Cloud-IoT Technologies in Society 5.0

List of Advisers/Recommenders
Preface
Reading Path
Content of This Book
Contents
About the Authors
Acronyms
Chapter 1: Convergence of Cloud-IoT, Industry 4.0 and Society 5.0
	1.1 Introduction
		1.1.1 Smart Factory: The Metaphor of Industry 4.0
		1.1.2 Indispensability of Industry 4.0
		1.1.3 Key Technology Factors of Industry 4.0
	1.2 Role of CC in Advancement of Industry 4.0
		1.2.1 Significance of CC in BFSI
		1.2.2 Benefits and Applications of CC in BFSI
	1.3 Intelligent Machine/Deep Learning Approaches and Societal Developments
	1.4 Cloud-IoT and Societal Developments
		1.4.1 Advantages of Cloud-IoT Based Societies
		1.4.2 Challenges of Cloud-IoT for Societal Development
		1.4.3 Goals of Industry 4.0 in Society 5.0 Developments
	1.5 Benevolent Contributions of Industry 4.0 to Society 5.0
		1.5.1 The Future and Society 5.0
	1.6 Conclusions
	References
Chapter 2: A Glimpse of Techno-Psychological Perspective of Society 5.0
	2.1 Introduction
	2.2 Literature Review
	2.3 Theoretical Framework of Social Psychology
	2.4 How Does Technology Influences SP?
		2.4.1 Technophilia
			2.4.1.1 Internet Addiction (IA) or IA Disorder (IAD)
		2.4.2 Technophobia
	2.5 Psychological Models of Technophobia
	2.6 Pros and Cons of Techno-Psychological Effects in Modern Societies
	2.7 Conclusions
	References
Chapter 3: Behavior and Sentiment Analysis of Smart Digital Societies Using Deep Machine Learning Technologies
	3.1 Introduction
	3.2 Theoretical Background
	3.3 Information Processing of Smart Digital Societies Using Deep Machine Learning Techniques
		3.3.1 Social Data Processing
	3.4 Information Management of Smart Digital Societies Using Operative Processing Techniques
		3.4.1 The Flow of Data in Smart City
		3.4.2 Information Management for Smart Cities
	3.5 Smart Digital Society’s Data Analysis and Evaluation Approaches
		3.5.1 Framework of Behavior/Sentiment Data Analysis and Evaluation Approaches
		3.5.2 Implementation Details of DL Based Sentiment Data Analysis Approaches
		3.5.3 Implementation Details of DL Based Behavior Evaluation Approaches
	3.6 Conclusions
	References
Chapter 4: Multi-Access Edge Computing for 5G Networks in Cloud-IoT Integrated Environment
	4.1 Introduction
	4.2 Theoretical Foundations and Related Work Descriptions
	4.3 The Described Algorithms and Methodology
		4.3.1 Key Issues and the Corresponding Open Issues
		4.3.2 Architecture of Enabling Edge Applications
			4.3.2.1 Edge Enabler Server (EES)
			4.3.2.2 Edge Enabler Client
			4.3.2.3 Edge Configuration Server (ECS)
			4.3.2.4 Application Client
			4.3.2.5 Edge Application Server (EAS)
			4.3.2.6 The Edge Reference Points (EDGE-1 to EDGE-8)
			4.3.2.7 The Cardinality Rules, Clauses, and Commonly Used Values
		4.3.3 Procedures and Information Flows for Service, Requests, and Response Provisioning
			4.3.3.1 Service Provisioning in Multi-Access 5G Networks
			4.3.3.2 Provisioning Request in Multi-Access 5G Networks
			4.3.3.3 Provisioning Response in Multi-Access 5G Networks
	4.4 Registering Edge-Enabler Clients and Server in Multi-Access 5G Networks
		4.4.1 Edge Enabler Client Registration in Multi-Access 5G Networks
			4.4.1.1 Edge Enabler Client Registration Request
			4.4.1.2 Edge Enabler Client Registration Response
		4.4.2 Edge Application Server Registration
			4.4.2.1 Edge Application Server Registration Request
			4.4.2.2 Edge Application Server Registration Response
		4.4.3 Edge Enabler Server Registration
			4.4.3.1 Edge Enabler Server Registration Request
			4.4.3.2 EES Registration Response
	4.5 Conclusions
	References
Chapter 5: Discovery and Location Reporting of Multi-Access Edge Enabled Clients and Servers for 5G Networks
	5.1 Introduction
	5.2 Edge Application Server Discovery for Multi-Access Edge Computing in 5G Networks
		5.2.1 Edge Application Server Discovery Request
		5.2.2 Edge Application Server Discovery Response
	5.3 User Equipment Location Reporting API
		5.3.1 Request-Response Model
		5.3.2 Subscribe-Notify Model
		5.3.3 Detection of UE Location from the 3GPP System
		5.3.4 Location Reporting API Request and Response
	5.4 Results and Discussions
		5.4.1 Edge Enabler Server Communication with Different Network Functions
		5.4.2 The Snapshots of the Linux Interface and Working of Simulator in Multi-Access 5G Network
	5.5 Conclusions
	References
Chapter 6: Enhancing the Concert of M-health Technologies in Smart Societies Using Cloud-IoT-Based Distributive Networks
	6.1 Introduction
	6.2 Theoretical Foundations & Literature Review
	6.3 The Proposed Model
		6.3.1 Architecture of Providing IoT and e-health Platforms in Smart Cities
		6.3.2 System Components
			6.3.2.1 Proposed Layers
		6.3.3 Security Model
		6.3.4 Integration of Responsive Technologies in Smart Cities
	6.4 Results and Discussions
