Overview

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.

Full Product Details

Author:   Abhinav Juneja (BMIET, India) ,  Vikram Bali (JSS Academy of Technical Education, India) ,  Sapna Juneja (BMIET, India) ,  Vishal Jain (Sharda University, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
Dimensions:   Width: 1.00cm , Height: 1.00cm , Length: 1.00cm
Weight:   0.454kg
ISBN:  

9781119768791

ISBN 10:   1119768799
Pages:   352
Publication Date:   05 November 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

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 69 4.4.1 Enabling Organizations to Regulate and Scale 69 4.4.2 Understanding About COVID-19 Infections 69 4.4.3 Gearing Up Study and Finding Treatments 69 4.4.4 Predicting Treatment and Healing Outcomes 70 4.4.5 Testing Patients and Diagnosing COVID-19 70 References 71 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 75 Nishant Jha and Deepak Prashar 5.1 Introduction 76 5.2 Related Work 78 5.3 Suggested Methodology 79 5.4 Models in Epidemiology 80 5.4.1 Bayesian Inference Models 81 5.4.1.1 Markov Chain (MCMC) Algorithm 82 5.5 Particle Filtering Algorithm 82 5.6 MCM Model Implementation 83 5.6.1 Reproduction Number 84 5.7 Diagnosis of COVID-19 85 5.7.1 Predicting Outbreaks Through Social Media Analysis 86 5.7.1.1 Risk of New Pandemics 87 5.8 Conclusion 88 References 88 Part 2: Emerging Technologies to Deal with COVID-19 91 6 Emerging Technologies for Handling Pandemic Challenges 93 D. Karthika and K. Kalaiselvi 6.1 Introduction 94 6.2 Technological Strategies to Support Society During the Pandemic 95 6.2.1 Online Shopping and Robot Deliveries 96 6.2.2 Digital and Contactless Payments 96 6.2.3 Remote Work 97 6.2.4 Telehealth 97 6.2.5 Online Entertainment 98 6.2.6 Supply Chain 4.0 98 6.2.7 3D Printing 98 6.2.8 Rapid Detection 99 6.2.9 QRT-PCR 99 6.2.10 Immunodiagnostic Test (Rapid Antibody Test) 99 6.2.11 Work From Home 100 6.2.12 Distance Learning 100 6.2.13 Surveillance 100 6.3 Feasible Prospective Technologies in Controlling the Pandemic 101 6.3.1 Robotics and Drones 101 6.3.2 5G and Information and Communications Technology (ICT) 101 6.3.3 Portable Applications 101 6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges 102 6.4.1 Remote Healthcare 102 6.4.2 Prevention Measures 103 6.4.3 Diagnostic Solutions 103 6.4.4 Hospital Care 104 6.4.5 Public Safety During Pandemic 104 6.4.6 Industry Adapting to the Lockdown 105 6.4.7 Cities Adapting to the Lockdown 105 6.4.8 Individuals Adapting to the Lockdown 106 6.5 The Golden Age of Drone Delivery 107 6.5.1 The Early Adopters are Winning 107 6.5.2 The Golden Age Will Require Collaboration and Drive 108 6.5.3 Standardization and Data Sharing Through the Smart City Network 108 6.5.4 The Procedure of AI and Non-AI-Based Applications 110 6.6 Technology Helps Pandemic Management 111 6.6.1 Tracking People With Facial Recognition and Big Data 111 6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots 112 6.6.3 Technology Supported Temperature Monitoring 112 6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity 112 6.7 Conclusion 113 References 113 7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19 117 Nusrat Rouf, Aatif Kaisar Khan, Majid Bashir Malik, Akib Mohi Ud Din Khanday and Nadia Gul 7.1 Introduction 118 7.2 Review of Technologies Used During the Outbreak of Ebola and SARS 120 7.2.1 Technological Strategies and Tools Used at the Time of SARS 120 7.2.2 Technological Strategies and Tools Used at the Time of Ebola 121 7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis 124 7.3.1 Artificial Intelligence 124 7.3.1.1 Application of AI in Developed Countries 127 7.3.1.2 Application of AI in Developing Countries 128 7.3.2 IoT & Robotics 129 7.3.2.1 Application of IoT and Robotics in Developed Countries 130 7.3.2.2 Application of IoT and Robotics in Developing Countries 131 7.3.3 Telemedicine 131 7.3.3.1 Application of Telemedicine in Developed Countries 132 7.3.3.2 Application of Telemedicine in Developing Countries 133 7.3.4 Innovative Healthcare 133 7.3.4.1 Application of Innovative Healthcare in Developed Countries 134 7.3.4.2 Application of Innovative Healthcare in Developing Countries 134 7.3.4.3 Application of Innovative Healthcare in the Least Developed Countries 135 7.3.5 Nanotechnology 135 7.4 Conclusion 136 References 137 8 Advances in Technology: Preparedness for Handling Pandemic Challenges 143 Shweta Sinha and Vikas Thada 8.1 Introduction 143 8.2 Issues and Challenges Due to Pandemic 145 8.2.1 Health Effect 146 8.2.2 Economic Impact 147 8.2.3 Social Impact 148 8.3 Digital Technology and Pandemic 149 8.3.1 Digital Healthcare 149 8.3.2 Network and Connectivity 151 8.3.3 Development of Potential Treatment 151 8.3.4 Online Platform for Learning and Interaction 152 8.3.5 Contactless Payment 152 8.3.6 Entertainment 152 8.4 Application of