Cyber-Physical Systems
AI and COVID-19
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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.
Key Features
- 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
Readership
Researchers or senior graduates working in academia; academics, instructors and senior students in colleges and universities, and faculty members working on Mathematics and Computing Technologies
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter 1. AI-based implementation of decisive technology for prevention and fight with COVID-19
- Abstract
- 1.1 Introduction
- 1.2 Related work
- 1.3 Proposed work
- 1.4 Results and analysis
- 1.5 Conclusion
- References
- Chapter 2. Internet of Things-based smart helmet to detect possible COVID-19 infections
- Abstract
- 2.1 Introduction
- 2.2 Related work
- 2.3 IoT-based smart helmet to detect the infection of COVID-19
- 2.4 Conclusion
- References
- Chapter 3. Role of mobile health in the situation of COVID-19 pandemics: pros and cons
- Abstract
- 3.1 Introduction
- 3.2 Implementation of a training module for the mHealth care worker
- 3.3 Government policies for the scale-up of the mHealth services
- 3.4 Popular models of mHealth serving for pandemic COVID-19
- 3.5 Ethical consideration
- 3.6 Superiority of mHealth services over other available services
- 3.7 Probability of conflict of interest between user and service provider
- 3.8 Legal consideration
- 3.9 Protection of privacy of end-users
- 3.10 Conclusion
- 3.11 Future prospects
- References
- Chapter 4. Combating COVID-19 using object detection techniques for next-generation autonomous systems
- Abstract
- 4.1 Introduction
- 4.2 Need for object detection
- 4.3 Object detection techniques
- 4.4 Applications of objection detection during COVID-19 crisis
- 4.5 Conclusion
- References
- Chapter 5. Non-contact measurement system for COVID-19 vital signs to aid mass screening—An alternate approach
- Abstract
- 5.1 Introduction
- 5.2 COVID-19 global scenarios
- 5.3 Measurement and testing protocols of COVID-19
- 5.4 Non-contact approaches to physiological measurement
- 5.5 Conclusion
- Acknowledgment
- References
- Chapter 6. Evolving uncertainty in healthcare service interactions during COVID-19: Artificial Intelligence - a threat or support to value cocreation?
- Abstract
- 6.1 Introduction
- 6.2 Service dominant logic in marketing
- 6.3 Service interactions and cocreated wellbeing
- 6.4 Uncertainty due to pandemic
- 6.5 Uncertainty in healthcare
- 6.6 The emerging role of Artificial Intelligence
- 6.7 AI combating uncertainty and supporting value cocreation in healthcare interactions
- 6.8 The spill-over effect of Artificial Intelligence
- 6.9 Conclusion and future work
- References
- Chapter 7. The COVID-19 outbreak: social media sentiment analysis of public reactions with a multidimensional perspective
- Abstract
- 7.1 Introduction
- 7.2 Data collection
- 7.3 Sentiment analysis of the tweets collected worldwide
- 7.4 Sentiment analysis of Tweets for India
- 7.5 Analysis of few most trending hashtags
- 7.6 Conclusion
- References
- Chapter 8. A new approach to predict COVID-19 using artificial neural networks
- Abstract
- 8.1 Introduction
- 8.2 Related studies
- 8.3 Fundamental symptoms and conditions responsible for COVID-19 infection
- 8.4 Proposed COVID-19 detection methodology
- 8.5 Brief description of artificial neural networks
- 8.6 Parameter settings for the proposed ANN model
- 8.7 Experimental results and discussion
- 8.8 Performance comparison between ANN and other classification algorithms
- 8.9 Conclusion
- Appendix
- References
- Chapter 9. Rapid medical guideline systems for COVID-19 using database-centric modeling and validation of cyber-physical systems
- Abstract
- 9.1 Introduction
- 9.2 Global pandemic of COVID-19
- 9.3 Database-centric cyber-physical systems for COVID-19
- 9.4 Modeling and validation of rapid medical guideline systems
- 9.5 Conclusion
- References
- Chapter 10. Machine learning and security in Cyber Physical Systems
- Abstract
- 10.1 Introduction
- 10.2 Related work
- 10.3 Motivation
- 10.4 Importance of cyber security and machine learning
- 10.5 Machine learning for CPS applications
- 10.6 Future for CPS technology
- 10.7 Challenges and opportunities in CPS
- 10.8 Conclusion
- References
- Chapter 11. Impact analysis of COVID-19 news headlines on global economy
- Abstract
- 11.1 Introduction
- 11.2 Related work
- 11.3 Proposed methodology
- 11.4 Results and experimental framework
- 11.5 Conclusion
- References
- Further reading
- Chapter 12. Impact of COVID-19: a particular focus on Indian education system
- Abstract
- 12.1 Introduction
- 12.2 Impact of COVID-19 on education
- 12.3 Sustaining the education industry during COVID-19
- 12.4 Conclusion
- References
- Chapter 13. Designing of Latent Dirichlet Allocation Based Prediction Model to Detect Midlife Crisis of Losing Jobs due to Prolonged Lockdown for COVID-19
- Abstract
- 13.1 Introduction
- 13.2 Literature survey
- 13.3 Methodology
- 13.4 Result and discussion
- 13.5 Conclusion and future scope
- References
- Chapter 14. Autonomous robotic system for ultraviolet disinfection
