Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.
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1. Essentials of COVID-19 Coronaviruses 2. Molecular Docking Study of Transmembrane serine protease type-2 inhibitors for the treatment of covid-19 3. Gut-lung crosstalk in COVID-19 pathology and fatality rate 4. Data Sharing and Privacy Issues Arising with COVID-19 Data and Applications 5. COVID-19 Outlook in the United States of America: A Data Driven Thematic Approach 6. Artificial Intelligence and COVID-19: Fighting Pandemics 7. Data Science A Survey on Statistical Analysis of the Latest Outbreak of 2019 Pandemic Novel Corona virus Disease (COVID-19) using ANOVA 8. Application of Big Data in the COVID-19 Pandemic 9. Artificial Intelligence based Solutions for COVID-19 10. Telemedicine applications for pandemic diseases with a focus on COVID-19 11. Impact of COVID-19 and Lockdown Policies on Farming, Food Security and Agribusiness in West Africa 12. Study and Impact Analysis of COVID-19 Pandemic Clinical Data on Infection Spreading 13. Towards Analyzing the Impact of HealthCare Treatments in Industry 4.0 Environment - A self-care case study during Covid-19 Outbreak 14. Big Data Processing and Analysis on the Impact of COVID-19 on Public Transport Delay 15. The Role of Societal Research and Development Center in Analyzing Society in Pandemic Times 16. Modelling and Predicting the Spread of COVID-19: A Continental Analysis 17. Applications of BIM for Disease Spread Assessment due to the Organisation of Building Artefacts 18. COVID-19 DIAGNOSIS-MYTHS AND PROTOCOLS 19. Quarantine within Quarantine: COVID-19 and GIS Scenario Dynamics Modelling in Tasmania, Australia 20. Essentials of COVID-19 and Treatment Approaches 21. Coronavirus Epidemic and Its Social / Mental Dimensions 22. Coronavirus: A Scientometric Study of World Research Publications 23. The Effects of COVID-19 Pandemic on Western Balkan Financial Markets 24. Prioritization of health emergency research and disaster preparedness: a systematic assessment of corona virus disease 2019 (COVID-19) pandemic 25. A Review on Epidemiology, Genomic Characteristics, Spread and Treatments of COVID-19 26. Control of antibiotic resistance and super infections as a strategy to manage COVID-19 deaths 27. Assessment of global research trends in the application of data science, deep and machine learning to COVID-19 pandemic 28. Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using Neural Network, Random Forest and molecular docking 29. The linkage between epidemic of COVID-19 and oil prices: Case of Saudi Arabia, January 22 - April 17 30. Role of Big Geospatial Data in the COVID-19 Crisis 31. COVID-19: Will it be a Game Changer in Higher Education in India? 32. Are Northern and Southern Regions equally affected by the COVID-19 Pandemic? Empirical Evidence from Nigeria 33. Covid-19 lethality reduction using Artificial Intelligence Solutions derived from Telecommunications Systems 34. The significance of Daily Increase and Mortality Cases due to COVID-19 in some African Countries 35. Data Interpretation Leading to Image Processing: A Hybrid Perspective to A Global Pandemic- COVID19 36. COVID-19: Monitoring the pandemic in India 37. Potential Antiviral Therapies for Corona Virus Disease (COVID-19)
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Examines the methods and applications of Data Science on the societal and medical impacts of COVID-19
Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics
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Produktdetaljer

ISBN
9780323907699
Publisert
2021-10-25
Utgiver
Vendor
Academic Press Inc
Vekt
1650 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
812

Om bidragsyterne

Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science. Dr. Deepak Gupta is an assistant professor at Maharaja Agrasen Institute of Technology, Delhi, India. He is an eminent academician, including roles as lecturer, researcher, consultant, community service, PhD, and post-doctorate supervision. Dr. Gupta focuses on rational and practical learning and has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning, and Soft Computing. Dr. Gupta has authored/edited a number of books, including Emerging Trends and Roles of Fog, Edge, and Pervasive Computing in Intelligent IoT-Driven Applications, Wiley; Advanced Machine Intelligence and Signal Processing, Springer; Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press; Explainable Edge AI: A Futuristic Computing Perspective, Springer; Applications of Big Data in Healthcare, Elsevier/Academic Press; and Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; among others. Victor Hugo C. de Albuquerque [M’17, SM’19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic. Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is one of the founders of Bhavya Publications and the Universal Innovator Lab, which is actively involved in research, innovation, conferences, start-up funding events, and workshops. He is currently working at the Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain.