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Cyber-Physical Systems: AI and COVID-19

  • ID: 5230589
  • Book
  • June 2021
  • 320 Pages
  • Elsevier Science and Technology
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Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS).

The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture.

  • Offers perspectives on the design, development and commissioning of intelligent applications
  • Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of COVID-19
  • Puts forth insights on how future illnesses can be supported using intelligent corona virus monitoring techniques
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Part I Intelligent Sensing and Applications  1. Notable Application for COVID-19 2. Medical Status Monitoring Application for COVID-19 3. Medication Intake Application for COVID-19 4. Challenges of Mobile Health Application for COVID-19 5. RFID Application for COVID-19 6. Smart policing and surveillance for COVID-19 7. Patient tracking, patient monitoring and hospital management 8. Smart-Phone based Tracking Systems for Patients of COVID-19 9. GIS and GPS System for monitoring Isolation and Quarantine Centers 10. Effective Record Maintenance Systems and Dashboard for COVID-19  11. Predictive and Forecasting Systems for COVID-19 12. Android-Based Telemedicine System for Patient-Monitoring for COVID-19 13. Body Sensor Networks: Challenges, Solutions, and Research Directions 14. Pervasive Health Monitoring Systems for COVID-19 15. Remote Detection and Prediction for COVID-19 16. Design and Deployment of a Mobile-Based Medical Alert System

Part II: Interdisciplinary Cyber Physical Systems 17. Multi domain decision support system for COVID-19 18. Internet of Things for COVID-19 19. Social media analytics for COVID-19 20. Data analytics for analyzing disruption from the spread of COVID-19 21. Big-data methods for mitigating the impact of COVID-19  22. Artificial Intelligence/machine learning model for COVID-19 23. New datasets for analysis of COVID-19 24. Medical image analysis for COVID-19 25. Intelligent health informatics for COVID-19 26. CPS based solutions for detection, control and prediction of COVID-19 27. Development of robots and humanoids for patient care 28. CPS Integration systems tools for COVID-19

Part III Cyber-physical modelling and simulation 29. Identification techniques in modelling and simulation 30. Modelling and simulation of sensor-based autonomous systems for COVID-19 31. Modelling and simulation of intelligent interaction using robotic systems for COVID-19 32. Mathematical modeling and forecasting of epidemic spreading of COVID-19 33. Modelling and simulation of COVID-19 34. A stochastic mathematical modelling for COVID-19 35. Mathematical models of the spread and consequences of the COVID-19

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Poonia, Ramesh Chandra
Dr. Ramesh Chandra Poonia is a Postdoctoral Fellow at CPS Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Ålesund, Norway. He has received his Ph.D. degree in Computer Science from Banasthali University, Banasthali, India, in July 2013. His research interests are Sustainable Technologies, Cyber-Physical Systems, Internet of Things, and Network Protocol Evaluation. He is Chief Editor of TARU Journal of Sustainable Technologies and Computing (TJSTC) and Associate Editor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier. He also serves on the editorial boards of a few international journals. He is the main author and co-author of 06 books and an editor of more than 25 special issues of journals and books including Springer, CRC Press - Taylor and Francis, IGI Global and Elsevier, edited books and Springer conference proceedings and has authored/co-authored over 65 research publications in peer-reviewed reputed journals, book chapters, and conference proceedings.
Agarwal, Basant
Dr. Basant Agarwal working as an Assistant Professor at Indian Institute of Information Technology (IIIT) Kota. Worked as Postdoctoral Fellow at Department of Computer Science, Norwegian University of Science and Technology. (NTNU), Norway under European Research Consortium for Informatics and. Mathematics (ERCIM) Fellowship program. Awarded Ph.D. on topic "Prominent Features Extraction for Sentiment Analysis” from Malaviya National Institute of Technology, Jaipur, Rajasthan. Worked as a Research Assistant at Temasek Laboratories, National University of Singapore (NUS), Singapore. Worked as an Assistant Professor, Department of Computer Science and Engineering, Central University of Rajasthan. Worked as an Assistant Professor, Lovely Professional University, Jalandhar, Punjab. Worked as Teaching Assistant at MNIT during Ph.D. against scholarship from Ministry of Human Resource Development, Government of India. Teaching Assistantship in MNIT during MTech.
Kumar, Sandeep
Dr. Sandeep Kumar received the B.E. degree in computer science and engineering from Engineering College Kota, in 2005, the M.Tech. degree in computer science and engineering from RTU Kota, in 2011, and the Ph.D. degree in the field of nature inspired computing. He is currently an Assistant Professor with the Department of Computer Science and Engineering, Amity University, Jaipur. He has published more than 50 research articles in refereed journals and international conferences, and edited two books and two conference proceedings. His areas of interest are theoretical computer science, swarm intelligence, and evolutionary computing. He is also working on Artificial Bee Colony algorithm, differential evolution, and Spider Monkey optimization algorithms.
Khan, Mohammad S.
Dr. Mohammad S. Khan (SM' 19) is currently an Assistant Professor of Computing at East Tennessee State University and the director of Network Science and Analysis Lab (NSAL). He received his M.Sc. and Ph.D. in Computer Science and Computer Engineering from the University of Louisville, Kentucky, USA, in 2011 and 2013, respectively. His primary area of research is in ad-hoc networks, wireless sensor networks, network tomography, connected vehicles, and vehicular social networks. He currently serves as an associate editor of IEEE Access, IET ITS, IET WSS, Springer's Telecommunication Systems and Neural Computing and Applications. He has been on technical program committees of various international conferences and technical reviewer of various international journals in his field. He is a senior member of IEEE.
Marques, Goncalo
Dr. Gonçalo Marques holds a BSc in Computer Science Engineering (2013) and an MSc in Mobile Computing (2015). He received a PhD degre in Computer Science Engineering at the University of Beira Interior (2020). Gonçalo is actually a researcher member of the Instituto de Telecomunicações at the University of Beira Interior. Furthermore, he is working as a Senior Software Engineer and is a licensed Professional Engineer. His current research interests include Internet of Things, Ambient Assisted Living, Enhanced Living Environments, e-Health, medical and healthcare systems, indoor air quality monitoring and assessment, noise monitoring, autonomous systems, and wireless sensor networks. Furthermore, authored or co-authored over 50 papers in journals and international conferences indexed in Scopus and Web of Science.
Nayak, Janmenjoy
Dr. Janmenjoy Nayak is working as an Associate Professor, Aditya Institute of Technology and Management (AITAM), (An Autonomous Institution) Tekkali, K Kotturu, AP- 532201, India. Being two times Gold Medallist in Computer Science in his career, he has been awarded with INSPIRE Research Fellowship from Department of Science & Technology, Govt. of India (both as JRF and SRF level) and Best researcher award from Jawaharlal Nehru University of Technology, Kakinada, Andhra Pradesh for the AY: 2018-19. He has edited nine books and seven Special Issues on the applications of Computational Intelligence, Soft Computing, data analytics and pattern recognition, published by Elsevier, Springer, Inderscience International publications. He has published more than 90 referred articles in various book chapters, conferences and International repute peer reviewed journals of Elsevier, Inderscience, Springer, IEEE etc. He is the regular member of IEEE and life member of some of the reputed societies like CSI India, Orissa Information Technology Society (OITS), Orissa Mathematical Society (OMS), IAENG (Hongkong) etc. He has successfully conducted and is being associated with International repute series conferences like ICCIDM, HIS, ARIAM, CIPR, SCDA etc. His area of interest includes data mining, nature inspired algorithms and soft computing.
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