Springer Computational Intelligence Techniques for Combating COVID-19 (EAI/Springer Innovations in Communication and Computing)

Product Description This book presents the latest cutting edge research, theoretical methods, and novel applications in the field of computational intelligence and computational biological approaches that are aiming to combat COVID-19. The book gives the technological key drivers behind using AI to find drugs that target the virus, shedding light on the structure of COVID-19, detecting the outbreak and spread of new diseases, spotting signs of a COVID-19 infection in medical images, monitoring how the virus and lockdown is affecting mental health, and forecasting how COVID-19  cases and deaths will spread across cities and why. Further, the book helps readers understand computational intelligence techniques combating COVID-19 in a simple and systematic way. From the Back Cover This book presents the latest cutting edge research, theoretical methods, and novel applications in the field of computational intelligence and computational biological approaches that are aiming to combat COVID-19. The book gives the technological key drivers behind using AI to find drugs that target the virus, shedding light on the structure of COVID-19, detecting the outbreak and spread of new diseases, spotting signs of a COVID-19 infection in medical images, monitoring how the virus and lockdown is affecting mental health, and forecasting how COVID-19  cases and deaths will spread across cities and why. Further, the book helps readers understand computational intelligence techniques combating COVID-19 in a simple and systematic way. Provides a comprehensive reference covering innovations and development of theories, conceptual models and computational algorithms focused on COVID-19; Asserts all relevant research, key themes, complex adaptive systems, metrics and paradigms dedicated towards COVID-19, enabled with evolutionary methods of computational sciences; Explores how AI and computational techniques can help to predict which patients with the virus would go on to develop Acute Respiratory Distress Syndrome (ARDS). About the Author Dr. Sandeep Kautish is working as Professor & Dean-Academics with LBEF Campus, Kathmandu Nepal running in academic collaboration with Asia Pacific University of Technology & Innovation Malaysia. He is an academician by choice and backed with 16+ Years of work experience in academics including over 05 years in academic administration in various institutions of India and abroad. He has meritorious academic records throughout his academic career. He earned his bachelors, masters and doctorate degree in Computer Science on Intelligent Systems in Social Networks. He holds PG Diploma in Management also. His areas of research interest are Business Analytics, Machine Learning, Data Mining, and Information Systems. He has 40+ publications in his account and his research works has been published in reputed journals with high impact factor and SCI/SCIE/Scopus/WoS indexing. His research papers can be found at Computer Standards & Interfaces (SCI, Elsevier), Journal of Ambient Intelligence and Humanized Computing (SCIE, Springer). Also, he has authored/edited various books with reputed publishers i.e. Springer, Elsevier, De Gruyter, and IGI Global. He has been invited as Keynote Speaker at VIT Vellore in 2019 for an International Virtual Conference. He filed one patent in the field of Solar Energy equipment using Artificial Intelligence in 2019. He is an editorial member/reviewer of various reputed SCI/SCIE journals i.e. Computer Communications (Elsevier), ACM Transactions on Internet Technology, Cluster Computing (Springer), Neural Computing and Applications (Springer), Journal of Intelligent Manufacturing (Springer), Multimedia Tools & Applications (Springer), Computational Intelligence (Wiley), Australasian Journal of Information Systems (AJIS, International Journal of Decision Support System Technology (IGI Global USA), International Journal of Image Mining (Inderscience). He has supervise