1Chapter 1. Overview of AI in Covid-19
355.2 Medical Image Annotation Tool
21.1 Introduction
365.3 Model architecture and training
31.2 Political and structural responses
375.4 Overview and status of Covid-19 outbreak
41.3 Leadership
385.5 Virology
51.4 Thrive: Toward the “next to normal.”
395.6 Epidemiology
61.5 Questions
405.7 Clinical Manifestations
7Chapter 2. Complications and Assessment of AI Applications Linked to Covid-19
415.8 Diagnosis
82.1 Introduction
425.9 Treatment
92.2 Methodology
435.10 Prevention
102.3 Survey of Surveys
445.11 Fundamentals of Deep Learning
112.4 Forecasting
455.12 Auto-encoder-decoder
122.5 Diagnosis
465.13 Types of Covid19 Problems Solved by DL
132.6 Containment and Monitoring
475.14 Overview of DL applications for medical
142.7 Drug Developments and Treatments
48Image Processing
152.8 Medical and Social Development
495.15 Artificial neural networks
162.9 Selection process: Methodology and Results
505.16 Deep learning
172.10 Questions
515.17 Questions
18Chapter 3. In-Depth Examination of Selected Applications
52Chapter 6. Covid-19 Diagnosis- Myth and Protocol
193.1 Introduction
536.1 Introduction
203.2 Ethical and Human Rights Framework
546.1.6 Treatment
213.3 Sorting Applications
556.2 Covid-19 Prevention
223.4 Surveillance Applications
566.3 Effective Covid-19 Prevention Tips
233.5 Questions
576.4 Convalescent Plasma Therapy
24Chapter 4. Data Privacy Issues in Connection with Covid-19
586.5 Questions
254.1 Introduction
59Chapter 7. Neurorobotics
264.2 Data Protection
607.1 Introduction
274.3 Privacy and Cyber Security
617.2 Receptive Field
284.4 Entertainment and Media
627.3 Optical Flow
294.5 Employment and Benefits
637.4 Motion Recognition
304.6 Litigation and Dispute Resolution
64 7.5 Mirror neurons
314.7 Tech and Data
657.6 Parallel Fiber
324.8 Questions
667.7 Questions
33Chapter 5. Deep Learning And Medical Image Processing For Covid-19 Pandemic
67Glossary
345.1 Introduction
