Optimized Computational Intelligence Driven Decision-Making: Theory, Application and Challenges (Industry 5.0 Transformation Applications)

Chapter 1 Emergence of Advanced Computational Intelligence Coupled with Smart Environment
	1.1 Introduction
	1.2 Background Works
	1.3 Integrated Smart Environment
	1.4 Proposed Models for Smart Intelligent Environment
		1.4.1 Smart Cities
			1.4.1.1 Garbage Monitoring System
			1.4.1.2 Accident Sensing System
		1.4.2 Smart Healthcare
		1.4.3 Smart Homes
			1.4.3.1 Weather Monitoring IoT-Based System
			1.4.3.2 Air Pollution Monitoring IoT-Based System
			1.4.3.3 Noise Pollution Monitoring IoT-Based System
			1.4.3.4 Forest Fire Detection IoT-Based System
	1.5 IoT Architecture
		1.5.1 Perception Layer
			1.5.1.1 Privacy and Verification
			1.5.1.2 Network Availability
			1.5.1.3 Service Integrity
			1.5.1.4 Jamming
			1.5.1.5 Eavesdropping
			1.5.1.6 Replay Attack
			1.5.1.7 Man-in-the-Middle (MITM) Attack
			1.5.1.8 Denial of Service (DoS)
			1.5.1.9 Tag Cloning
			1.5.1.10 Take Off i.e. Spoofing
			1.5.1.11 Device Tampering
			1.5.1.12 Outage of Nodes
			1.5.1.13 Leakage of Information
		1.5.2 Network Layer
			1.5.2.1 Selective Forwarding
			1.5.2.2 Sybil Attack
			1.5.2.3 Sinkhole/Black Hole Attack
			1.5.2.4 Wormhole
			1.5.2.5 Attacks of Hello Flood
		1.5.3 Support Layer
			1.5.3.1 Data Tampering
			1.5.3.2 Unauthorized Access
			1.5.3.3 DoS Attack
		1.5.4 Application Layer
			1.5.4.1 Sniffer
			1.5.4.2 Injection
			1.5.4.3 Session Hijacking
			1.5.4.4 Distributed Denial of Service (DDoS)
			1.5.4.5 Social Engineering
	1.6 Smart Environment and Advanced Computational Intelligence
	1.7 Advanced Computational Intelligences: Possible Uses in Smart Environment
		1.7.1 Smart Infrastructure and Green Economy
		1.7.2 Resolving the Issue of Sustainability
	1.8 Conclusion
	References
Chapter 2 Machine Learning-Enabled Integrated Information Platform for Educational Universities
	2.1 Introduction
	2.2 Cloud-Based Web Application for University
		2.2.1 Overview
		2.2.2 Working Principles
		2.2.3 Cloud Computing Techniques
		2.2.4 Cloud Database
		2.2.5 Advantages of Cloud-Based Database
		2.2.6 How Cloud-Based Website is Better than Non-Cloud Websites
	2.3 Integrated Information Platform of Indian Universities Using Machine Learning
		2.3.1 Overview
		2.3.2 Applications of Machine Learning in Integrated Information Platform
		2.3.3 What are Uses of Machine Learning in This Platform
	2.4 Applications Used to Designed This Web Platform
		2.4.1 Front-End Development
		2.4.2 Backend Development
	2.5 Analysis Result
		2.5.1 Home Page
		2.5.2 Sign Up/Log In Page
		2.5.3 Explore University
		2.5.4 Recommended Comparison
		2.5.5 Manually Comparison
	Conclusion
	References
Chapter 3 False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things
	3.1 Introduction
	3.2 Literature Review
	3.3 Technical Methodology
		3.3.1 Autoencoders (AE) used for Identifying False Data
			3.3.1.1 Encoder Network
			3.3.1.2 Decoder Network
		3.3.2 Denoising Autoencoder (DAE) used for Data Recovery
	3.4 Proposed Model for Detecting False Data and its Correction
		3.4.1 Attack Detection Algorithm
		3.4.2 Denoising Autoencoders-Based Data Cleaning
		3.4.3 Algorithm for False Data Detection and Correction
	3.5 Complexity Analysis of Proposed Model
	3.6 Advantages of the Model
	3.7 Future Scope and Limitations of the Proposed Model
	3.8 Conclusion
	References
Chapter 4 Fake News Detection: Restricting Spreading of Misinformation Using Machine Learning
	4.1 Introduction
	4.2 Scope of False News Detection
	4.3 Main Highlights of the Analysis
		4.3.1 Approach
		4.3.2 Naive Bayes
		4.3.3 Support Vector Machine (SVM)
	4.4 A Novel Model for False News Detection
		4.4.1 Aggregator
		4.4.2 News Authentication
		4.4.3 News Suggestion/Recommendation System
