Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

Preface
Contents
About the Editors
Forecasting and Visualization
Coronavirus Spreading Forecasts Based on Susceptible-Infectious-Recovered and Linear Regression Model
	1 Introduction
	2 Pandemic Theory
	3 SIR Model
		3.1 Simulation of SIR (the Susceptible-Infected-Removed) Model Using R Packages
	4 Linear Regression Model
	5 Comparison of SIR and Linear Regression Model
	6 Conclusion
	References
Virus Graph and COVID-19 Pandemic: A Graph Theory Approach
	1 Introduction
	2 Motivation
	3 Related Work
		3.1 Variable Set
		3.2 Variable Graph
		3.3 Edge V-Graph
		3.4 Vertex V-Graph
		3.5 N-Partite V-Graphs
		3.6 Bipartite V-Graph
	4 Graph Theoretical Model
		4.1 Virus Graph I
		4.2 Virus Graph II
		4.3 Virus Graph III
		4.4 Virus Graph IV
	5 Growth Rate
	6 Types of Growth
		6.1 One–One Growth
		6.2 One-P Growth
		6.3 One—All Growth
	7 COVID-19
	8 Growth Rate of COVID -19
		8.1 Complexity
		8.2 Limitations
	9 Conclusions and Future Outlook
	References
Nonparametric Analysis of Tracking Data in the Context of COVID-19 Pandemic
	1 Introduction
	2 Homogeneity Measure for Samples Without Ties
	3 Homogeneity Measure for Samples with Ties
	4 Experiments and Results
	5 Conclusions and Future Work
	References
Visualization and Prediction of Trends of Covid-19 Pandemic During Early Outbreak in India Using DNN and SVR
	1 Introduction
	2 Literature Survey
	3 Preliminaries
		3.1 Deep Neural Network (DNN)
		3.2 Support Vector Regression (SVR)
		3.3 Dataset Description
	4 Implementation Requirements
	5 Covid-19 Pandemic—India with Respect to World
	6 Representation and Prediction with DNN Model
	7 Representation and Prediction with SVR Model
	8 Error Computation with SVR and DNN Model
	9 Discussion
	10 Conclusion and Future Work
	References
Diagnosis and Predictions of COVID-19
The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach
	1 Introduction
	2 Related Works
	3 Generative Adversarial Networks and Deep Transfer Learning
		3.1 GAN Architecture
		3.2 Deep Transfer Learning Networks
	4 Datasets Characteristics
	5 Proposed Model Architecture
	6 Experimental Results
		6.1 Deep Transfer Model’s Accuracy and Performance Metrics Without GAN
		6.2 Confusion Matrices for Deep Transfer Models with GAN
		6.3 Performance Metrics for Deep Transfer Models with GAN
	7 Conclusions and Future Works
	References
COVID-19 Data Analysis and Innovative Approach in Prediction of Cases
	1 Introduction
	2 State-of-Art
	3 COVID-19 Intelligent Data Analysis
		3.1 Identifying and Plotting Global Hotspots
		3.2 Analysis of COVID-19 Spread in India
		3.3 Comparison of Spread of COVID-19 in India with Neighboring Countries
		3.4 Comparison of COVID-19 Spread in India with Global Hotspots
		3.5 Comparison of COVID-19 Spread in the States of India
	4 Prediction of Future COVID-19 Cases in India
	5 Conclusions
	References
Detection of COVID-19 Using Chest Radiographs with Intelligent Deployment Architecture
	1 Introduction
	2 Literature Review
	3 Proposed Methodology
	4 Result and Discussion
	5 Deployment Architecture
	6 Conclusion
	References
COVID-19 Diagnostics from the Chest X-Ray Image Using Corner-Based Weber Local Descriptor
	1 Introduction
	2 Literature Review
	3 The Proposed CWLD Scheme
		3.1 X-Ray Image Contrast Enhancement
		3.2 Points of Interest Localization
		3.3 WLD Extraction
		3.4 COVID-19 Diagnostics
	4 Experimental Results and Analysis
		4.1 Database Description
		4.2 Experimental Setup
		4.3 CWLD Based on Differential Excitation ξ(xc)
		4.4 CWLD Based on Direction of Gradient α(xc)
		4.5 CWLD Based on Combined ξ(xc) and α(xc)
		4.6 Results Analysis
		4.7 Comparison with Previous Studies
	5 Conclusions
	References
Why Are Generative Adversarial Networks Vital for Deep Neural Networks? A Case Study on COVID-19 Chest X-Ray Images
	1 Introduction
	2 Background
		2.1 Small Data and Augmentation
		2.2 Generative Adversarial Networks
	3 Methodologies
		3.1 Stochastic Gradient Decent (SGD) Optimizer
		3.2 Improved Adam Optimizer
		3.3 Training and Generating the GAN
	4 Experimental Results: Discussion and Analysis
		4.1 Dataset Description and Experiments Setup
		4.2 Experiments Scenarios
	5 Conclusions
	References
Artificial Intelligence (AI) Against COVID-19
Artificial Intelligence Against COVID-19: A Meta-analysis of Current Research
	1 Introduction
	2 Meta-Analysis
		2.1 Journal Database Search
