COVID-19: Prediction, Decision-Making, and its Impacts (eBook)

This book outlines artificial intelligence for COVID-19 issues that are ranging from prediction to decision-making for healthcare support in human lives. Starting with major COVID-19 issues and challenges, it takes possible AI-based solutions for multiple problems, such as early prediction, its role for public health, detection of positive cases, drug analysis, and healthcare support. It mainly employs publicly available data (population) to predict who should be tested for COVID-19, for example, radiological image data to detect COVID-19 positive cases from other similar and/or different manifestations, such as pneumonia, distributed healthcare support, and supply chains in the middle of COVID-19 pandemic. The book includes recently developed AI-driven tools and techniques, such as pattern recognition, anomaly detection, machine learning, and data analytics. It covers a wide range of audience from computer science and engineering to healthcare professionals.

 

Dr. KC Santosh (IEEE Senior Member) is the Chair and an Associate Professor for the Department of Computer Science at the University of South Dakota. Before joining USD, Dr. Santosh worked as a Research Fellow at the US National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a Postdoctoral Research Scientist at the LORIA Research Centre, Universite de Lorraine, in direct collaboration with ITESOFT, France. He also served as a Research Scientist at the INRIA Nancy Grand Est Research Centre, France, where he has received his Ph.D. diploma in Computer Science. Dr. Santosh has published more than 70 peer-reviewed research articles, 100 conference proceedings, and 11 book chapters. He has authored 4 books and edited 3 books, 14 journal issues, and 6 conference proceedings. He is currently Editor-in-Chief of IJSIP and an Associate Editor for several journals, such as International Journal of Machine Learning and Cybernetics and IEEE Access. He has also chaired more than 10 international conference events. His research projects have been funded by multiple agencies, including the SDCRGP, Department of Education (DOE), and the National Science Foundation (NSF). Dr. Santosh is the proud recipient of the Presidents Research Excellence Award (USD, 2019) and an award from the Department of Health & Human Services (2014).

Dr. Amit Joshi is currently the Director of Global Knowledge Research Foundation and also an Entrepreneur & Researcher who has completed his master's and research in the areas of cloud computing and cryptography in medical imaging. Dr. Joshi has an experience of around 10 years in academic and industry in prestigious organizations. Dr. Joshi is an active member of ACM, IEEE, CSI, AMIE, IACSIT, Singapore, IDES, ACEEE, NPA, and many other professional societies. Currently, Dr. Joshi is the International Chair of InterYIT at International Federation of Information Processing (IFIP, Austria). He has presented and published more than 50 papers in national and international journals/conferences of IEEE and ACM. Dr. Joshi has also edited more than 40 books which are published by Springer, ACM, and other reputed publishers. Dr. Joshi has also organized more than 50 national and international conferences and programs in association with ACM, Springer, and IEEE to name a few across different countries including India, UK, Europe, USA, Canada, Thailand, Egypt, and many more.

 

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The book aims to outline the issues of AI and COVID-19, involving predictions,medical support decision-making, and possible impact on human life. Starting withmajor COVID-19 issues and challenges, it takes possible AI-based solutions forseveral problems, such as public health surveillance, early (epidemic) prediction,COVID-19 positive case detection, and robotics integration against COVID-19.Beside mathematical modeling, it includes the necessity of changes in innovationsand possible COVID-19 impacts. The book covers a clear understanding of AI-driven tools and techniques, where pattern recognition, anomaly detection, machinelearning, and data analytics are considered. It aims to include the wide range ofaudiences from computer science and engineering to healthcare professionals.