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E-raamat: Enabling Healthcare 4.0 for Pandemics: A Roadmap Using AI, Machine Learning, IoT and Cognitive Technologies

Edited by (Sharda University, India), Edited by (BMIET, India), Edited by , Edited by (JSS Academy of Technical Education, India), Edited by (BMIET, India)
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  • Ilmumisaeg: 08-Sep-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119769071
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Enabling Healthcare 4.0 for Pandemics: A Roadmap Using AI, Machine Learning, IoT and Cognitive TechnologiesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 08-Sep-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119769071
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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 this book, 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.
Preface xv
Part 1: Machine Learning for Handling COVID-19 1(90)
1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic
3(18)
Sapna Juneja
Abhinav Juneja
Vikram Bali
Vishal Jain
1.1 Introduction
3(2)
1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem
4(1)
1.2 COVID-19 Diagnosis in Patients Using Machine Learning
5(5)
1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19
6(1)
1.2.2 Machine Learning to Speed Up Drug Development
7(1)
1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19
8(2)
1.3 AI and Machine Learning as a Support System for Robotic System and Drones
10(7)
1.3.1 AI-Based Location Tracking of COVID-19 Patients
10(1)
1.3.2 Increased Number of Screenings Using AI Approach
11(1)
1.3.3 Artificial Intelligence in Management of Resources During COVID-19
11(1)
1.3.4 Influence of AI on Manufacturing Industry During COVID-19
11(3)
1.3.5 Artificial Intelligence and Mental Health in COVID-19
14(1)
1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis?
14(1)
1.3.7 Advantages and Disadvantages of AI in Post COVID Era
15(2)
1.4 Conclusion
17(1)
References
17(4)
2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic
21(18)
Rehab A. Rayan
Imran Zafar
Iryna B. Romash
2.1 Introduction
22(2)
2.2 Key Techniques of HCS 4.0 for COVID-19
24(5)
2.2.1 Artificial Intelligence (AI)
24(1)
2.2.2 The Internet of Things (IoT)
25(1)
2.2.3 Big Data
25(1)
2.2.4 Virtual Reality (VR)
26(1)
2.2.5 Holography
26(1)
2.2.6 Cloud Computing
27(1)
2.2.7 Autonomous Robots
27(1)
2.2.8 3D Scanning
28(1)
2.2.9 3D Printing Technology
28(1)
2.2.10 Biosensors
29(1)
2.3 Real World Applications of HCS 4.0 for COVID-19
29(4)
2.4 Opportunities and Limitations
33(1)
2.5 Future Perspectives
34(1)
2.6 Conclusion
34(1)
References
35(4)
3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques
39(20)
Supriya Raheja
Shaswata Datta
3.1 Introduction
39(1)
3.2 Literature Review
40(2)
3.3 Types of Machine Learning
42(1)
3.4 Machine Learning Algorithms
43(5)
3.4.1 Linear Regression
43(2)
3.4.2 Logistic Regression
45(1)
3.4.3 K-NN or K Nearest Neighbor
46(1)
3.4.4 Decision Tree
47(1)
3.4.5 Random Forest
48(1)
3.5 Analysis and Prediction of COVID-19 Data
48(6)
3.5.1 Methodology Adopted
49(5)
3.6 Analysis Using Machine Learning Models
54(1)
3.6.1 Splitting of Data into Training and Testing Data Set
54(1)
3.6.2 Training of Machine Learning Models
54(1)
