Digital Transformation: Industry 4.0 to Society 5.0 (Disruptive Technologies and Digital Transformations for Society 5.0)

Preface
Introduction
Contents
Editors and Contributors
Abbreviations
1 Evolution of Industry 4.0 and Its Fundamental Characteristics
	1 Introduction
		1.1 Industry 4.0 Introduction
		1.2 Industry 4.0 Definitions
		1.3 Benefits of Industry 4.0
		1.4 Motivations Behind the Evolution of Industry 4.0
	2 Industry 4.0 Concepts, State of Arts, and Challenges
		2.1 Basic Components of Industry 4.0
		2.2 Characteristics of Industry 4.0
		2.3 State of Arts
		2.4 Conceptualizing the Fourth Industrial Revolution
		2.5 Goals to Consummate Industry 4.0
		2.6 Drivers of Industry 4.0
		2.7 Implementation Challenges of Industry 4.0
	3 Methodologies in Industry 4.0
		3.1 Validating Technologies/Base Technologies of Industry 4.0
		3.2 Nine Technology Peers of Industry 4.0
		3.3 Architectural Design of Industry 4.0
		3.4 Artificial Intelligence in Industry 4.0
		3.5 Processes and Interaction in Industry 4.0
	4 Applications, Use Cases, and Projects of Industry 4.0
		4.1 Influence of 5G Technologies on Industry 4.0
		4.2 5G Tech Support for Industry 4.0
		4.3 Industry 4.0 Application Scenarios Accredited by 5G
	References
2 Transportation System Using Deep Learning Algorithms in Industry 4.0 Towards Society 5.0
	1 Introduction
	2 Deep Learning Techniques/Algorithms
		2.1 Recursive Neural Network
		2.2 Recurrent Neural Network (RNN)
		2.3 Convolution Neural Network
		2.4 Deep Generative Network
	3 Transportation Network Representation Using Deep Learning
	4 Various Domains that are Being Revolutionized by Deep Learning
		4.1 Self-Driving Cars
		4.2 Traffic Congestion Identification and Prediction
		4.3 Predicting Vehicle Maintenance Needs
		4.4 Public Transportation Optimization
	5 Architecture of Convolutional Neural Network (CNN) Model
		5.1 High-Resolution Data Collection
		5.2 CNN for Crash Predict
	6 Traffic Flow Prediction
	7 Urban Traffic Flow Prediction
	8 Open Research Challenges and Future Directions
	9 Conclusion
	References
3 A Brief Study of Adaptive Clustering for Self-aware Machine Analytics
	1 Introduction
	2 Clustering
		2.1 Types of Clustering
	3 Traditional Clustering Algorithm versus Bio-inspired Clustering
	4 Self-aware Clustering
	5 Adaptive Clustering for Industry 4.0
		5.1 Adaptive Clustering in Mobile Computing
		5.2 Adaptive Clustering in Wireless Network
		5.3 Adaptive Clustering in IoT
		5.4 Adaptive Clustering in Cloud
		5.5 Role of Clustering in Machine Analytics
		5.6 Importance of Adaptive Clustering for Self-aware in Machine Analytics
	6 Result and Discussion
	7 Conclusion
	References
4 Managing Healthcare Data Using ML Algorithms and Society 5.0
	1 Introduction
	2 Skin Cancer
		2.1 Human Skin Cancer
		2.2 Obstacles to Detecting Skin Lesions
		2.3 Literature Survey
	3 Methodology
		3.1 Image Preprocessing
		3.2 Median Filter
		3.3 Lesion Segmentation
		3.4 Feature Extraction
		3.5 Feature Reduction
		3.6 Image Classification
	4 Digital Health Using Federated Learning
		4.1 Federated Learning\'s Statistical Challenges
		4.2 Federated Learning Communication Efficiency
		4.3 Security and Privacy
		4.4 Multiple-Party Computation with Security
		4.5 Privacy Differential
		4.6 Applications
	5 Communal Issues that Concern Various Applications of ML in Medicine
		5.1 Legislation
		5.2 Interpretability and Explainability
		5.3 Privacy and Anonymity
		5.4 Ethics and Fairness
	6 Conclusion
	References
5 Cloud Computing—Everything as a Cloud Service in Industry 4.0
	1 Introduction
		1.1 Introduction to Cloud Computing
		1.2 Why We Need Cloud Computing?
	2 Different Services in Cloud Computing
