Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Editors
List of Contributors
Acknowledgement
Part 1 Introduction to Computer Vision and Internet of Things
1. Rise of Computer Vision and Internet of Things
1.1 Introduction
1.2 Evolution of CV and IoT
1.3 Evolving Toward CV and IoT
1.4 Enhancement of IoT Using CV and 5G Technology
1.5 Challenging Issue
1.6 CV and IoT in Real-Time Applications
1.6.1 Autonomous Vehicles
1.6.2 Healthcare System
1.6.2.1 Precise Diagnosis
1.6.2.2 Timely Detection of Illness
1.6.2.3 Facial Recognition
1.6.3 CV-Based Agriculture
1.6.3.1 Drone-Based Monitoring and Smarter Farming
1.6.3.2 Yield Analysis
1.6.3.3 Crop Grading and Sorting
1.6.3.4 Automated Pesticide Spraying and Phenotyping
1.6.3.5 Forest Information
1.6.4 Less Traffic Congestion
1.6.5 Smart Parking
1.6.6 Object Detection and Tracking
1.7 Conclusion
References
2. IoE: An Innovative Technology for Future Enhancement
2.1 Introduction
2.2 IoT and Its Present Perspectives
2.3 IoEβIts Role and Responsibility
2.4 Interplay between IoE and IoT
2.5 Security in IoE
2.6 Role and Importance of IoE
2.7 France: IoE Smart City Pilot
2.8 Conclusion
References
3. An Overview of Security Issues of Internet of Things
3.1 Introduction
3.2 Literature Review
3.3 Smart IoT Devices
3.4 Major Security Issues of IoT Devices
3.5 Threats to Security
3.5.1 Vulnerabilities
3.5.2 Attack
3.6 Purpose of IoT Attacks
3.7 Classification of Intruders
3.8 Conclusion
References
4. Use of Robotics in Real-Time Applications
4.1 Introduction
4.2 Related Work
4.3 Current Challenging Issues
4.4 Different Areas of Robotics in Real-Time Applications
4.5 Conclusion
References
Part 2 Tools and Technologies of IoT with Computer Vision
5. Preventing Security Breach in Social Media: Threats and Prevention Techniques
5.1 Introduction
5.2 Related Research Work
5.3 Importance of Social Media
5.4 Privacy and Network Threats in Social Media
5.4.1 Classic Threats
5.4.2 Modern Threats
5.4.3 Combination Threats
5.4.4 Threats Targeting Children
5.5 Prevention Techniques and Strategies
5.5.1 Never Connect to Open Wi-Fi Networks
5.5.2 Check Before You Post
5.5.3 Limit the Number of People
5.5.4 Avoid Rumors
5.5.5 Use New Authentication Techniques
5.5.6 Use of Antivirus
5.5.7 Make Password Strong
5.5.8 Keep Software Update
5.5.9 Analyze the Setting
5.5.10 Observe Your Children
5.6 Future Scope
5.7 Conclusion
References
6. Role of Image Processing in Artificial Intelligence and Internet of Things
6.1 Introduction
6.2 Image Processing
6.3 AI Solutions for Image Processing
6.3.1 OpenCV
6.3.2 TensorFlow
6.3.3 Keras
6.3.4 VXL
6.3.5 AForge.NET
6.4 Advantages of the Use of AI in Image Processing
6.5 Challenges of AI in Image Processing
6.6 Image Processing in IoT
6.7 Conclusion
References
7. Computer Vision in Surgical Operating Theatre and Medical Imaging
7.1 Introduction
7.2 Evolution of CV
7.3 Medical Imaging Techniques
7.3.1 X-Ray
7.3.2 Computed Tomography (CT)
7.3.3 Magnetic Resonance Imaging (MRI)
7.3.4 Diagnostic Medical Sonography
7.4 Digital Imaging Standards in MI
7.4.1 Digital Imaging and Communication in Medicine
7.4.2 Picture Archiving and Communication System
7.5 CV Algorithms Used in MI
7.5.1 Classification
7.5.2 Localization
7.5.3 Segmentation
7.6 Use Cases AI and CV in MI
7.6.1 Diagnostic Assistance
7.6.2 Screening and Sortation
7.6.3 Monitoring
