2.8 Implementation Challenges
11.3 General Solutions
11.4 Conclusion
References
Chapter 12 Security, Privacy, Trust, and Other Issues in Industries 4.0
12.1 Introduction
12.1.1 Integration of Modern Technologies
12.1.2 Globalization and Emerging Issues
12.1.3 Cybersecurity 4.0
12.2 Security Fog Computing
12.2.1 Fog Computing in Industrial Internet of Things
12.2.2 Plant Safety and Security Including Augmented Reality and Virtual Reality
12.2.3 Augmented Reality Security and Privacy Issues
12.2.4 Safety Application
12.3 IoT Challenges
12.3.1 Privacy and Security Concerns of Industrial Internet of Things
12.3.2 Security-Related Issues in Industry 4.0
12.3.3 Side-Channel Attacks
12.3.4 Spear Phishing
12.3.5 Cyberterrorism
12.4 Security Threats and Solutions of Industrial Internet of Things
12.4.1 Spoofing
12.4.2 Data Tempering
12.4.3 Malicious Code Injection
12.5 Conclusion
References
Chapter 13 Designing a Quantum Computer to Gear up Artificial Intelligence for Industry 4.0
13.1 Introduction
13.2 Literature Survey
13.3 Proposed Work
13.3.1 Motivation for Quantum Computers in Industry 4.0 Revolution
13.3.2 Working of the Quantum Computer
13.3.3 Datasets and Qubit Processing Requirement
13.3.4 Optimizations for Quantum Computer
13.3.5 Dataset Complexity
13.3.6 Green Quantum Computing and Its Importance for Industry 4.0
13.4 Simulation Results
13.4.1 Processing Time
13.4.2 Memory Consumption
13.5 Conclusion and Future Work
References
Chapter 14 Opportunities in Neural Networks for Industry 4.0
14.1 Introduction: Why Is Machine Learning Interesting to Industry 4.0?
14.2 Machine Learning
14.3 Challenges in Industry 4.0 That Can Benefit from Using Machine Learning
14.3.1 Fault Detection and Diagnosis
14.3.2 Predicting Remaining Useful Lifetime
14.3.3 Predictive Maintenance
14.3.4 Optimizing Energy Consumption
14.3.5 Cybersecurity
14.3.6 Soft Sensors
14.4 Some Cases of Success Deploying ML in Industry 4.0
14.4.1 Detecting Defects in Sanitary Ware with Deep Learning
14.4.2 Detection of Anomalies in Embedded System Using Electrical Signature
14.5 Conclusions and Final Remarks
References
Chapter 15 A Smarter Way to Collect and Store Data: AI and OCR Solutions for Industry 4.0 Systems
15.1 Introduction
15.2 Background
15.3 Architecture of Wireless Extraction of Display Panel
15.3.1 Block Diagram
15.3.2 Modular Diagram
15.3.3 Equations Applied
15.4 ESP32 Cam Module
15.4.1 Why Is the ESP32-CAM-MB USB Programmer Preferred over the FTDI Programmer?
15.4.2 So How Is the ESP32-CAM-MB USB Programmer Used?
15.5 Wireless LAN Network Setup
15.6 Optical Character Recognition for Text Detection and Text Recognition
15.6.1 KerasOCR’s Text Detector – CRAFT Model
15.6.2 Training Custom KerasOCR Models
15.6.3 EasyOCR Architecture
15.6.4 Training of Custom EasyOCR Model
15.6.5 Real-Time Streaming Using IP Webcam
15.6.6 Processing the Data
15.7 Working of the Model
15.7.1 Creating a Class for Handling Web Camera Stream
15.7.2 Using the Web Camera Stream Class
15.7.3 Detecting Texts or Selecting Regions of Interest
15.7.4 Cropping Selected ROIs from an Image
15.7.5 Performing Optical Character Recognition on the Cropped Images
15.8 Application GUI
15.9 Conclusion
References
Index
EULA