	4.5 Literature Review
	4.6 Results and Analysis
	4.7 Conclusion
	References
Chapter 5 Adaptability, Flexibility, and Accessibility Through Telemedicine
	5.1 Introduction
	5.2 Related Works
	5.3 Proposed Model for Remote Health Monitoring System
		5.3.1 Microcontroller and Sensor
	5.4 Benefits of the Proposed Model
	5.5 Constraints of the Proposed Model
	5.6 Conclusion
	5.7 Future Works
	References
Chapter 6 Crop Prediction by Implementing Machine Learning in an IoT-Based System
	6.1 Introduction
	6.2 Literature Review
	6.3 Proposed Model for Crop Prediction
	6.4 Results and Analysis
	6.5 Challenges Faced
	6.6 Advantages of the Proposed Model
	6.7 Disadvantages of the Proposed Model
	6.8 Conclusion
	References
Chapter 7 Relevance of Smart Management of Road Traffic System Using Advanced Intelligence
	7.1 Introduction
	7.2 Related Works
		7.2.1 Traffic Lighting System
		7.2.2 Smart Parking System
		7.2.3 Vehicle Theft Detection System
	7.3 Proposed Model of Traffic Management System
		7.3.1 Traffic Lighting System
		7.3.2 Smart Parking System
		7.3.3 Vehicle Theft Detection System
	7.4 Role of AI in Traffic Management
	7.5 Conclusion and Future Works
	References
Chapter 8 Visualization of Textual Corpora Using Social Network Analysis
	8.1 Introduction
		8.1.1 Importance of Character Networks
		8.1.2 Visualization of Dynamic Networks
		8.1.3 Contributions
	8.2 Related Literature
		8.2.1 Visualization of Social Networks
		8.2.2 Community Discovery (CD)
		8.2.3 Community Discovery in Dynamic Networks
	8.3 Proposed Method
		8.3.1 Basic Idea of Algorithm
		8.3.2 Life Cycle of Dynamic Communities
		8.3.3 Notation
		8.3.4 Algorithm
	8.4 Implementation and Results
		8.4.1 Pre-Processing the Data
		8.4.2 Generating Graph
		8.4.3 Community Detection
		8.4.4 Score-Similarity Measure
		8.4.5 Visualization of Network
		8.4.6 Visualization of Snapshots
		8.4.7 Analysis of Results
	8.5 Conclusion and Future Work
	References
Chapter 9 Autonomous Intelligent Vehicles: Impact, Current Market, Future Trends, Challenges, and Limitations
	9.1 Introduction
	9.2 The Global Impact of the AV Industry
	9.3 Role of Machine Learning in Autonomous Vehicles
	9.4 Significance of the AV Industry in Various Sectors
		9.4.1 Traffic Management
		9.4.2 Roads and Urban Infrastructure
		9.4.3 Logistics
		9.4.4 Healthcare
		9.4.5 Job Market
		9.4.6 Environment and Society
	9.5 Current Market and Future Trends in AV Industry
		9.5.1 Tesla and Waymo: Two Key Players in the Autonomous Vehicle Industry
		9.5.2 AI Datasets and ML-Based Development
		9.5.3 Use of Sensors and Other Hardware
	9.6 Challenges and Limitations
		9.6.1 Data Privacy
		9.6.2 Cybersecurity
		9.6.3 Policies and Regulations
		9.6.4 Ethical Issues
		9.6.5 Other Common Challenges
	9.7 Conclusion
	References
Chapter 10 Role of Smart and Predictive Healthcare in Modern Society
	10.1 Introduction
	10.2 Healthcare System
	10.3 Role of Predictive Analytics in Healthcare
		10.3.1 Disease Prevention
		10.3.2 Early Detection
		10.3.3 Diagnosis
		10.3.4 Treatment Planning
		10.3.5 Resource Optimization
	10.4 Application of IoT in Healthcare
		10.4.1 Home Healthcare
		10.4.2 m-Health
		10.4.3 Electronic Health Record (EHR)
	10.5 IoT Based Healthcare Management Framework
		10.5.1 Data Collection Layer
		10.5.2 Connectivity Layer
		10.5.3 Cloud Layer
		10.5.4 Application Layer
		10.5.5 Consumer Layer
	10.6 Future Recommendations for Research
	10.7 Conclusion
	References
Chapter 11 An Analytical Study on Depression Detection Using Machine Learning
	11.1 Introduction
	11.2 Literature Survey
	11.3 Proposed System
	11.4 Challenges of Machine Learning in Depression Detection
	11.5 Conclusion and Future Work
	References
Chapter 12 Revolutionizing Healthcare: Empowering Faster Treatment with IoT-Powered Smart Healthcare
	12.1 Introduction
