Artificial intelligence and machine learning for COVID-19

Preface
Contents
Chapter 1: Smart Technologies for COVID-19: The Strategic Approaches in Combating the Virus
	1.1 Introduction
		1.1.1 Scope of the Study
	1.2 Related Works
		1.2.1 Radio Frequency Identification
		1.2.2 Wireless Sensor Network
		1.2.3 Contact Tracing
		1.2.4 COVID-19 Laboratory Tests
		1.2.5 Thoracic Imaging
	1.3 Smart Technology Applications
		1.3.1 The Strategic Approaches
			1.3.1.1 Prepare and Be Ready
			1.3.1.2 Protect and Reduce Transmission
			1.3.1.3 Identify and Treat
			1.3.1.4 Innovate and Learn
		1.3.2 Smart Technologies for COVID-19
			1.3.2.1 Smart HandWashing and Sanitizer
			1.3.2.2 Non-Contact Infrared Thermometer
			1.3.2.3 Smart Wireless Biosensors
			1.3.2.4 VivaLnk Temperature Sensor
			1.3.2.5 Kinsa Smart Thermometer
			1.3.2.6 EarlySense
			1.3.2.7 Autonomous Vehicle Technology (AVT)
			1.3.2.8 Robots
			1.3.2.9 Artificial Intelligent
			1.3.2.10 Drones
		1.3.3 Importance and Benefit
			1.3.3.1 Social Media and Wireless Communication Technology
			1.3.3.2 Digital Health Technology
			1.3.3.3 Autonomous Vehicle Technology
		1.3.4 Challenges and Limitations
			1.3.4.1 Social Media
			1.3.4.2 Contact Tracing
			1.3.4.3 Drones
			1.3.4.4 AVT
	1.4 Conclusion
	References
Chapter 2: A Review on COVID-19
	2.1 Introduction
		2.1.1 Origin
	2.2 Research on Safety Precautions
		2.2.1 Law and Limit of Quarantine [5]
		2.2.2 A Mathematical Framework to Optimize Border Control to Stop the Global Spread [7]
		2.2.3 Result
		2.2.4 H1N1 Case Study Model Calibration
		2.2.5 Shortcomings
	2.3 Testing
		2.3.1 Viral Test
		2.3.2 Antibody Test
			2.3.2.1 Detection of COVID-19 Using Chest Radiography Images [9]
			2.3.2.2 Computational Prediction of Protein Structure Associated with COVID-19
		2.3.3 AlphaFold
		2.3.4 Using a Neural Network to Predict Physical Properties [11]
	2.4 Research on Treatment
		2.4.1 “Solidarity”
		2.4.2 How the “Solidarity” Trial Works
		2.4.3 Convalescent Plasma Therapy
		2.4.4 Results
		2.4.5 Effects of CP Transfusion
	2.5 Impact on World Economy
		2.5.1 Effect on Environment
	2.6 Conclusion
	References
Chapter 3: Artificial Intelligence in face of the Novel CoronaVirus
	3.1 Introduction
		3.1.1 Related Work
		3.1.2 AI Platform for the COVID-19 Pandemic
	3.2 Field of Artificial Intelligence Application in the COVID-19 Pandemic
		3.2.1 Identification Measures
		3.2.2 Detection Measures
		3.2.3 Prevention Measures
		3.2.4 Prediction Measures
		3.2.5 Therapeutic Measures
	3.3 Datasets for AI Applications in the COVID-19 Pandemic
		3.3.1 Data Types
		3.3.2 Data Acquisition
			3.3.2.1 Smartphone-Based Method
			3.3.2.2 Biomedical Equipment-Based Method
	3.4 AI Methods in the COVID-19 Pandemic
		3.4.1 Designing and Building AI Algorithms in Screening for COVID-19
			3.4.1.1 Machine Learning
			3.4.1.2 Deep Learning
				AlexNet
				ResNet
				DenseNet
				VGG
				Capsule Networks
				U-Net
				Inception Network
		3.4.2 Evaluating AI Models in Screening the COVID-19
			3.4.2.1 Size of COVID-19 Data
			3.4.2.2 Augmented Data Usage
			3.4.2.3 Types of Modality