- Abstract
- 14.1 Introduction
- 14.2 Background
- 14.3 Implementation
- 14.4 Model topology
- 14.5 Conclusion
- References
- Chapter 15. Emerging health start-ups for economic feasibility: opportunities during COVID-19
- Abstract
- 15.1 Introduction
- 15.2 Health-tech verticals for start-ups
- 15.3 Research gap
- 15.4 Aim of the study
- 15.5 Research methodology
- 15.6 Health-tech category I Indian start-ups
- 15.7 Conclusions
- References
- Index
Product details
- No. of pages: 278
- Language: English
- Copyright: © Academic Press 2021
- Published: October 30, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128245576
- eBook ISBN: 9780323853576
About the Editors
Ramesh Poonia
Dr. Ramesh Chandra Poonia is an Associate Professor at the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. Recently completed his Postdoctoral Fellowship from 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 Cyber-Physical Systems, Network Protocol Evaluation and Artificial Intelligence. 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 in the editorial boards of a few international journals. He is main author and co-author of 06 books and an editor of more than 25 special issue 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.
Affiliations and Expertise
Associate Professor
Department of Computer Science,CHRIST (Deemed to be University), Bangalore, Karnataka, India
Basant Agarwal
Dr. Basant Agarwal works as an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India, which is an Institute of National Importance. He holds a Ph.D. and M.Tech. from the Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India. He has more than 9 years of experience in research and teaching. He has worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. His research interest include Artificial Intelligence, Cyber physical systems, Text mining, Natural Language Processing, Machine learning, Deep learning, Intelligent Systems, Expert Systems and related areas.
Affiliations and Expertise
Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India.
Sandeep Kumar
Dr. Sandeep Kumar is currently an Associate Professor at CHRIST (Deemed to be University) Bangalore and a Part-time Post-Doctoral research fellow at Imam Muhammad ibn Saud Islamic University Saudi Arabia. Before joining CHRIST, he has worked with ACEIT Jaipur, Jagannath University Jaipur, and Amity University Rajasthan. He is an associate editor for the Human-centric Computing and Information Sciences (HCIS) journal published by Springer. He has published more than sixty research papers in various international journals/conferences and attended several national and international conferences and workshops. He has authored/edited five books in the area of computer science. His research interests include nature-inspired algorithms, swarm intelligence, soft computing, and computational intelligence
Affiliations and Expertise
Associate Professor, Department of Computer Science and Engineering, CHRIST (Deemed to be University)
Mohammad Khan
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.
Affiliations and Expertise
Assistant Professor of Computing, East Tennessee State University and Director, Network Science and Analysis Lab (NSAL), Johnson City , Tennessee, USA
Goncalo Marques
Gonçalo Marques holds a PhD in Computer Science Engineering and is member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia and database systems. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences and is also involved in several edited books projects.
Affiliations and Expertise
Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
Janmenjoy Nayak
Dr. Janmenjoy Nayak is working as an Assistant Professor, Department of Computer Science, Maharaja Sriram Chandra BhanjaDeo University, Baripada, Odisha, 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, AP for the AY: 2018-19 and many other awards from national and international academic agencies. He has edited 21 books and 8 Special Issues on the applications of Computational Intelligence, Soft Computing, and pattern recognition, published by Springer, Inderscience International publications. He has published more than 170+ referred articles in various book chapters, conferences and International repute peer reviewed journals of Elsevier, Inderscience, Springer, IEEE etc. He is serving as Associate editor of IEEE ACCESS journal and reviewer for over 100 well-reputed journals and conferences, from IEEE, ACM, Springer, Elsevier, Wiley, and Inderscience publishers. He has a total twelve years of experience in both teaching and research. He is the senior member of IEEE and life member of some of the reputed societies like Soft Computing Research Society (SCRS), CSI India, Orissa Information Technology Society (OITS), Orissa Mathematical Society (OMS), IAENG (Hongkong) etc. He has successfully conducted and is being associated with 14 International repute series conferences like ICCIDM, HIS, ARIAM, CIPR, SCDA etc. His area of interest includes data mining, nature inspired algorithms and Applied Artificial Intelligence.
Affiliations and Expertise
Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCBD) University, Mayurbhanj, India