7.6.4 Charting
7.7 Application of CV in Imaging
7.7.1 Cardiovascular Image Analysis
7.7.2 Oncology
7.7.3 Ophthalmology
7.7.4 Neurology
7.7.5 Orthopedic
7.7.6 Emergency Medicine
7.7.7 MRI Brain Interpretation
7.7.8 X-Ray Analysis
7.7.9 Surgery
7.8 Critical Success Factor
7.8.1 Accuracy
7.8.2 Seamless Integration
7.8.3 Training
7.8.4 Productivity Metrics
7.8.5 Data Security
7.9 CV in Surgery and MI
7.9.1 Understanding of Surgical Procedure
7.9.2 Object Detection
7.9.3 Object Tracking or Computer-Assisted Navigation
7.10 Deployment Issues of Vision-Based Systems
7.11 Conclusion
References
Part 3 IoT with Computer Vision for Real-Time Applications
8. Self-Driving Cars: Tools and Technologies
8.1 Introduction
8.2 Tools and Technologies
8.2.1 Car Navigation System
8.2.2 Location System
8.2.3 Electronic Map
8.2.4 Map Matching
8.2.5 Global Path Planning
8.2.6 Environment Perception
8.2.7 Laser Perception
8.2.8 Radar Perception
8.2.9 Visual Perception
8.3 Vehicle Control
8.4 Vehicle Control Method
8.5 Comparison between Camera and LIDAR
8.6 Disadvantages of Self Driving Cars
8.7 Legal Issue
8.8 Conclusion
References
9. IoT and Remote Sensing
9.1 Background of Internet of Things
9.2 Background of Remote Sensing
9.3 Process of Remote Sensing
9.4 IoT-Based Remote Sensing Sensor Systems
9.4.1 IoT-Enabled Passive Sensors
9.4.2 IoT-Enabled Active Sensors
9.5 Remote Sensing and Its Types
9.6 Data Acquisition and Data Interpretation
9.6.1 Data Acquisition
9.6.2 Data Interpretation
9.7 Application Areas of IoT in Remote Sensing
9.7.1 Mineral Exploration
9.7.2 Disaster Management
9.7.3 History and Archeology
9.7.4 Environmental Observations
9.7.5 Land Cover Analysis
9.8 IoT and GIS
9.8.1 Real-Time GIS and IoT
9.8.2 Capabilities of Real-Time GIS Platform
9.9 IoT and GPS
9.10 Future Scope
9.11 Conclusion
References
10. Synthetic Biology and Artificial Intelligence
10.1 Introduction
10.2 CRISTA Method of Machine Learning
10.2.1 Data Labeling
10.2.2 Prediction Targeted Activity
10.2.3 Prediction Non-Targeted Activity
10.2.4 Data Scattering
10.2.5 Selecting Data
10.2.6 Setting Data into Machine-Readable Form
10.2.7 Algorithm Selection
10.2.8 Predicting CRISPR Target Activity: GNL Scorer
10.2.9 Insight in CRISPR ML Models and Way of Minimization Errors
10.3 Possible Consequences of CRISPR Technology
10.3.1 Recent Investigations on Unnatural Nuclear Pairs and Exciting Near Future
10.3.2 Enormous Potential of CRISPR Technology and Its Ethic Controversies
10.4 Conclusions
References
11. Innovation and Emerging Computer Vision and Artificial Intelligence Technologies in Coronavirus Control
11.1 Introduction
11.2 Background
11.3 Computer Vision (CV) Technology
11.4 Computer Vision for Covid-19 Diagnosis
11.4.1 X-Ray Radiography (CXR)
11.4.2 Computed Tomography (CT)
11.5 Computer Vision for Covid-19 Prevention
11.5.1 Face Mask Detection
11.5.2 Thermal Imaging
11.5.3 Drones in Covid-19
11.5.4 Germ Scanning
11.6 Face Mask Detection Framework
11.7 AI Vision for Covid-19 Treatment
11.7.1 Disease Progression Score
11.7.2 Depth Cameras and DL
11.7.3 Support Vaccination Development
11.8 AI Vision for Ventilation Management in Intensive Care Unit (ICU)
11.9 Future of Emerging AI Technologies
11.10 Conclusion
References
12. State of the Art of Artificial Intelligence in Dentistry and Its Expected Future
12.1 Introduction