3.6.3 Calculating the Score
54(1)
3.7 Conclusion & Future Scope
55(1)
References
55(4)
4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning
59(16)
Sujata Chauhan
Madan Singh
Puneet Garg
4.1 Introduction
60(1)
4.2 Effect of COVID-19 on Different Sections of Society
61(3)
4.2.1 Effect of COVID-19 on Mental Health of Elder People
61(1)
4.2.2 Effect of COVID-19 on our Environment
61(1)
4.2.3 Effect of COVID-19 on International Allies and Healthcare
62(1)
4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19
63(1)
4.2.5 Effect of COVID-19 on Labor Migrants
63(1)
4.2.6 Impact of COVID-19 on our Economy
64(1)
4.3 Definition and Types of Machine Learning
64(5)
4.3.1 Machine Learning & Its Types
65(3)
4.3.2 Applications of Machine Learning
68(1)
4.4 Machine Learning Approaches for COVID-19
69(2)
4.4.1 Enabling Organizations to Regulate and Scale
69(1)
4.4.2 Understanding About COVID-19 Infections
69(1)
4.4.3 Gearing Up Study and Finding Treatments
69(1)
4.4.4 Predicting Treatment and Healing Outcomes
70(1)
4.4.5 Testing Patients and Diagnosing COVID-19
70(1)
References
71(4)
5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19
75(16)
Nishant Jha
Deepak Prashar
5.1 Introduction
76(2)
5.2 Related Work
78(1)
5.3 Suggested Methodology
79(1)
5.4 Models in Epidemiology
80(2)
5.4.1 Bayesian Inference Models
81(1)
5.4.1.1 Markov Chain (MCMC) Algorithm
82(1)
5.5 Particle Filtering Algorithm
82(1)
5.6 MCM Model Implementation
83(2)
5.6.1 Reproduction Number
84(1)
5.7 Diagnosis of COVID-19
85(3)
5.7.1 Predicting Outbreaks Through Social Media Analysis
86(1)
5.7.1.1 Risk of New Pandemics
87(1)
5.8 Conclusion
88(1)
References
88(3)
Part 2: Emerging Technologies to Deal with COVID-19 91(120)
6 Emerging Technologies for Handling Pandemic Challenges
93(24)
D. Karthika
K. Kalaiselvi
6.1 Introduction
94(1)
6.2 Technological Strategies to Support Society During the Pandemic
95(6)
6.2.1 Online Shopping and Robot Deliveries
96(1)
6.2.2 Digital and Contactless Payments
96(1)
6.2.3 Remote Work
97(1)
6.2.4 Telehealth
97(1)
6.2.5 Online Entertainment
98(1)
6.2.6 Supply Chain 4.0
98(1)
6.2.7 3D Printing
98(1)
6.2.8 Rapid Detection
99(1)
6.2.9 QRT-PCR
99(1)
6.2.10 Immunodiagnostic Test (Rapid Antibody Test)
99(1)
6.2.11 Work From Home
100(1)
6.2.12 Distance Learning
100(1)
6.2.13 Surveillance
100(1)
6.3 Feasible Prospective Technologies in Controlling the Pandemic
101(1)
6.3.1 Robotics and Drones
101(1)
6.3.2 5G and Information and Communications Technology (ICT)
101(1)
6.3.3 Portable Applications
101(1)
6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges
102(5)
6.4.1 Remote Healthcare
102(1)
6.4.2 Prevention Measures
103(1)
6.4.3 Diagnostic Solutions
103(1)
6.4.4 Hospital Care
104(1)
6.4.5 Public Safety During Pandemic
104(1)
6.4.6 Industry Adapting to the Lockdown
105(1)
6.4.7 Cities Adapting to the Lockdown
105(1)
6.4.8 Individuals Adapting to the Lockdown
106(1)
6.5 The Golden Age of Drone Delivery
107(4)
6.5.1 The Early Adopters are Winning
107(1)
6.5.2 The Golden Age Will Require Collaboration and Drive
108(1)
6.5.3 Standardization and Data Sharing Through the Smart City Network
108(2)
6.5.4 The Procedure of AI and Non-AI-Based Applications
110(1)
6.6 Technology Helps Pandemic Management
111(2)
6.6.1 Tracking People With Facial Recognition and Big Data