		6.4.1 Challenges of Implementing M-health Technologies in Smart Cities
		6.4.2 Effectiveness and Environmental Assessment
	6.5 Conclusions
	References
Chapter 7: Supervision of Communication and Control Services in Societies of Smart Cities Using Sheltered Cloud-Based Confirmation and Access Techniques
	7.1 Introduction
	7.2 The Objectives and New Generation Challenges of Cloud-IoT Integrated Investigation Systems
		7.2.1 The Primary Objectives
		7.2.2 Present Challenges
	7.3 Admittance Control Mechanism of Cloud-IoT Services
		7.3.1 Virtual Machine’s Security
		7.3.2 Various Operations and Governance of Services in Cloud-IoT Environment
	7.4 Security Issues for Cloud-IoT Integrated Communication Systems and Servers
		7.4.1 Security Concerns of Cloud-IoT Server Communications in IRTMCCS
		7.4.2 Probable Architecture of Safe Cloud-IoT-Based Communication Model
		7.4.3 Secure Cloud-IoT Integrated Test Setup Model
		7.4.4 Performance Analysis and Result Comparison of Described Cloud-IoT Integrated IRTMCCS with Other Systems
		7.4.5 Executing the Results of Described IRTMCCS
	7.5 Conclusions
	References
Chapter 8: Life Quality Improvement in Smart City Societies Using Cloud–IoT and Deep Machine Learning (CIDML) Technologies
	8.1 Introduction
	8.2 Theoretical Foundations
	8.3 Objectives of Life Quality Improvements in Smart Societies
	8.4 Smart City Life Quality Improvement Related Problems
	8.5 Described Work Model
		8.5.1 Traffic Surveillance in Smart City Societies
		8.5.2 Accident Detection and Death Prevention in Smart City Societies
		8.5.3 Solving Parking Problems of Smart City Societies Using Sensors and ML Techniques
		8.5.4 Waste Management and Garbage Collection in Smart City Societies
		8.5.5 Implementation Details
	8.6 Conclusions
	References
Chapter 9: Multiple Disease Infection Prediction in Smart Societies Using Intelligent Machine Learning Techniques
	9.1 Introduction
	9.2 Background and Theoretical Foundations
	9.3 The Tools, Technologies, and Algorithms
		9.3.1 The Python, Pandas and their Libraries
		9.3.2 The Numpy, MatPlot, Scilit, Tkinetr, SQLite, and their Libraries
		9.3.3 The Machine Learning Algorithms
		9.3.4 The Steps and Implementation Details of Described Algorithms
			9.3.4.1 The Steps of Described Algorithms
			9.3.4.2 Implementation Details of Described Algorithms
	9.4 Experimental Discussions
		9.4.1 Implementation of Typhoid/Pneumonia/Viral Fever/Covid Detection for Severe Symptoms
			9.4.1.1 Import Python Libraries for Performing Pre-processing Tasks
			9.4.1.2 Dividing Medical Data into Test, Train, and Validation Data Sets
			9.4.1.3 Define the Path Variables for the Input Medical Datasets
			9.4.1.4 Design of Functions for Performing Pre-processing on the Input Data
			9.4.1.5 Call the Defined Function of Sect. 9.4.1.4 Using Different Path Variables
			9.4.1.6 Performing the On-Hot-Encoding and Printing the Shape of NumPy Array
			9.4.1.7 Visualization of Medical Data and Images Using ML Techniques
			9.4.1.8 Defining Checkpoints and Layers
			9.4.1.9 Add and Import Libraries to Build Convolution Layer of Neural Network
			9.4.1.10 Building the Layers of Convolution Neural Network
			9.4.1.11 Providing Training to Convolution Neural Network
			9.4.1.12 Visualizing Output Metrics of the Trained Model
			9.4.1.13 Save the Output
			9.4.1.14 Classify the Medical Image Using Saved Data
			9.4.1.15 Transfer Learning
		9.4.2 Implementation of Corona Virus Detection Part for Mild Symptoms
			9.4.2.1 The Splitting of Training Tests
			9.4.2.2 Visualizing Training and Test Datasets
	9.5 Results and Discussions
		9.5.1 Discussions of Pneumonia and Normal Chests Classifications
		9.5.2 Discussions of Covid-19 Virus Detection for Mild Symptoms
	9.6 Conclusions
	References
Chapter 10: Societal Opinion Mining Using Machine Intelligence
	10.1 Introduction
	10.2 Theoretical Background of Social Opinion Mining (SOM)
	10.3 Mathematical Model of SOM
		10.3.1 Fuzzy Logic-Based Mathematical Model of SOM
		10.3.2 Linear Algebra Based Mathematical Model of SOM
	10.4 Effective Machine Learning Tools for SOM
		10.4.1 Clustering Technique (CLT)
		10.4.2 Naïve-Bayes Classifier (NBC)
		10.4.3 Artificial Neural Network (ANN)
		10.4.4 Firefly Algorithm (FA)
		10.4.5 Rough Set (RS) Classifiers
		10.4.6 Support Vector Machine (SVM) Classifiers
		10.4.7 Decision Tree (DT)
		10.4.8 Ensemble Classifier (EC)
		10.4.9 Random Forest (RF)
		10.4.10 Deep Learning (DL) Algorithms
	10.5 Experimental Results and Discussions
		10.5.1 The Datasets
		10.5.2 Features and Linguistic Patterns Used
		10.5.3 Comparative Mining Efficacy of Different ML Techniques
	10.6 Conclusions
	References
Appendix I
Appendix II
Appendix III
Appendix IV
Appendix V
Appendix VI
Appendix VII
Appendix VIII
Appendix IX
Appendix X
Appendix XI
Appendix XII
Index