Autonomous service for managing real time notification in detection of COVID-19 virus

Yousef Methkal Abd Algani, K. Boopalan, G. Elangovan, D. Teja Santosh, K. Chanthirasekaran, Indrajit Patra, N. Pughazendi, B. Kiranbala, R. Nikitha, M. Saranya

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In today's world, the most prominent public issue in the field of medicine is the rapid spread of viral sickness. The seriousness of the disease lies in its fast spreading nature. The main aim of the study is the proposal of a framework for the earlier detection and forecasting of the COVID-19 virus infection amongst the people to avoid the spread of the disease across the world by undertaking the precautionary measures. According to this framework, there are four stages for the proposed work. This includes the collection of necessary data followed by the classification of the collected information which is then taken in the process of mining and extraction and eventually ending with the process of decision modelling. Since the frequency of the infection is very often a prescient one, the probabilistic examination is measured as a degree of membership characterised by the fever measure related to the same. The predictions are thereby realised using the temporal RNN. The model finally provides effective outcomes in the efficiency of classification, reliability, the prediction viability etc.

Original languageEnglish
Article number108117
JournalComputers and Electrical Engineering
StatePublished - Jul 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd


  • COVID-19, Fog computing
  • Cloud computing
  • Internet of Things
  • Temporal RNN, Virus


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