Artificial Intelligence Applications in a Pandemic: COVID-19

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Preface
Editors
1 Role of Artificial Intelligence in COVID-19
	1.1 Introduction
	1.2 Background
	1.3 Algorithms Used in Artificial Intelligence
		1.3.1 Support Vector Machine (SVM)
		1.3.2 Convoluted Neural Networks (CNN)
		1.3.3 Decision Trees
		1.3.4 K-Nearest Neighbours (KNN)
		1.3.5 Logistic Regression
		1.3.6 Linear Regression
	1.4 Applications of AI for Fighting Covid-19
		1.4.1 Issues and Challenges in AI COVID-19
	1.5 Conclusion
	References
2 Application of 3D Printing in COVID-19
	2.1 Introduction
	2.2 Types of Modeling
		2.2.1 Fused Deposition Modeling (FDM)
		2.2.2 Selective Laser Melting (SLM)
		2.2.3 Electron-Beam Melting
		2.2.4 Laminated Object Manufacturing (LOM)
		2.2.5 Material Jetting (MJ)
		2.2.6 Stereolithography (SLA)
		2.2.7 Binder Jet
	2.3 Components of a 3D Printer
	2.4 Materials Used
	2.5 Applications of 3D Printing in COVID-19
		2.5.1 Nasopharyngeal (NP) Swab
		2.5.2 Face Shield
		2.5.3 Various Masks
			2.5.3.1 N95 Masks
			2.5.3.2 Snorkel Mask Adapter
			2.5.3.3 3D Printed Mask Frames
			2.5.3.4 Mask Extenders
			2.5.3.5 Open-Source 3D Printed Ventilator Device
			2.5.3.6 Hospital Respiratory Apparatus
			2.5.3.7 3D Printed Isolation Wards
			2.5.3.8 Contact-Free Devices
			2.5.3.9 Drone Parts
			2.5.3.10 3D Bioprinting
			2.5.3.11 Antimicrobial Polymers in the COVID-19 Pandemic
	2.6 Conclusion
	References
3 Role of IoT and AI in COVID-19
	3.1 Introduction
	3.2 AI and IoT for Large- and Small-Scale COVID-19 Screening and Monitoring
		3.2.1 Quarantine Tracking
		3.2.2 IoT Q-Band
	3.3 Clinical Decision Support System (CDSS)
		3.3.1 Wearable, Cuffless Blood-Pressure Measuring Devices
	3.4 Internet of Things Buttons for Real-Time Notifications in Hospital
	3.5 IoT-Based Smart Helmet for COVID-19
	3.6 Sanitization Using IoT and AI Technology
	3.7 Ultraviolet Light Surface Disinfection Devices
	3.8 Drones and Other Robots for Spraying Disinfectant
	3.9 IoT-Enabled Smart City During COVID-19
	3.10 Conclusion and Future Scope
	References
4 Potential Contributions of AI Against COVID-19
	4.1 Introduction
	4.2 Current Strategy
		4.2.1 Containment (“Epidemiological Avoidance”)
		4.2.2 Testing Is Critical
	4.3 Role of AI: From Diagnosis to Outcome Predictions
		4.3.1 Isolation: Drone Delivery
		4.3.2 Equipment: 3D Printing
		4.3.3 Care: Intelligent Robot
		4.3.4 Data: Internet of Things (IoT)
		4.3.5 Model: Deep Reinforcement Learning
		4.3.6 Drugs: Generative Design Algorithms
		4.3.7 Radiology: CT Modalities
	References
5 A Comparative Study of COVID-19 Data Analysis Using R Programming
	5.1 Introduction
	5.2 Objective
	5.3 Methods
	5.4 Results
	5.5 Conclusion
	5.6 Key Messages
	References
6 COVID Cases Analysis: Dynamic Animated Plots Using R Programming
	6.1 Introduction
	6.2 Method
	6.3 Results
		6.3.1 Line Plot Animation
		6.3.2 Bar Plot Animation
		6.3.3 Bubble Plot Animation
	6.4 Conclusion
	References
7 Tracking and Analyzing COVID-19 Pandemic Using Twitter and Topic Modelling
	7.1 Introduction
	7.2 What Is Topic Modelling?
	7.3 Related Work
	7.4 Other Ways of Topic Analysis
	7.5 How Can Twitter and Topic Modelling Be Used in Tackling COVID-19?
		7.5.1 Role of Twitter
		7.5.2 Twitter and Covid
		7.5.3 How Can Topic Modelling Help?
	7.6 How Does Topic Modelling Work?
		7.6.1 Data Collection
		7.6.2 Data Cleaning
		7.6.3 Topic Modelling
	7.7 Further Study
	References
8 Artificial Neural Network Application to Analyze 3D Image Printing Using Artificial Intelligence in COVID-19
	8.1 Introduction
		8.1.1 Background
		8.1.2 Problem Formula
		8.1.3 Problem Limit
		8.1.4 Direction
		8.1.5 Divining Annual Research
	8.2 Literature Review
		8.2.1 Image Processing Substance
		8.2.2 Counter-Propagation Neural Network
	8.3 Discussion and Implementation
		8.3.1 Case Analysis
		8.3.2 Data Acquisition Method
		8.3.3 Data Extraction
		8.3.4 Data Normalization
		8.3.5 Numeric Data Accumulation
		8.3.6 Artificial Neural Network Structure
	8.4 Analysis and Simulation
		8.4.1 Sample Preparation
		8.4.2 Learning Activity Simulation
		8.4.3 Learn Rate Effect
		8.4.4 Momentum Effect
		8.4.5 Initial Value Reach Effect
		8.4.6 Regional Composition Effects Towards JSB Sample
	8.5 Conclusion
	8.6 Exercises
	References
9 The Evolution of Emerging Market (EM) Sovereign CDS Spreads During COVID-19
	9.1 Introduction
	9.2 Recent Literature
		9.2.1 Local Risk Factors
		9.2.2 Global and Local Risk Factors
	9.3 Overview of Emerging Markets And COVID-19
	9.4 Methodology
		9.4.1 Stage 1 Estimate, January 2014–June 2019 (Table 9.1)
			9.4.1.1 Data
			9.4.1.2 Specification
			9.4.1.3 Exposure to Global and Regional Risks and Fiscal Fundamentals in Emerging Markets
		9.4.2 Second Estimation Phase, January–June 2020
			9.4.2.1 Data
			9.4.2.2 Specification
			9.4.2.3 Residue Review, March 2020
	9.5 Results
	9.6 Conclusion
	References
10 Prediction of COVID-19 Data Using Business Intelligence Tools
	10.1 Introduction
	10.2 Methodology
		10.2.1 Dataset
		10.2.2 BI Tool
		10.2.3 Machine Learning Environment
		10.2.4 Data Integration
		10.2.5 Visualization of Historical Data
	10.3 Results Interpretation
		10.3.1 Results of Forecast Option
		10.3.2 Results of the Prediction Models Using R
		10.3.3 Results of the Prediction Models Using Python
	10.4 Qualification of the Predictive Models
	10.5 Conclusion
	References
Index