Artificial Intelligence in Cyber-Physical Systems (Wireless Communications and Networking Technologies) 9781032164830, 9781032164847, 9781003248750, 1032164832

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of Figures
List of Tables
Preface
Editors
Contributors
About This Book
Chapter 1 Mood-Detection Based Media Recommendation
1.1 Introduction
1.1.1 Mood Taxonomy
1.2 Related Works
1.2.1 Media Recommendation
1.2.2 Text-Based Emotion Detection
1.2.3 Image-Based Emotion Detection
1.3 Proposed System
1.3.1 Text-Based Emotion Detection
1.3.2 Emotion Detection Using Feature Extraction
1.3.3 Media Recommendation Algorithm
1.3.4 Result
References
Chapter 2 AI- and IoT-Based Body Sensor Networks for Healthcare System: A Systematic Review
2.1 Introduction
2.2 Literature Review
2.3 Features of BSN
2.4 Importance of BSN in IoT
2.5 Architecture of IoT Based on BSN in Healthcare System
2.6 AI Technology Using Deep Learning Methods for Sensor Data
2.7 Applications of BSN
2.7.1 Medical Field
2.7.2 Disability Services
2.7.3 Sports and Fitness
2.7.4 Military Services
2.7.5 Entertainment
2.8 Challenges of BSN and IoT in Healthcare Technologies
2.8.1 Scalability
2.8.2 Security
2.8.3 Privacy and Confidentiality
2.9 Conclusion
References
Chapter 3 On the Convergence of Blockchain and IoT for Enhanced Security
3.1 Introduction
3.2 Motivation
3.3 IoT Security
3.3.1 IoT Background
3.3.2 IoT Security Risks
3.3.2.1 Security Risks at Sensing Layer
3.3.2.2 Security Risks at Network Layer
3.3.2.3 Security Risks at Middleware Layer
3.3.2.4 Security Risks at Application Layer
3.4 Blockchain Technology
3.5 Integration of Blockchain with IoT
3.5.1 Architecture of the System
3.6 Challenges of the Integrated Technologies
3.7 Conclusion
References
Chapter 4 Artificial Intelligence in Cloud Computing
4.1 Introduction
4.2 Cyber-Physical System
4.2.1 CPS Structure
4.2.2 CPS Components
4.2.3 CPS Characteristics
4.2.4 Domains of CPS
4.2.5 CPS Challenges
4.2.6 CPS Security
4.3 Cloud Computing Technologies
4.4 AI Technologies
4.4.1 Detection of Malware Using ML Algorithms
4.5 Conclusions
References
Chapter 5 Current and Future Trends in an Intelligent Transportation System with Applications of AI
5.1 Introduction
5.1.1 Internet of Things (IoT)
5.1.2 Machine Learning (ML)
5.1.3 Sensor Technology
5.1.3.1 In-Vehicle Sensors
5.1.4 IoT and ML in Making Our Current Transportation 'Smart'
5.2 Trends of Smart Transportation System
5.2.1 Route Optimization-Navigation
5.2.2 Parking
5.2.3 Lights
5.2.4 Accident Detection and Prevention
5.2.5 Road Anomalies Detection
5.2.6 Infrastructure
5.3 Case Study: Smart Transportation: The Case of Karachi
5.3.1 Smart Transportation: Concept
5.3.2 The Case of Karachi
5.3.2.1 Proposed Framework for Karachi
5.3.2.2 Concluding Remarks
5.4 Conclusion
References
Chapter 6 Intelligent 5G Networks and Augmented Virtual Reality in Smart Transportation
6.1 Introduction
6.2 Smartness of 5G Network
6.3 5G Intelligent Transportation
6.3.1 Vehicular Communication
6.3.2 Automated Driving
6.3.3 Intelligent Navigation
6.3.4 Information Society on Road Safety
6.3.5 Isolated Traffic Light Control
6.4 In-Vehicle and Wide Area Connectivity
6.5 Signalized Road Network Improvement
6.6 Issues and Challenges
6.7 Conclusion and Future Scope
References
Chapter 7 Cyber-Physical Security Issues and Challenges Using Machine Learning and Deep Learning Technologies
7.1 Introduction
7.2 Literature Survey
7.3 Overview of IoT Techniques Used for Security
7.3.1 Advantages of IoT
7.3.2 Limitation of IoT
7.4 Cloud, Edge, and Fog Techniques
7.4.1 Cloud
7.4.1.1 Advantage of Cloud in Security
7.4.1.2 Limitations of Cloud in Security
7.4.2 Edge
7.4.2.1 Advantages of Edge in Security
7.4.2.2 Disadvantages of Edge in Security
7.4.3 Fog
7.4.3.1 Advantages of Fog in Security
7.4.3.2 Disadvantages of Fog in Security