			3.4.2.4 Transfer Learning
			3.4.2.5 Combined AI Algorithm
	3.5 Challenges and Limitations
	3.6 Concluding Remarks
	Appendix 1
	Appendix 2
	References
Chapter 4: Digital Transformation and Emerging Technologies for COVID-19 Pandemic: Social, Global, and Industry Perspectives
	4.1 Introduction
	4.2 Artificial Intelligence
		4.2.1 AI Approaches for COVID-19
		4.2.2 Future Directions
	4.3 Internet of Things (IoT)
		4.3.1 IoT Approaches for COVID-19
		4.3.2 Future Directions
	4.4 Cloud, Edge, and Fog Computing
		4.4.1 Cloud and Fog Computing Approaches for COVID-19
		4.4.2 Future Directions
	4.5 Deep Learning
		4.5.1 Deep Learning Approaches for COVID-19
		4.5.2 Future Directions
	4.6 Big Data Analytics
		4.6.1 Big Data Analytics
		4.6.2 The Need for Big Data Analytics
		4.6.3 Stages Involved in Big Data Analytics
		4.6.4 Types of Big Data Analytics
		4.6.5 Tools Used in Big Data Analytics
		4.6.6 Application Areas Using Big Data Analytics
	4.7 Blockchain Technology
	4.8 Unmanned Aerial Vehicles
	4.9 Robotics
	4.10 Industry 4.0
	4.11 Conclusion
	References
Chapter 5: A Deep Analysis and Prediction of COVID-19 in India: Using Ensemble Regression Approach
	5.1 Introduction
	5.2 Related Work
		5.2.1 Covid-19 Analysis in India
		5.2.2 Machine Learning for COVID-19
	5.3 Analysis and Visualization of COVID-19 Effects in India
	5.4 Regression Analysis of COVID-19 in India
		5.4.1 Gradient-Boosting Regressor
		5.4.2 Extra-Trees Regressor
		5.4.3 Ada-Boost Regressor
		5.4.4 Random-Forest Regressor
		5.4.5 Results
	5.5 Conclusion
	References
Chapter 6: Image Enhancement in Healthcare Applications: A Review
	6.1 Introduction
	6.2 Applications of Image Enhancement
		6.2.1 Super-Resolution Applications
		6.2.2 Reconstruction Applications
		6.2.3 Contrast Enhancement Applications
		6.2.4 Denoising Applications
		6.2.5 Other Applications
	6.3 Conclusion
	References
Chapter 7: Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease
	7.1 Introduction
	7.2 Literature Survey
	7.3 Proposed Methodology
		7.3.1 Preprocessing
		7.3.2 Segmentation
			7.3.2.1 Fuzzy C-Means Algorithm
		7.3.3 Feature Extraction
			7.3.3.1 Feature Extraction Using GLCM
		7.3.4 Classification Using 3D_ImpCNN
	7.4 Performance Analysis
	7.5 Conclusion
	References
Chapter 8: Drone-Based Social Distancing, Sanitization, Inspection, Monitoring, and Control Room for COVID-19
	8.1 Introduction to Drone-Based System
		8.1.1 What Is Drone-Based System
		8.1.2 Rules and Regulations for Drone-Based Systems
		8.1.3 Features of Drone-Based System in Smart Healthcare System
		8.1.4 Organization of Work
	8.2 COVID-19 Pandemics
		8.2.1 Types of COVID-19
		8.2.2 History of Coronavirus/COVID-19
		8.2.3 Effects of COVID-19 Pandemic to Mankind
		8.2.4 Pandemic Prevention Methods
			8.2.4.1 Social Distancing
			8.2.4.2 Personal Hygiene
			8.2.4.3 Crowd Preventing and Alert Mechanisms
			8.2.4.4 COVID-19 Diagnosis, Treatment, and Prevention
			8.2.4.5 COVID-19 Symptoms
			8.2.4.6 COVID-19 Treatment
			8.2.4.7 COVID-19 Precautions
	8.3 Literature Review
	8.4 Case Studies
	8.5 Conclusion and Discussion
	References
