Computer Vision and Internet of Things: Technologies and Applications

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 IoE—Its 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 Wind 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