7.5 ML Methods Used for Security
7.6 DL Methods Used for Security
7.7 Security Application Areas
7.8 Conclusion
References
Chapter 8 Brain MRI Image Active Contour Segmentation for Healthcare Systems
8.1 Introduction
8.1.1 Need for Segmentation of Brain MRI in Health Care Systems
8.1.2 Importance of De-Noising in Brain MR Images
8.2 Literature Review
8.2.1 Thresholding
8.2.1.1 Otsu's Thresholding
8.2.1.2 Local Thresholding
8.2.1.3 Threshold for Histograms
8.2.2 Segmentation Based on the Edges
8.2.3 Segmentation by Region
8.2.3.1 Region Growing
8.3 Proposed Methodology
8.3.1 Chan-Vese ACM
8.3.2 Proposed Model for Segmentation
8.4 Results and Discussion
8.5 Conclusion
References
Chapter 9 Machine Learning Techniques Applied to Extract Objects from Images: Research Issues Challenges and a Case Study
9.1 Introduction
9.2 Motivation
9.3 Challenges and Issues
9.4 Architecture
9.5 Classifying Object
9.5.1 Techniques for Object Detection
9.5.2 Region Proposal Networks
9.5.2.1 One-Stage Object Detection Algorithms
9.5.2.2 Two-Stage Object Detection Algorithms
9.6 Applications of Machine Learning
9.6.1 Machine Learning in Computer Vision
9.6.2 Object Detection and Dimensionality Prediction Using Machine Learning and Deep Learning
9.7 Image Classification
9.8 Case Studies
9.8.1 Reading Image
9.8.2 Resize Image
9.8.3 Data Augmentation
9.8.3.1 Techniques-Data Augmentation
9.9 Image Classification Techniques
9.9.1 SVM Models
9.9.2 Decision Tree Models
9.9.3 k-Nearest Neighbor Model
9.9.4 Artificial Neural Network (ANN) Models
9.9.5 CNN Model
9.10 Conclusion
References
Chapter 10 AI and IoT-Enabled Technologies and Applications for Smart City
10.1 Introduction
10.1.1 Internet of Things
10.1.2 Artificial Intelligence
10.2 IoT-Enabled Applications
10.2.1 Smart Cities
10.2.2 Retail and Logistics
10.2.3 Healthcare
10.2.4 Smart Energy
10.2.5 Transportation System
10.2.6 Security and Emergencies
10.2.7 Environmental Monitoring
10.2.8 Smart Agriculture
10.3 Internet of Things and Artificial Intelligence: A Literature Survey
10.3.1 Literature Survey on IoT for Smart City
10.3.2 Literature Survey on AI for Smart City
10.4 AI and IoT-Enabled Technologies
10.4.1 Radio Frequency Identification (RFID)
10.4.2 Near-Field Communication (NFC)
10.4.3 Bluetooth
10.4.4 Zensys Wave (Z-Wave)
10.4.5 Light Fidelity (Li-Fi)
10.4.6 Wireless Fidelity (Wi-Fi)
10.4.7 ZigBee
10.4.8 Wireless Smart Utility Network (Wi-SUN)
10.4.9 Cellular Technologies
10.4.10 Long Range Wide Area Network (LoRaWAN)
10.4.11 Low-Power Wireless Personal Area Networks (6LoWPAN)
10.4.12 SigFox Technology
10.4.13 Narrow Band IoT (NB-IoT)
10.5 Things to Be Remembered in AI and IoT-Enabled Smart City
10.5.1 Strengths
10.5.2 Weaknesses
10.5.3 Opportunities
10.5.4 Threats
10.6 Challenges for Deployment of IoT in Smart City
10.7 Conclusion
References
Chapter 11 Blood Cancer Classification with Gene Expression Using Modified Convolutional Neural Network Approach
11.1 Introduction
11.2 Related Work
11.2.1 Speeded-Up-Robust-Feature – Based MCNN
11.2.2 Optimized SURF-Based MCNN
11.2.3 Normalization
11.2.4 Class Separation
11.2.5 Feature Pattern Extraction
11.2.6 Feature Selection
11.2.7 Modified CNN
11.3 Experimental Results
11.4 Conclusion
References
Chapter 12 An Introspective Approach to Fathom Human-Inspired Bipedal Walk Using Gait Analysis for the Matrix of Cyber-Physical System
12.1 Introduction
12.1.1 Human Gait Analysis and Key Terms
12.1.2 Humanoid Model
12.1.3 Design, Implementation of Bipeds
12.1.4 Human Walk Vs Bipedal Walk – Limitations and Constraints
12.1.5 Human Gait Analysis Approach
12.1.6 Chapter Structure
12.1.7 Vision-Based Gait Analysis
12.1.8 CPS and Its Motivation
12.2 Applications of Gait Analysis
12.2.1 Sport Science
12.2.2 Physical Rehabilitation
12.2.3 Clinical Applications
