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Pandemic Detection and Analysis Through Smart Computing Technologies

Pandemic Detection and Analysis Through Smart Computing Technologies

  • 定價:7998
  • 使用購物金最高可抵100詳情

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This powerful new volume explores the diverse and sometimes unexpected roles that IoT and AI technologies played during the recent COVID-19 global pandemic. The book discusses the how existing and new state-of-the art technology has been and can be applied for global health crises in a multitude of ways.

The chapters in Pandemic Detection and Analysis through Smart Computing Technologies look at exciting technological solutions for virus detection, prediction, classification, prevention, and communication outreach. The book considers the various modes of transmission of the virus as well as how technology has been implemented for personalized healthcare systems and how it can be used for future pandemics.

The huge importance of social and mobile communication and networks during the pandemic is addressed such as in business, education, and healthcare; in research and development; for health information and outreach; in social life; and more. A chapter also addresses using smart computing for forecasting the damage caused by COVID-19 using time series analyses.

This up-to-the-minute volume illuminates on the many ways AI, IoT, machine learning, and other technologies have important roles in the diverse challenges faced during COVID-19 and how they can be enhanced for future pandemic situations. The volume will be of high interest to those in different fields of computer science and other domains as well as to data scientists, government agencies and policymakers, doctors and healthcare professionals, engineers, economists, and many other professionals. This book will also be very helpful to faculty, students, and research scholars in understanding the pre- and post-effect of this pandemic.



Ram Shringar Raw, PhD, is working in the Department of Computer Science and Engineering of Netaji Subhas University of Technology, East Campus, Delhi, India. He formerly worked as an Associate Professor in the Department of Computer Science, Indira Gandhi National Tribal University (A Central University, MP). He has more than 18 years of teaching, administrative, and research experience. Currently, he is associated with a wide range of journals and conferences as chief editor, editor, chair, and member. He has guided many MTech and PhD students for their dissertation and thesis. Currently, he is guiding two MTech and four PhD students. His current research interest includes mobile ad hoc networks, vehicular ad hoc networks, and IoT cloud-based networks. Dr. Raw has published two books and more than 100 research papers in international journals and conferences, including those from IEEE, Elsevier. Springer, Wiley & Sons, Taylor & Francis, Inderscience, Hindawi, IERI Letters, American Institute of Physics, etc.

Vishal Jain, PhD, is Associate Professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P., India. Before that, he has worked for several years as Associate Professor at Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 14 years of experience in the academics. He has more than 400 research citation indices with Google Scholar (h-index score 10 and i-10 index 11). He has authored more than 85 research papers in conferences and journals, including the Web of Science and Scopus. He has authored and edited more than 10 books with various reputed publishers, including Springer, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald, and IGI-Global. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012-13 from the Computer Society of India and a Best Faculty Award for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi. He holds PhD (CSE), MTech (CSE), MBA (HR), MCA, MCP and CCNA.

Sanjoy Das, PhD, is Associate Professor and Head, Department of Computer Science, Indira Gandhi National Tribal University (a Central Government University), Amarkantak, M.P. (Manipur Campus), India. Before joining IGNTU he worked as an Associate Professor at the School of Computing Science and Engineering, Galgotias University, India, as well as Assistant Professor. He was also Assistant Professor at G. B. Pant Engineering College, Uttarakhand, and Assam University, Silchar, India. His current research interests include mobile ad hoc networks and vehicular ad hoc networks, distributed systems, and data mining. He has published numerous papers in international journals and conferences, including those associated with IEEE and Springer. He did his BE, MTech, and PhD degrees in Computer Science.

Meenakshi Sharma, PhD, is Dean and Professor in the School of Computer Science & Engineering of the University Center of Research & Development (UCRD) at Galgotias University, Greater Noida, India. She has over 16 years of experience in teaching and research. She is a Senior Member of IEEE and is a highly qualified professional with an MTech in Computer Science & Engineering and a PhD in Computer Science (both from Kurukshetra University). Her research interests are machine learning, image processing, big data analytics, data compression, and digital and data warehousing. She has published over 60 research papers in IEEE Transaction, SCIE, SCI and Scopus in machine learning, deep learning, and AI, in collaboration with international authors. She has published four international grant patents and seven Indian patents. She was awarded a Best Research and Teacher Award in 2017 and 2018. Dr. Sharma has capably guided three PhD candidates and 40+ students in undergraduate/postgraduate programs, with five currently under guidance. She is valuable member of various engineering societies, including ISTE, ACM, InSc, ISDS Society, Japan, IEAE, and many others.



  • ISBN:9781774910320
  • 規格:精裝 / 310頁 / 普通級 / 初版
  • 出版地:美國




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