Contactless Healthcare Facilitation and Commodity Delivery Management During COVID 19 Pandemic (Advanced Technologies and Societal Change)

Preface
Contents
1 Detection of Coronavirus from Chest X-ray Images Using 2D Convolutional Neural Network
	1.1 Introduction
	1.2 Related Works
	1.3 Materials and Methods
		1.3.1 Dataset
		1.3.2 Model Architecture
		1.3.3 Performance Metrics
	1.4 Experimental Result Analysis
		1.4.1 Experiment Setup
		1.4.2 Result Analysis
	1.5 Conclusion
	References
2 Interpretation of COVID-19 CT Scans
	2.1 Introduction
	2.2 Proposed U-Net Architecture
		2.2.1 Slice-and-Fuse Strategy
		2.2.2 U-Net for Detection
		2.2.3 Retraining of U-Net Model
		2.2.4 Process Workflow
	2.3 Performance Metrics
	2.4 Comparison with Recently Reported COVID-19 Segmentation Performance
	2.5 Limitations of Implementation Strategies
	2.6 Future Scope
	2.7 Conclusion
	References
3 Deep Learning-Based Prediction of nCOVID-19 Disease Using Chest X-ray Images (CXRIs)
	3.1 Introduction
	3.2 Materials and Methods
		3.2.1 Dataset
		3.2.2 Model Formulation
		3.2.3 Proposed Prediction Process
	3.3 Experimental Result and Analysis
		3.3.1 Evaluation Metrics
	3.4 Conclusion
	References
4 Explainable Deep Learning Through Grad-CAM and Feature Visualization for the Detection of COVID-19 in Chest X-ray Images
	4.1 Introduction
	4.2 Literature Study
	4.3 Experiments
	4.4 Results
	4.5 Explainable Deep Learning Results
	4.6 Conclusion
	References
5 Preserving the Privacy of COVID-19 Infected Patients Data Using a Divergent-Scale Supervised Learning for Publishing the Informative Data
	5.1 Introduction
	5.2 Related Works
	5.3 Materials and Methods
		5.3.1 Model Description
	5.4 Experimental Results
	5.5 Conclusion
	References
6 Personal Cloud System for Hospital Data Management to Store COVID-19 Patients Records
	6.1 Introduction
		6.1.1 Objective
		6.1.2 Purpose
		6.1.3 Scope
		6.1.4 Organization of the Report
	6.2 Previous Studies
		6.2.1 Cloud Storage
		6.2.2 Raspberry Pi
		6.2.3 Network-Attached Storage (NAS)
		6.2.4 Reference Papers
		6.2.5 Android Studio
		6.2.6 Java ME
		6.2.7 PHP(Hypertextpreprocessor)
		6.2.8 AJAX
		6.2.9 Bootstrap
		6.2.10 JavaScript
	6.3 System Analysis
		6.3.1 Existing System
		6.3.2 Proposed System
		6.3.3 Hardware Requirements
		6.3.4 Software Requirements
		6.3.5 Feasibility Study
	6.4 System Design
		6.4.1 System Architecture
	6.5 Implementations
		6.5.1 Installing Required Packages
		6.5.2 Setting Up Database
		6.5.3 Modules
		6.5.4 Android Studio Modules
	6.6 Testing
		6.6.1 Unit-Testing
		6.6.2 Testing the Functionality
	6.7 Screenshots
	6.8 Conclusion
		6.8.1 Future Enhancements
	References
7 Strategies and Tools for Effective Suspicious Event Detection from Video: A Survey Perspective (COVID-19)
	7.1 Introduction
	7.2 Problem Statement and Background Knowledge
	7.3 Comparative Analysis of Suspicious Event Detection Tools and Datasets
	7.4 Conclusion and Future Work
	References
8 Smart Water Purifier and Dispenser for Averting Spread of COVID-19 Infection—Machine Learning Approach
	8.1 Introduction
	8.2 Literature Review
	8.3 Challenges
	8.4 Methodology
	8.5 Description of the Model
	8.6 Conclusions
	References
9 Impact of COVID-19 on Power Sector
	9.1 Introduction
	9.2 Impact on Power Sector
		9.2.1 Impact on Total Power Generation
		9.2.2 Impact on Plant Load Factor
	9.3 Impact on Requirement and Consumption
		9.3.1 Impact on Various Energy Resources
	9.4 Impact on Various State Power Generating Stations
		9.4.1 Impact on Northern Region
		9.4.2 Impact on Western Region
		9.4.3 Impact on Southern Region
		9.4.4 Impact on Eastern Region
	9.5 Impact on North Eastern Region
		9.5.1 Impact on All India Region
	9.6 Conclusion
	References
10 Automated Navigation System with Indoor Assistance for Blind
	10.1 Introduction
	10.2 Related Work
	10.3 System Description
	10.4 Implementation
	10.5 Conclusion
	References
11 Estimation of Quantity and Nutritional Information of Food Using Image Processing
	11.1 Introduction
		11.1.1 Purpose
		11.1.2 Scope
		11.1.3 Image-Based Estimation of Real Food Size for Accurate Food Calorie Estimation
		11.1.4 CalorieCam
		11.1.5 Weakly Supervised Segmentation-Based Calorie Estimation
		11.1.6 ARDeep Calorie Cam
		11.1.7 Depth Calorie Cam
		11.1.8 Smartphone Applications for Promoting Healthy Diet and Nutrition
		11.1.9 Object Detection via Region-Based Fully Convolutional Networks
		11.1.10 Understanding of Object Detection Based on CNN Family and YOLO
		11.1.11 YOLO (You Only Look Once)—Real-Time Object Detection for Real-Time Food Detection
		11.1.12 Python Scripting Language
		11.1.13 OpenCV
		11.1.14 SQL Alchemy
		11.1.15 Flask
		11.1.16 Tkinter
		11.1.17 Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment
		11.1.18 An Automatic Calorie Estimation System of Food Images on a Smartphone
	11.2 System Analysis
		11.2.1 Existing System
		11.2.2 Limitations
		11.2.3 Proposed System
		11.2.4 Feasibility Study
		11.2.5 Effort, Duration, and Cost Estimation using COCOMO
		11.2.6 Hardware Requirements
		11.2.7 Software Requirements
		11.2.8 Software Requirements’ Specification Revision History
	11.3 System Design
		11.3.1 System Architecture
		11.3.2 SQLite
		11.3.3 Object Detection Module
		11.3.4 Area Estimation Module
		11.3.5 Height Estimation Module
		11.3.6 Deployment Architecture
		11.3.7 AmazonEC2
		11.3.8 Nginx
		11.3.9 Gunicorn3
		11.3.10 Flask
	11.4 Implementation
		11.4.1 Object-Detection Module
		11.4.2 Area-Estimation Module
		11.4.3 Height Estimation Module
	11.5 Testing
	11.6 Screenshots
	11.7 Conclusion
	References