12.2 The Main Challenge in the Use of Virtual Reality in Dentistry
12.3 Fundamentals of Artificial Intelligence and Its Performances in Dentistry
12.4 Some Examples of the Application of Artificial Intelligence in Dentistry
12.5 Applications in Dental and Maxillofacial Radiology
12.6 Other Applications
12.7 Conclusions
References
13. Analysis of Machine Learning Techniques for Airfare Prediction
13.1 Introduction
13.2 Background
13.3 Looking into the Data by the Team
13.4 Data Collection and Preprocessing
13.4.1 Dataset
13.4.2 Understanding Data and Preprocessing.
13.4.3 Extracting Derived Features from the Data
13.4.4 Handling Categorical Data and Feature Encoding
13.4.5 Analysis of Dataset
13.5 Analysis of Machine Learning Techniques
13.5.1 Random Forest
13.5.2 Linear Regression
13.5.3 Decision Tree
13.5.4 K-Nearest Neighbor (KNN) Algorithm
13.6 Algorithms Implementation and Evaluation
13.6.1 Random Forest Algorithm
13.6.2 Linear Regression Algorithm
13.6.3 Decision Tree Algorithm
13.6.4 K-Nearest Neighbor Algorithm
13.7 Limitations
13.8 Conclusion
13.9 Future Work
References
Part 4 Challenging Issues and Novel Solutions
14. CapsNet and KNN-Based Earthquake Prediction Using Seismic and WindΒ Data
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.3.1 KNN Method
14.3.2 CapsNet
14.4 Experimental Setup and Result Discussion
14.4.1 Datasets Used
14.4.2 Result Comparison
14.5 Conclusion
References
15. Computer-Aided Lung Cancer Detection and Classification of CT Images Using Convolutional Neural Network
15.1 Introduction
15.2 Literature Survey
15.3 Proposed System
15.3.1 Flow of Proposed System
15.3.2 Morphological Operation
15.3.3 Design of the Proposed System
15.4 Experimental Setup of the Proposed System
15.4.1 Dataset
15.4.2 Pre-Processing
15.4.3 Image Segmentation
15.4.4 Convolutional Neural Network (CNN)
15.4.5 Proposed CNN Model for Lung CT Classification
15.4.6 Data Augmentation
15.5 Performance Evaluation
15.6 Experimental Results
15.7 Comparison
15.8 Discussion
15.9 Conclusion
Acknowledgments
References
16 Real-Time Implementations of Background Subtraction for IoT Applications
16.1 Introduction
16.2 Background Subtraction: A Short Preliminary Overview
16.3 GPU Implementations
16.4 Embedded Implementations
16.5 Specific Architectures
16.5.1 Digital Signal Processor
16.5.2 Very Large Scale Integration
16.5.3 Field-Programmable Gate Array
16.6 Parallel Implementations
16.7 Programming Languages
16.8 Low-Complexity Strategies
16.9 Fog Computing and Edge Computing
16.10 Conclusion
Acknowledgments
References
17. The Role of Artificial Intelligence in E-Health: Concept, Possibilities, and Challenges
17.1 Introduction
17.2 Artificial Intelligence (AI)
17.3 Types of Artificial Intelligence
17.4 AI in E-Health
17.5 Limitations of Artificial Intelligence (AI)
17.5.1 The Confusion Matrix
17.5.2 ROC Curve
17.6 Literature Survey
17.7 Current Era of Artificial Intelligence and Its Impact on E-Health
17.8 Artificial Intelligence Techniques
17.8.1 Machine Learning
17.8.2 Types of Machine Learning
17.8.2.1 Supervised Learning
17.8.2.2 Unsupervised Learning
17.8.2.3 Semi-Supervised Learning
17.8.3 Machine Learning in E-Health
17.8.4 Deep Learning
17.8.4.1 Deep Learning in E-Health
17.9 Healthcare Data and Databases
17.10 Ideas, Possibilities, and Challenges
17.11 Conclusions
References
Index