		2.1 Infrastructure as a Service: [IaaS]
		2.2 Platform as Service: [PaaS]
		2.3 Software as a Service: [SaaS]
	3 Different Cloud Models
		3.1 Public Cloud
		3.2 Private Cloud
		3.3 Hybrid Cloud
		3.4 Multi Cloud
	4 Applications of Cloud
		4.1 Cloud in Business Sector
		4.2 Cloud in Education System
		4.3 Cloud in Medical and Healthcare
		4.4 Cloud in Software Development
	5 Comparison of Various Cloud Platforms
		5.1 Resource Allocation on All Models
	6 Conclusion
	References
6 Glimpse of Cognitive Computing Towards Society 5.0
	1 Introduction
		1.1 A Glimpse into the Evolution off Societies
		1.2 The Need for Society 5.0
		1.3 The Working of Society 5.0 as A Solution to Social Problems
		1.4 Attaining Society 5.0
	2 The Implementation and Impact of Society 5.0
		2.1 Infrastructure
		2.2 Mobility
		2.3 Health
		2.4 Education
		2.5 Manufacturing
		2.6 Agriculture
		2.7 Energy
		2.8 Disaster Prevention
		2.9 Food Products
		2.10 Fintech
		2.11 Tourism
		2.12 Cyber Space
	3 Cognitive Computing in a Nutshell
		3.1 Characteristics of Cognitive Computing
		3.2 The Differences Between Artificial Intelligence and Cognitive Computing
		3.3 Advantages of Cognitive Computing
		3.4 Caveats of Cognitive Computing
	4 Use Case Scenarios of Cognitive Computing at Work
		4.1 Intelligent Assistant-Cora (Royal Bank of Scotland-RBS)
		4.2 Personal Travel Planner by WayBlazer
		4.3 Cafewell—A Healthcare Concierge by Welltok
		4.4 Fantasy Football Team Decision Maker by Edge up Sports
	5 Conclusion
		5.1 Future Scope and Discussion
	References
7 Big Data Analytics in Industry 4.0 in Legal Perspective: Past, Present and Future
	1 Introduction
	2 The Basic Flow of Big Data\'s Past, Present, and Future
		2.1 The Origins of Data
		2.2 The Dawn of Statistics
		2.3 Modern Data Storage in Its Infancy
		2.4 Business Intelligence\'s Beginnings
		2.5 1964
		2.6 Data Centres Are Getting Started
		2.7 The Internet\'s First Years
		2.8 Big Data\'s Earliest Concepts
		2.9 Big Data in the Twenty-First Century
	3 From Industry 4.0 to Society 4.0
	4 From Industry 4.0 to Market 4.0
		4.1 Phases of Marketing 4.0
	5 Literature Review
	6 The Legal Constraints of Big Data Analytics
	7 Analysis of Data Protection Principles in the Context of Big Data
	8 Big Data and Black Data Affairs
		8.1 Advantages
		8.2 Disadvantages
	9 Legal Standpoint—Comparative Reflection
		9.1 United States of America
		9.2 United Kingdom
		9.3 India
		9.4 Brazil
		9.5 Bangladesh
		9.6 Australia
		9.7 Conclusion
	References
8 Unified Architectural Framework for Industrial Internet of Things
	1 Introduction
	2 The Technologies Associated with IIoT
		2.1 Industry 4.0
		2.2 Cyber-Physical Systems (CPS)
	3 Industrial Automation and Control Systems (IACS)
	4 Literature Review
	5 IoT to IIoT
	6 Basic Overview of IIoT Architecture
	7 IIoT Architecture
	8 IIoT Framework
	9 IIoT Framework Application
		9.1 Industrial IoT Platforms (IIoT)
		9.2 Conclusion
	References
9 Human–Robot Coordination and Collaboration in Industry 4.0
	1 Introduction
		1.1 Robots at Workplace
		1.2 Inclusion of Robot Workforce
		1.3 Organizational Benefits of Including Robot Workforce
	2 Literature Review
		2.1 Table of Literature Review–Human–Robot Collaboration and Co-Ordination
	3 Human–Robot Coordination and Collaboration
		3.1 Drivers for Human–Robot Coordination and Collaboration
		3.2 Barriers for Human–Robot Coordination and Collaboration
	4 Human–Robot Coordination and Collaboration Towards Organization Performance
		4.1 Organizational Performance
	5 Framework for Human–Robot Coordination and Collaboration
		5.1 Framework for Human–Robot Coordination and Collaboration Towards Organization Performance
	6 Implications
