This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.
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Each article is self-contained with a detailed description of each method, background information, statistics and mathematical details, theory, and application. Anyone looking for inspiration could find several examples and use cases of available data that can be used for insight generation. The strength of this edited book is that the readers would become familiar with several data sources and demonstration of intelligent analysis of data that social scientist routinely collect. (Enayet Raheem, ISCB News, iscb.info, June, 2022)
Chapter
1. Machine Learning Based Ensemble Approach for Predicting the
Mortality Risk of Covid-19 Patients: A Case Study.
Chapter
2. The Role of
Internet of Health Things (IoHTs) & Innovative Internet of 5G Medical
Robotic Things (IIo-5GMRTs) in COVID-19 Global Health Risk Management and
Logistics Planning.
Chapter
3. Battling COVID-19 with Process Model of
Integrated Digital Technology: An Analysis of Qualitative Data.
Chapter
4.
High-fidelity intelligence ventilator to help infect with Covid-19 based on
artificial intelligence.
Chapter
5. Boon of Artificial Intelligence in
Diagnosis of Covid-19.
Chapter
6. Artificial Intelligence and Big Data
Solutions for COVID-19.
Chapter
7. Modeling the Transmition Dynamics of
COVID-19 Virus Disease in Nigeria.
Chapter
8. Emerging Trends in Higher
Education during Pandemic Covid-19: An impact study From West Bengal.-
Chapter
9. COVID-19: Virology, Epidemiology, Diagnostics and Predictive
modelling.
Chapter
10. Improved Estimation inLogistic Regression through
Quadratic Bootstrap Approach: An Application in Indian Agricultural
e-learning System during COVID-19 Pandemic.
Chapter
11. COVID-19 and Stock
Markets: Deaths and Strict Policies.
Chapter
12. Artificial Intelligence
Techniques in Medical Imaging for Detection of Corona Virus (COVID-19 /
SARS-COV-2): A Brief Survey.
Chapter
13. A Travelling Disinfection-man
Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing
knowledge-based Optimization Algorithm.
Chapter
14. COVID-19 Lock down
Impact on Mental Health: A Cross-sectional Online Survey from Kerala, India.-
Chapter
15. Analysis, Modelling and Prediction of COVID-19 Outbreaks using
Machine Learning Algorithms.
Dr. Niranjanamurthy M is Assistant Professor, Department of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, Karnataka, India. He completed Ph.D. in Computer Science at JJTU, Rajasthan (2016); M.Phil. in Computer Science at VMU, Salem (2009); Masters in Computer Applications at Visvesvaraiah Technological University, Belgaum, Karnataka (2007); BCA from Kuvempu University 2004 with University 5th Rank. He has 10 years of teaching experience and 2 years of industry experience as Software Engineer. He has published books in Scholars Press Germany and CRC Press. He also published 56 research papers and filed 12 patents. Currently, he is guiding four Ph.D. research scholars in the areas of data science, edge computing, ML, and networking. He is Reviewer of 22 international journals and Series Editor in CRC Press and Scrivener Publishing. He has received best research journal reviewer and researcher awards. He is a member of IEEE, CSTA, IAENG, and INSC. His areas of interest are data science, ML, edge computing, software engineering, web services, cloud computing, and networking.
Dr. Siddhartha Bhattacharyya [ LFOSI, LFISRD, FIET (UK), FIETE, FIE(I), SMIEEE, SMIETI, SMACM, LMCRSI, LMCSI, LMISTE, LMIUPRAI, LMCEGR, LMICCI, LMALI, MIRSS, MIAENG, MCSTA, MIAASSE, MIDES, MISSIP, MSDIWC] is currently serving as Professor in the Department of Computer Science and Engineering of Christ University, Bangalore. He is Co-Author of 5 books and Co-Editor of 75 books and has more than 300 research publications in international journals and conference proceedings to his credit. He has got two PCTs to his credit. He is Associate Editor of several reputed journals including Applied Soft Computing, IEEE Access, Evolutionary Intelligence, and IET Quantum Communications. He is Editor of International Journal of Pattern Recognition Research and Founding Editor-in-Chief of International Journal of Hybrid Intelligence, Inderscience.His research interests include hybrid intelligence, pattern recognition, multimedia data processing, social networks, and quantum computing.
Dr. Neeraj Kumar is currently engaged with the Department of Information Technology, Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow (India). He has completed his Doctorate in Information Technology from BBAU, Lucknow, in March 2020. He has completed his basic education from Government Polytechnic, Budaun, and then graduation and PG from UPTU, Lucknow, and IIIT Allahabad in the year 2005 and 2010, respectively. After graduation, he was appointed as Lecturer in BSACET, Mathura, while after PG, appointed as Assistant Professor in various institutes of RTU and UPTU. He has published more than two dozen research articles in reputed international journals and conferences. He has published few patents related to computer science and disaster management. He has published few authored and edited books withthe publishers of national repute. He has research interests in topics related to real-life problem, including disaster management, IoT, big data, soft computing, cyber security, and quantum cryptography.
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