Elsevier Cyber-Physical Systems: AI and COVID-19

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