12.2.4 Biometric Applications
12.2.5 Prosthetic Limbs Design
12.2.6 AR/VR Applications
12.2.7 Humanoid Robotics
12.2.8 Human Activity Recognition
12.2.9 Expert Systems
12.2.10 Orthopedic Care
12.3 Gait Analysis New Emerging Security Dimensions in CPS
12.4 Research Challenges and Limitations
12.5 Conclusions and Future Scope
References
Chapter 13 Capacitated Vehicle Routing Problem Using Algebraic Particle Swarm Optimization with Simulated Annealing Algorithm
13.1 Introduction
13.2 Problem Formulation
13.3 Permutation Group Preliminaries
13.3.1 Permutation Group
13.3.2 Abstract Algebraic Operations
13.4 The Proposed Algorithm
13.4.1 Initial Population and Fitness Value
13.4.2 The Proposed Algorithm
13.5 Simulation Study
13.5.1 Simulation Results
13.6 Conclusion
References
Chapter 14 An Innovative Smart IoT Device to Measure and Monitor Patient's Critical Parameters in Hospitals
14.1 Introduction
14.1.1 IoT Implementation in Transforming Business
14.1.2 Giant Energy Management Employing IoT Technology
14.1.3 IoT a Boost in Smart Farming
14.2 Literature Survey
14.3 Proposed System
14.3.1 Prominent Outcomes of the Product
14.4 Result and Discussion
14.4.1 Testing and Performance Analysis
14.5 Conclusion and Future Scope
References
Chapter 15 Health Analysis by Digital Doctor Using Deep Neural Network
15.1 Introduction
15.2 Overview and Problem Statement
15.2.1 Motivation
15.2.2 Contributions of This Work
15.3 Terminology
15.3.1 Natural Language Understanding
15.4 Objectives
15.4.1 To Develop a Video-Calling Web Application
15.4.2 To Develop a Video-Calling Web Application
15.4.3 To Implement Feature Extraction so as to Extract the Patient's Details to Develop a Video-Calling Web Application
15.4.4 To Build a Recommender System Similar for Patients Based on the Features Extracted
15.5 Proposed Method/Algorithm
15.5.1 Problem Definition
15.5.2 Proposed Idea/System
15.6 System Architecture
15.6.1 System Analysis and Design
15.6.2 Requirement Specification
15.6.3 Design and Test Steps
15.6.4 Algorithms and Pseudo Code
15.7 Performance Study
15.7.1 Implementation
15.7.2 Video Calling
15.7.3 Speech to Text
15.7.4 NLP
15.7.5 Recommender System
15.8 Results and Analysis
15.8.1 Summary of Performance Study
15.9 Conclusions and Future Work
References
Chapter 16 AI and IoT in Supply Chain Management and Disaster Management
16.1 Introduction
16.1.1 Supply Chain Management
16.2 What Is IoT?
16.2.1 Role of IoT in SCM
16.2.1.1 Enhance Emergency Preparedness
16.2.2 Benefits of IoT for Management of Supply Chain
16.3 Artificial Intelligence
16.3.1 Artificial Intelligence (AI) in Supply Chain Management (SCM)
16.3.2 How Can AI Be Utilized in the Supply Chain?
16.3.3 AI Possibilities in SCM
16.3.3.1 Improve Human Workforces
16.3.3.2 Supply and Demand Forecasting
16.3.3.3 Inventory Management (Turnover and Wastage)
16.3.3.4 Quality Control and Smart Maintenance
16.3.3.5 Shipping Efficiency
16.3.4 AI's Advantages in the Supply Chain Context
16.3.4.1 Making Informed Decisions
16.3.4.2 Increased Efficiency
16.3.4.3 Competitive Advantage
16.3.4.4 Scaling Organization
16.3.4.5 Customer Satisfaction
16.4 Organizational Transformations Disaster Management
16.5 Role of IoT in Disaster Management
16.5.1 Prevent
16.5.2 Preparation
16.5.3 Response
16.5.4 Recover
16.6 Conclusion
References
Chapter 17 Cyborgs: A Coming Era
17.1 Introduction
17.2 Brain-Computer Interfacing: Cybernetics in the Field of Biotechnology
17.3 Roboroach: The Cyborg Cockroach
17.4 Cyborg Rat
17.5 How Are Humanoid and Robots Different from Cyborgs
17.6 Some Real-Life Cyborgs
17.6.1 Hearing Color: World's Famous Cyborg
17.6.2 Captain Cyborg: Dr. Kevin Warwick
17.7 Advantages and Disadvantages of Cyborgs
17.8 Proposed Work and Methodology
17.9 Result Analysis
17.10 Future of Cyborgs
17.11 Conclusion
References
Index