111(1)
6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots
112(1)
6.6.3 Technology Supported Temperature Monitoring
112(1)
6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity
112(1)
6.7 Conclusion
113(1)
References
113(4)
7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19
117(26)
Nusrat Rouf
Aatif Kaisar Khan
Majid Bashir Malik
Akib Mohi Ud Din Khanday
Nadia Gul
7.1 Introduction
118(2)
7.2 Review of Technologies Used During the Outbreak of Ebola and SARS
120(4)
7.2.1 Technological Strategies and Tools Used at the Time of SARS
120(1)
7.2.2 Technological Strategies and Tools Used at the Time of Ebola
121(3)
7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis
124(12)
7.3.1 Artificial Intelligence
124(1)
7.3.1.1 Application of AI in Developed Countries
127(1)
7.3.1.2 Application of AI in Developing Countries
128(1)
7.3.2 IoT & Robotics
129(1)
7.3.2.1 Application of IoT and Robotics in Developed Countries
130(1)
7.3.2.2 Application of IoT and Robotics in Developing Countries
131(1)
7.3.3 Telemedicine
131(1)
7.3.3.1 Application of Telemedicine in Developed Countries
132(1)
7.3.3.2 Application of Telemedicine in Developing Countries
133(1)
7.3.4 Innovative Healthcare
133(1)
7.3.4.1 Application of Innovative Healthcare in Developed Countries
134(1)
7.3.4.2 Application of Innovative Healthcare in Developing Countries
134(1)
7.3.4.3 Application of Innovative Healthcare in the Least Developed Countries
135(1)
7.3.5 Nanotechnology
135(1)
7.4 Conclusion
136(1)
References
137(6)
8 Advances in Technology: Preparedness for Handling Pandemic Challenges
143(20)
Shweta Sinha
Vikas Thada
8.1 Introduction
143(2)
8.2 Issues and Challenges Due to Pandemic
145(4)
8.2.1 Health Effect
146(1)
8.2.2 Economic Impact
147(1)
8.2.3 Social Impact
148(1)
8.3 Digital Technology and Pandemic
149(4)
8.3.1 Digital Healthcare
149(2)
8.3.2 Network and Connectivity
151(1)
8.3.3 Development of Potential Treatment
151(1)
8.3.4 Online Platform for Learning and Interaction
152(1)
8.3.5 Contactless Payment
152(1)
8.3.6 Entertainment
152(1)
8.4 Application of Technology for Handling Pandemic
153(4)
8.4.1 Technology for Preparedness and Response
153(2)
8.4.2 Machine Learning for Pandemic Forecast
155(2)
8.5 Challenges with Digital Healthcare
157(1)
8.6 Conclusion
158(1)
References
159(4)
9 Emerging Technologies for COVID-19
163(26)
Rohit Anand
Nidhi Sindhwani
Avinash Saini
Shubham
9.1 Introduction
163(2)
9.2 Related Work
165(1)
9.3 Technologies to Combat COVID-19
166(11)
9.3.1 Blockchain
167(1)
9.3.1.1 Challenges and Solutions
168(1)
9.3.2 Unmanned Aerial Vehicle (UAV)
169(1)
9.3.2.1 Challenges and Solutions
169(1)
9.3.3 Mobile APK
170(1)
9.3.3.1 Challenges and Solutions
170(1)
9.3.4 Wearable Sensing
171(1)
9.3.4.1 Challenges and Solutions
172(1)
9.3.5 Internet of Healthcare Things
173(1)
9.3.5.1 Challenges and Solutions
175(1)
9.3.6 Artificial Intelligence
175(1)
9.3.6.1 Challenges and Solutions
175(1)
9.3.7 5G
176(1)
9.3.7.1 Challenges and Solutions
176(1)
9.3.8 Virtual Reality
176(1)
9.3.8.1 Challenges and Solutions
177(1)
9.4 Comparison of Various Technologies to Combat COVID-19
177(8)
9.5 Conclusion
185(1)
References
185(4)
10 Emerging Techniques for Handling Pandemic Challenges
189(22)
Ankur Gupta
Puneet Garg
10.1 Introduction to Pandemic
190(4)
