Enabling Healthcare 4.0 for Pandemics

A Roadmap Using Ai, Machine Learning, Iot and Cognitive Technologies
Herausgegeben:Juneja, Abhinav; Bali, Vikram; Juneja, Sapna; Jain, Vishal

• Gebundenes Buch

Produktbeschreibung

ENABLING HEALTHCARE 4.0 for PANDEMICS

The book explores the role and scope of AI, machine learning and other current technologies to handle pandemics.

In this timely book, the editors explore the current state of practice in Healthcare 4.0 and provide a roadmap for harnessing artificial intelligence, machine learning, and Internet of Things, as well as other modern cognitive technologies, to aid in dealing with the various aspects of an emergency pandemic outbreak. There is a need to improvise healthcare systems with the intervention of modern computing and data management platforms to increase the reliability of human processes and life expectancy. There is an urgent need to come up with smart IoT-based systems which can aid in the detection, prevention and cure of these pandemics with more precision. There are a lot of challenges to overcome but this book proposes a new approach to organize the technological warfare for tackling future pandemics.

In thisbook, the reader will find:
_ State-of-the-art technological advancements in pandemic management;
_ AI and ML-based identification and forecasting of pandemic spread;
_ Smart IoT-based ecosystem for pandemic scenario.

Audience
The book will be used by researchers and practitioners in computer science, artificial intelligence, bioinformatics, data scientists, biomedical statisticians, as well as industry professionals in disaster and pandemic management.

Produktdetails

• Verlag: Wiley & Sons / Wiley-Scrivener
• Artikelnr. des Verlages: 1W119768790
• 1. Auflage
• Seitenzahl: 352
• Erscheinungstermin: 22. September 2021
• Englisch
• Abmessung: 234mm x 161mm x 23mm
• Gewicht: 610g
• ISBN-13: 9781119768791
• ISBN-10: 1119768799
• Artikelnr.: 60896289

Autorenporträt

Abhinav Juneja PhD is Professor and Head of Computer Science & Information Technology Department, at KIET Group of Institutions, Ghaziabad, Delhi-NCR, India. He has published more than 40 research articles. Vikram Bali PhD is Professor and Head of Computer Science and Engineering Department at JSS Academy of Technical Education, Noida, India. Sapna Juneja PhD is Professor and Head of Computer Science Department at IMS Engineering College, Ghaziabad, India. Vishal Jain PhD is an Associate Professor in the Department of Computer Science and Engineering, Sharda University, Greater Noida, India. He has published more than 85 research articles and authored/edited more than 15 books. Prashant Tyagi, MBBS MS MCh is a practicing plastic surgeon at Cosmplastik Clinic, Sonepat, Delhi-NCR, India.

Inhaltsangabe

Preface xv

Part 1: Machine Learning for Handling COVID-19 1

1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic 3
Sapna Juneja, Abhinav Juneja, Vikram Bali and Vishal Jain

1.1 Introduction 3

1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem 4

1.2 COVID-19 Diagnosis in Patients Using Machine Learning 5

1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 6

1.2.2 Machine Learning to Speed Up Drug Development 7

1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 8

1.3 AI and Machine Learning as a Support System for Robotic System and Drones 10

1.3.1 AI-Based Location Tracking of COVID-19 Patients 10

1.3.2 Increased Number of Screenings Using AI Approach 11

1.3.3 Artificial Intelligence in Management of Resources During COVID-19 11

1.3.4 Influence of AI on Manufacturing Industry During COVID-19 11

1.3.5 Artificial Intelligence and Mental Health in COVID-19 14

1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? 14

1.3.7 Advantages and Disadvantages of AI in Post COVID Era 15

1.4 Conclusion 17

References 17

2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic 21
Rehab A. Rayan, Imran Zafar and Iryna B. Romash

2.1 Introduction 22

2.2 Key Techniques of HCS 4.0 for COVID-19 24

2.2.1 Artificial Intelligence (AI) 24

2.2.2 The Internet of Things (IoT) 25

2.2.3 Big Data 25

2.2.4 Virtual Reality (VR) 26

2.2.5 Holography 26

2.2.6 Cloud Computing 27

2.2.7 Autonomous Robots 27

2.2.8 3D Scanning 28

2.2.9 3D Printing Technology 28

2.2.10 Biosensors 29

2.3 Real World Applications of HCS 4.0 for COVID-19 29

2.4 Opportunities and Limitations 33

2.5 Future Perspectives 34

2.6 Conclusion 34

References 35

3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques 39
Supriya Raheja and Shaswata Datta

3.1 Introduction 39

3.2 Literature Review 40

3.3 Types of Machine Learning 42

3.4 Machine Learning Algorithms 43

3.4.1 Linear Regression 43

3.4.2 Logistic Regression 45

3.4.3 K-NN or K Nearest Neighbor 46

3.4.4 Decision Tree 47

3.4.5 Random Forest 48

3.5 Analysis and Prediction of COVID-19 Data 48

3.5.1 Methodology Adopted 49

3.6 Analysis Using Machine Learning Models 54

3.6.1 Splitting of Data into Training and Testing Data Set 54

3.6.2 Training of Machine Learning Models 54

3.6.3 Calculating the Score 54

3.7 Conclusion & Future Scope 55

References 55

4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning 59
Sujata Chauhan, Madan Singh and Puneet Garg

4.1 Introduction 60

4.2 Effect of COVID-19 on Different Sections of Society 61

4.2.1 Effect of COVID-19 on Mental Health of Elder People 61

4.2.2 Effect of COVID-19 on our Environment 61

4.2.3 Effect of COVID-19 on International Allies and Healthcare 62

4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 63

4.2.5 Effect of COVID-19 on Labor Migrants 63

4.2.6 Impact of COVID-19 on our Economy 64

4.3 Definition and Types of Machine Learning 64

4.3.1 Machine Learning & Its Types 65

4.3.2 Applications of Machine Learning 68

4.4 Machine Learning Approaches for COVID-19 6