	7 Conclusion and Future Research Scope
	References
10 Revolutionizing the Techno-Human Space in Human Resource Practices in Industry 4.0 to Usage in Society 5.0
	1 Introduction: What is Artificial Intelligence?
		1.1 Literature Review
		1.2 The AI Present Scenario
		1.3 Racing to AI in Business
		1.4 The HR World
		1.5 Technology and HR
	2 AI Ecosystem
		2.1 Trends in the AI Ecosystem
		2.2 AI Roadmap Development
		2.3 Utilizing the AI Roadmap
		2.4 Enhancing the HR Processes Using AI
		2.5 Collaborative Intelligence in Recruitment Function: All About Estimations!
		2.6 AI in Learning and Development Function of Human Resources Management
	3 Collaborative Artificial Intelligence (CAI) Conceptual Background
		3.1 Business and Collaborative Artificial Intelligence
		3.2 Collaborative Artificial Intelligence in Business–Case 1
		3.3 Challenge Problems in CAI Scenarios
	4 What is Society 5.0?
		4.1 IOT-CAI-Smart Cities
		4.2 IOT and Urban Knowledge
	5 Conclusions
	References
11 An Architecture of Cyber-Physical System for Industry 4.0
	1 Introduction
		1.1 Cyber-Physical Systems
		1.2 Industry 4.0
		1.3 CPS Industry Compatibility with 4.0
		1.4 Characteristics
		1.5 Inquiry on the Design of CPS
	2 Literature Review
		2.1 Implementation of CPS Technique
		2.2 Case Study: Developing Own CPS
		2.3 Case Study: KPIs Implementation
	3 Information and Operational Technology
		3.1 Operational Technology Support
		3.2 Information Technology Support
	4 Convergence of IT and OT in IIoT
		4.1 IT and OT Are no Longer Separate Fields of Study
		4.2 How Will IoT Embedded with IT and OT?
	5 CPS Functions and Applications at a Glance
	6 Electronic Platform
		6.1 Necessity of an Electronic Platform
		6.2 Developing a Digital Business Technology Infrastructure
		6.3 Eye on Electronic Platform
	7 Conclusion
	References
12 Machine Learning and Deep Learning Algorithms for Alzheimer Disease Detection and its Implication in Society 5.0
	1 Introduction
	2 Statutory Liquidity Ratio (SLR)
	3 Data Set
	4 Internet of Things (IoT)
	5 Healthcare, Artificial Intelligences
		5.1 Types of Learning
		5.2 Importance of Deep Learning
	6 Convolution Neural Network
	7 Conclusion
	References
13 Deep Convolutional Extreme Learning Machine with AlexNet-Based Bone Cancer Classification Using Whole-Body Scan Images
	1 Introduction
	2 Literature Review
	3 Materials and Methods
		3.1 Dataset Description
		3.2 Whole-Body Scanning
		3.3 System Model
	4 Case Study
	5 Conclusion
	References
14 Adaptive Clustering for Self-aware Machine Analytics
	1 Introduction
		1.1 Clustering
		1.2 Cluster Analysis
		1.3 Measures of Similarity
		1.4 Clustering Algorithms
		1.5 Structure of Adaptive Clustering
		1.6 Types of Adaptive Clustering
		1.7 The Problem of Adaptive Clustering
	2 Real-Time Applications of Ada Clustering
		2.1 Cargo Transportation Logistics
		2.2 Adaptive Clustering for Telecom Companies
		2.3 Tracing Large-Scale MPI (Message Passing Interface) Applications
		2.4 Application of Ontologies for Capturing the Semantics of the Problem Domain
		2.5 Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing
	3 Artificial Intelligence (AI)
		3.1 Classification of AI
	4 Self-awareness
		4.1 Self-awareness in Software Systems
		4.2 Self-adaptivity in Software Systems
		4.3 Perception and Learning Affordances
	5 Sensory-Motor Learning
		5.1 Objects
		5.2 Actions
		5.3 Effects
		5.4 Affordance Learning
		5.5 Use Affordances for Problem-Solving
	6 Learning Actions and Plans
		6.1 Multiple Action Selection System
	7 Human–Robot Interaction
		7.1 Human–Human Joint Action
		7.2 Human–Robot Joint Action
	8 Self-aware Decision Making
		8.1 Decision System
	9 Applications
	10 Conclusion
	References