10.1.1 How Pandemic Spreads?
190(1)
10.1.2 Background History
191(1)
10.1.3 Corona
192(2)
10.2 Technique Used to Handle Pandemic Challenges
194(3)
10.2.1 Smart Techniques in Cities
194(2)
10.2.2 Smart Technologies in Western Democracies
196(1)
10.2.3 Techno- or Human-Driven Approach
197(1)
10.3 Working Process of Techniques
197(4)
10.4 Data Analysis
201(5)
10.5 Rapid Development Structure
206(1)
10.6 Conclusion & Future Scope
207(1)
References
208(3)
Part 3: Algorithmic Techniques for Handling Pandemic 211(106)
11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling
213(24)
Tan Nhat Pham
Son Vu Truong Dao
11.1 Introduction
213(1)
11.2 Methodology
214(16)
11.2.1 Data Collection
214(1)
11.2.2 Mathematical Model Development
215(2)
11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm
217(2)
11.2.4 Discrete Version of APGWO
219(1)
11.2.4.1 Population Initialization
219(1)
11.2.4.2 Discrete Search Operator for PSO Main Loop
223(1)
11.2.4.3 Discrete Search Strategy for GWO Nested Loop
224(1)
11.2.4.4 Constraint Handling
230(1)
11.3 Computational Results
230(2)
11.4 Conclusion
232(1)
References
233(4)
12 Multi-Purpose Robotic Sensing Device for Healthcare Services
237(14)
HirakRanjan Das
Dinesh Bhatia
Ajan Patowary
Animesh Mishra
12.1 Introduction
238(1)
12.2 Background and Objectives
238(1)
12.3 The Functioning of Multi-Purpose Robot
239(9)
12.4 Discussion and Conclusions
248(1)
References
249(2)
13 Prevalence of Internet of Things in Pandemic
251(24)
Rishita Khurana
Madhulika Bhatia
13.1 Introduction
252(3)
13.2 What is IoT?
255(5)
13.2.1 History of IoT
255(1)
13.2.2 Background of IoT for COVID-19 Pandemic
256(1)
13.2.3 Operations Involved in IoT for COVID-19
257(1)
13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19?
257(3)
13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT
260(4)
13.3.1 Smart Disease Surveillance Based on Internet of Things
261(1)
13.3.1.1 Smart Disease Surveillance
261(2)
13.3.2 IoT PCR for Spread Disease Monitoring and Controlling
263(1)
13.4 Global Technological Developments to Overcome Cases of COVID-19
264(6)
13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic
265(4)
13.4.2 Key Benefits of Using IoT in COVID-19
269(1)
13.4.3 A Last Word About Industrial Maintenance and IoT
270(1)
13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic
270(1)
13.5 Results & Discussions
270(1)
13.6 Conclusion
271(1)
References
272(3)
14 Mathematical Insight of COVID-19 Infection-A Modeling Approach
275(24)
Komal Arora
Pooja Khurana
Deepak Kumar
Bhanu Sharma
14.1 Introduction
275(2)
14.1.1 A Brief on Coronaviruses
276(1)
14.2 Epidemiology and Etiology
277(1)
14.3 Transmission of Infection and Available Treatments
278(1)
14.4 COVID-19 Infection and Immune Responses
279(1)
14.5 Mathematical Modeling
280(12)
14.5.1 Simple Mathematical Models
281(1)
14.5.1.1 Basic Model
281(1)
14.5.1.2 Logistic Model
282(1)
14.5.2 Differential Equations Models
283(1)
14.5.2.1 Temporal Model (Linear Differential Equation Model, Logistic Model)
283(1)
14.5.2.2 SIR Model
284(1)
14.5.2.3 SEIR Model
285(1)
14.5.2.4 Improved SEIR Model
287(1)
14.5.3 Stochastic Models
288(1)
14.5.3.1 Basic Model
288(1)
14.5.3.2 Simple Stochastic SI Model
289(1)
14.5.3.3 SIR Stochastic Differential Equations
290(1)
14.5.3.4 SIR Continuous Time Markov Chain
290(1)
14.5.3.5 Stochastic SIR Model
291(1)
14.5.3.6 Stochastic SIR With Demography
292(1)
14.6 Conclusion
292(1)
References
293(6)
15 Machine Learning: A Tool to Combat COVID-19
299(18)
Shakti Arora
Vijay Anant Athavale
Tanvi Singh
15.1 Introduction
300(3)
15.1.1 Recent Survey and Analysis
301(2)
15.2 Our Contribution
303(4)
15.3 State-Wise Data Set and Analysis
307(1)
15.4 Neural Network
308(1)
15.4.1 M5P Model Tree
309(1)
15.5 Results and Discussion
309(5)
15.6 Conclusion
314(1)
15.7 Future Scope
314(1)
References
314(3)
Index 317
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.
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