Chapter 9: Application of AI Techniques for COVID-19 in IoT and Big Data Era: A Survey
	9.1 Introduction
		9.1.1 Comparison to Other Survey
		9.1.2 Contribution and Scope of the Survey
	9.2 Incorporating AI in Combating COVID-19
		9.2.1 Medical Therapy and Biomedics in AI
		9.2.2 Diagnosis and Detection Using AI
		9.2.3 Infoveillance and Epidemiology
		9.2.4 Forecast and Identifying Using AI
	9.3 Incorporating Big Data in Combating COVID-19
		9.3.1 Discovery of Vaccine and Drugs
		9.3.2 Predicting the Outbreak
		9.3.3 Treatment and Diagnosis
		9.3.4 Tracking the Spread of Virus
	9.4 Incorporating IoT in Combating COVID-19
		9.4.1 Telehealth
		9.4.2 Smart Detection
		9.4.3 Gadget
	9.5 Incorporating Cloud in Combating COVID-19
		9.5.1 Diagnosis
		9.5.2 Medical Applications
		9.5.3 Tracking and Detection
		9.5.4 Cloud Services
	9.6 Difficulties and Recommendations
		9.6.1 Lacking Dataset
		9.6.2 Security
		9.6.3 Regulating the Outbreak
	9.7 Discussion
		9.7.1 AI
		9.7.2 Big Data
		9.7.3 IoT
		9.7.4 Cloud
	9.8 Conclusion
	References
Chapter 10: Application of IoT, AI, and 5G in the Fight Against the COVID-19 Pandemic
	10.1 Introduction
	10.2 Usage of IOT in the Fight Against COVID-19
		10.2.1 Patient Monitoring and Tracking
		10.2.2 Elderly Care
		10.2.3 Identifying and Managing COVID-19 Patient
		10.2.4 Medications
		10.2.5 Data Storage and Management
		10.2.6 Enforcing Lockdown and Social Distancing
		10.2.7 Spraying Disinfectant and Public Announcements
	10.3 The Use of Artificial Intelligence in the Fight Against COVID-19
		10.3.1 Predictions
		10.3.2 Drug Development
		10.3.3 Medical Diagnosis, Screening, and Contact Tracing
		10.3.4 Fake News Detection
	10.4 Uses of 5G in the Fight Against COVID-19
		10.4.1 Thermal Imaging
		10.4.2 Telemedicine
		10.4.3 Monitoring
		10.4.4 Education, Training, and Counselling
		10.4.5 Robots and Drones
	10.5 Challenges
	10.6 Successful Applications of IOT, AI, and 5G in the Fight Against COVID-19
		10.6.1 Artificial Intelligence
		10.6.2 5G Technology
		10.6.3 IOT
	10.7 Conclusion
	References
Chapter 11: AI Techniques for Resource Management During COVID-19
	11.1 Introduction
	11.2 Resource Utilization
		11.2.1 Resource Allocation
			11.2.1.1 Need for Resource Allocation
	11.3 Strategic Planning
		11.3.1 What Is Strategic Planning?
			11.3.1.1 Strategic Management and Strategic Execution
			11.3.1.2 Steps Involved in Strategic Planning and Management
			11.3.1.3 Strategic Map
		11.3.2 Attributes of a Good Planning Framework
			11.3.2.1 Algorithm Foundations for Business Strategy
			11.3.2.2 Algorithm for Hard Business Problems
	11.4 AI-Based Resource Management Techniques
		11.4.1 Implications of Strategic Managers of Cognitive Simplification of Problems
		11.4.2 Algorithms for Resource Allocation
	11.5 Models for Strategic Planning in Industry
		11.5.1 Strategic Planning Process Model
		11.5.2 Issue-Based Strategic Planning Model
		11.5.3 Alignment Strategic Model
		11.5.4 Scenario Strategic Planning
		11.5.5 Organic Strategic Planning Model
	11.6 AI and ML Techniques for Resource Management
	11.7 Conclusion
	References
Index