		12.1.1 Main Contribution of the Paper
			12.1.1.1 Using IoT to Track Abnormalities
			12.1.1.2 Emergency Alerts for Patients
			12.1.1.3 Ambulance Notification
			12.1.1.4 Patient Medical History and Family Contacts
			12.1.1.5 Early Access to Treatment
	12.2 Scope/Motivation
	12.3 Literature Survey
	12.4 Smart Technology
		12.4.1 IoT-Enabled Healthcare
		12.4.2 Importance of Smart Healthcare System
	12.5 Methods and Materials
		12.5.1 Smart Sensors Deployed in Model
			12.5.1.1 Heart Rate Sensor
			12.5.1.2 Blood Pressure Sensor
			12.5.1.3 Body Temperature Sensor
			12.5.1.4 Accelerometer
			12.5.1.5 Gyroscope
			12.5.1.6 Magnetometer
			12.5.1.7 Barometric Pressure Sensor
			12.5.1.8 Oximetry Sensor
			12.5.1.9 Bioimpedance Sensor
		12.5.2 Working of Model
	12.6 Result
	12.7 Conclusion
	References
Chapter 13 Machine Learning Algorithms for Initial Diagnosis of Parkinson’s Disease
	13.1 Overview of Parkinson’s Disease
	13.2 Scope
	13.3 Related Works
	13.4 Comparative Analysis of Parkinson’s Disease
	13.5 Pros and Cons Using ML Algorithms
	13.6 Conclusion and Future Works
	13.7 Bibliography
	References
Chapter 14 Towards a Sustainable Future: Harnessing the Power of Computational Intelligence to Track Climate Change
	14.1 Introduction
	14.2 Artificial Intelligence and Climate Change Adaptation
	14.3 Related Works
	14.4 Comparative Analysis of Technological Frameworks to Handle Climate Crisis
		14.4.1 Artificial Intelligence for Drought Assessment and Forecasting
		14.4.2 Carbon Dioxide Capture by Help of Synthetic Intelligence
		14.4.3 Discussion of the Case Studies
	14.5 Future Scope of Climatic Crisis Handling with AI
	14.6 Conclusion
	References
Chapter 15 Impact of Computational Intelligence and Modeling in Tackling Weather Fluctuation
	15.1 Introduction
	15.2 Objective
	15.3 Causes of Climate Crisis
		15.3.1 Greenhouse Gases
		15.3.2 Fossil Fuels
		15.3.3 Deforestation
		15.3.4 Agriculture and Livestock
		15.3.5 Industrial Processes
		15.3.6 Transportation
	15.4 Significance of AI and Modeling on Climate Crisis
		15.4.1 Design and Optimization of Renewable Energy Systems
		15.4.2 Develop AI-Driven Solutions to Reduce Deforestation
			15.4.2.1 Detection and Monitoring of Deforestation
		15.4.3 Forest Fire Prediction and Prevention
		15.4.3 Forest Fire Prediction and Prevention 15.4.3.1 Forest Restoration
			15.4.3.1 Forest Restoration
		15.4.4 Create AI-Driven Solutions to Improve Agricultural Practices to Reduce Carbon Emissions
		15.4.5 Precision Farming
		15.4.6 Sustainable Livestock Production
		15.4.7 Carbon Sequestration
		15.4.8 Energy Efficiency
		15.4.9 Analyze and Predict the Effects of Climate Change
		15.4.10 Studying Ecosystems
		15.4.11 Predicting Human Health Impacts
		15.4.12 Predicting Economic Impacts
	15.5 Plastic Waste Detection Model
		15.5.1 Convolutional Neural Networks (CNNs)
			15.5.1.1 Image Classification
			15.5.1.2 Object Detection
			15.5.1.3 Segmentation
			15.5.1.4 Time-Series Analysis
			15.5.1.5 Accuracy
			15.5.1.6 Efficiency
			15.5.1.7 Flexibility
	15.5.2 Deep Belief Networks (DBNs)
		15.5.2.1 Feature Extraction
		15.5.2.2 Object Detection
		15.5.2.3 Transfer Learning
	15.5.3 Advantages of Using DBNs for Plastic Waste Detection
		15.5.3.1 Robustness
		15.5.3.2 Scalability
		15.5.3.3 Flexibility
	15.6 Forest Fire Prediction Models Using AI
		15.6.1 How ML Models Can Help to Prevent Forest Fire
			15.6.1.1 Early Warning Systems
			15.6.1.2 Predictive Modeling
			15.6.1.3 Image Analysis
			15.6.1.4 Real-Time Monitoring
			15.6.1.5 Improved Firefighting Techniques
			15.6.1.6 Early Detection
	15.7 Results
		15.7.1 Plastic Waste Detection
		15.7.2 Forest Fire Prediction Model
			15.7.2.1 Data Preprocessing
			15.7.2.2 Training and Validation
	15.8 Conclusion
	References
Index
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