		2.2 Publication Profiling
	3 AI in Various Aspects of COVID-19 Research
		3.1 AI in Diagnosis and Prediction of COVID-19
		3.2 AI in Epidemiology (Viral Forecasting, Control and Spread Dynamics)
		3.3 AI in the Molecular Study, Drug Design, and Treatment of COVID-19
		3.4 AI in Commerce, Business, Governance, Education and Training
	4 Conclusion
	References
Insights of Artificial Intelligence to Stop Spread of COVID-19
	1 Introduction
	2 Brief Technical Backgrounds
		2.1 Deep Learning:
		2.2 Computer Vision
		2.3 IoT or Edge Device
		2.4 Edge Computing
	3 Review of Some Recent State-of-the-Arts
	4 Some Critical Areas Through AI to Stop Spreading COVID-19
		4.1 Monitoring Intensive Care Unit (ICU) with AI Technologies
		4.2 Patient Care with AI Assistant
		4.3 Monitoring Hygienic Practice
		4.4 Monitoring Systematic Social Distancing
	5 Conclusion and Future Scope
	References
AI Based Covid19 Analysis-A Pragmatic Approach
	1 Introduction
		1.1 Motivation and Contributions
		1.2 Section Organization
	2 Global Health Emergency Declared by WHO
		2.1 Global Scenario
		2.2 COVID-19 Pathophysiology
	3 Transmission of SARS-CoV-2
	4 Prevention
	5 Diagnosis
		5.1 Nucleic Acid Detection Technology
		5.2 Computed Tomography (CT) Scanning
	6 Treatment
	7 Literature Review of AI Based Analysis
		7.1 State-of-the-art in Covid-19 Scenario
		7.2 Image Processing Based Analysis
		7.3 Data Science Based Forecasting
		7.4 Genomics Based Analysis
		7.5 Role of Mathematical Modelling in Infectious Diseases
	8 Mathematical Preliminaries of Infectious Disease Modeling
	9 Datasets Available
	10 Case Scenarios: Problem and Solution
	11 Conclusion
	References
Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak
	1 Introduction
		1.1 Motivation
	2 Update on COVID-19 in Namibia
	3 Psychosocial Support
	4 Statistical and Machine Learning Methods
		4.1 Statistical Models
		4.2 Machine Learning Models
	5 Statistical Results and Discussion
	6 Linear Regression for Machine Learning
		6.1 Regression Analysis
		6.2 Estimation Process
		6.3 Regression Model
	7 Results of Regression-Machine Learning
		7.1 Plot at Mean Value of Date of Symptoms Onset and Infected Cases
	8 Conclusions
	References
Role of the Accurate Detection of Core Body Temperature in the Early Detection of Coronavirus
	1 Introduction
		1.1 Types of Thermometers
		1.2 Difference Between Industrial Thermometer and Medical Thermometer
	2 Related Research
	3 Wireless Body Area Network
		3.1 5G Technology
		3.2 Using IR Thermometer in a WBAN Network
	4 Conclusions
	References
The Effect CoronaVirus Pendamic on Education into Electronic Multi-modal Smart Education
	1 Introduction
	2 The Effect CoronaVirus on Education
	3 Smart Education
	4 Benefits and Challenges of Smart Education Applications
	5 Open Research Directions of Smart Education
	6 Conclusion
	References
Deep Learning Against COVID-19
An H2O’s Deep Learning-Inspired Model Based on Big Data Analytics for Coronavirus Disease (COVID-19) Diagnosis
	1 Introduction
	2 Big Data Analytics
	3 Deep Learning
		3.1 Convolutional Neural Networks (CNN)
		3.2 Generative Adversarial Networks (GANs)
	4 Big Data Analytics Against COVID-19
	5 Deep Learning Against COVID-19
	6 The Proposed H2O’s Deep-Learning-Inspired Model Based on Big Data Analytics (DLBD-COV) for COVID-19 Diagnosis
	7 The Experimental Results
		7.1 Experiment 1: Compare Between GAN and CNN Implemented in DLBD-COV
		7.2 Experiment Two: Evaluate the Overall Accuracy of DLBD-COV Model
		7.3 Experiment Three: Evaluate the Computational Time of DLBD-COV Model
		7.4 Experiment Four: Test the Impact of Using H2O on DLBD-COV Performance
	8 Conclusion
	References
Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique
	1 Introduction
		1.1 The Literature Review
	2 Material
		2.1 Statistical Features of Dataset
		2.2 Visual Features of Dataset
	3 Method
		3.1 Deep Learning
		3.2 Convolutional Neural Network
		3.3 Feature Fusion and Ranking Technique
		3.4 Support Vector Machines (SVMs)
		3.5 Proposed Method
	4 Experimental Results
		4.1 Classification Results of Subset 1
		4.2 Classification Results of Subset 2
		4.3 Performance Evaluation
	5 Discussion
	6 Conclusion
	References
Stacking Deep Learning for Early COVID-19 Vision Diagnosis
	1 Introduction
	2 Deep Learning Models
	3 Research Methodology
		3.1 Stacking Ensemble Deep Learning
		3.2 Model
	4 Result
	5 Conclusion
	References