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 âSolidarityâ
2.4.2 How the âSolidarityâ 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