Technology for Handling Pandemic 153 8.4.1 Technology for Preparedness and Response 153 8.4.2 Machine Learning for Pandemic Forecast 155 8.5 Challenges with Digital Healthcare 157 8.6 Conclusion 158 References 159 9 Emerging Technologies for COVID-19 163 Rohit Anand, Nidhi Sindhwani, Avinash Saini and Shubham 9.1 Introduction 163 9.2 Related Work 165 9.3 Technologies to Combat COVID-19 166 9.3.1 Blockchain 167 9.3.1.1 Challenges and Solutions 168 9.3.2 Unmanned Aerial Vehicle (UAV) 169 9.3.2.1 Challenges and Solutions 169 9.3.3 Mobile APK 170 9.3.3.1 Challenges and Solutions 170 9.3.4 Wearable Sensing 171 9.3.4.1 Challenges and Solutions 172 9.3.5 Internet of Healthcare Things 173 9.3.5.1 Challenges and Solutions 175 9.3.6 Artificial Intelligence 175 9.3.6.1 Challenges and Solutions 175 9.3.7 5G 176 9.3.7.1 Challenges and Solutions 176 9.3.8 Virtual Reality 176 9.3.8.1 Challenges and Solutions 177 9.4 Comparison of Various Technologies to Combat COVID-19 177 9.5 Conclusion 185 References 185 10 Emerging Techniques for Handling Pandemic Challenges 189 Ankur Gupta and Puneet Garg 10.1 Introduction to Pandemic 190 10.1.1 How Pandemic Spreads? 190 10.1.2 Background History 191 10.1.3 Corona 192 10.2 Technique Used to Handle Pandemic Challenges 194 10.2.1 Smart Techniques in Cities 194 10.2.2 Smart Technologies in Western Democracies 196 10.2.3 Techno- or Human-Driven Approach 197 10.3 Working Process of Techniques 197 10.4 Data Analysis 201 10.5 Rapid Development Structure 206 10.6 Conclusion & Future Scope 207 References 208 Part 3: Algorithmic Techniques for Handling Pandemic 211 11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling 213 Tan Nhat Pham and Son Vu Truong Dao 11.1 Introduction 213 11.2 Methodology 214 11.2.1 Data Collection 214 11.2.2 Mathematical Model Development 215 11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm 217 11.2.4 Discrete Version of APGWO 219 11.2.4.1 Population Initialization 219 11.2.4.2 Discrete Search Operator for PSO Main Loop 223 11.2.4.3 Discrete Search Strategy for GWO Nested Loop 224 11.2.4.4 Constraint Handling 230 11.3 Computational Results 230 11.4 Conclusion 232 References 233 12 Multi-Purpose Robotic Sensing Device for Healthcare Services 237 HirakRanjan Das, Dinesh Bhatia, Ajan Patowary and Animesh Mishra 12.1 Introduction 238 12.2 Background and Objectives 238 12.3 The Functioning of Multi-Purpose Robot 239 12.4 Discussion and Conclusions 248 References 249 13 Prevalence of Internet of Things in Pandemic 251 Rishita Khurana and Madhulika Bhatia 13.1 Introduction 252 13.2 What is IoT? 255 13.2.1 History of IoT 255 13.2.2 Background of IoT for COVID-19 Pandemic 256 13.2.3 Operations Involved in IoT for COVID-19 257 13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? 257 13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT 260 13.3.1 Smart Disease Surveillance Based on Internet of Things 261 13.3.1.1 Smart Disease Surveillance 261 13.3.2 IoT PCR for Spread Disease Monitoring and Controlling 263 13.4 Global Technological Developments to Overcome Cases of COVID-19 264 13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic 265 13.4.2 Key Benefits of Using IoT in COVID-19 269 13.4.3 A Last Word About Industrial Maintenance and IoT 270 13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic 270 13.5 Results & Discussions 270 13.6 Conclusion 271 References 272 14 Mathematical Insight of COVID-19 Infection—A Modeling Approach 275 Komal Arora, Pooja Khurana, Deepak Kumar and Bhanu Sharma 14.1 Introduction 275 14.1.1 A Brief on Coronaviruses 276 14.2 Epidemiology and Etiology 277 14.3 Transmission of Infection and Available Treatments 278 14.4 COVID-19 Infection and Immune Responses 279 14.5 Mathematical Modeling 280 14.5.1 Simple Mathematical Models 281 14.5.1.1 Basic Model 281 14.5.1.2 Logistic Model 282 14.5.2 Differential Equations Models 283 14.5.2.1 Temporal Model (Linear Differential Equation Model, Logistic Model) 283 14.5.2.2 SIR Model 284 14.5.2.3 SEIR Model 285 14.5.2.4 Improved SEIR Model 287 14.5.3 Stochastic Models 288 14.5.3.1 Basic Model 288 14.5.3.2 Simple Stochastic SI Model 289 14.5.3.3 SIR Stochastic Differential Equations 290 14.5.3.4 SIR Continuous Time Markov Chain 290 14.5.3.5 Stochastic SIR Model 291 14.5.3.6 Stochastic SIR With Demography 292 14.6 Conclusion 292 References 293 15 Machine Learning: A Tool to Combat COVID-19 299 Shakti Arora, Vijay Anant Athavale and Tanvi Singh 15.1 Introduction 300 15.1.1 Recent Survey and Analysis 301 15.2 Our Contribution 303 15.3 State-Wise Data Set and Analysis 307 15.4 Neural Network 308 15.4.1 M5P Model Tree 309 15.5 Results and Discussion 309 15.6 Conclusion 314 15.7 Future Scope 314 References 314 Index 317

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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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