Autonomous service for managing real time notification in detection of COVID-19 virus. (July 2022)

• Record Type: Journal Article
• Authors: Algani, Yousef Methkal Abd; Boopalan, K.; Elangovan, G; Santosh, D. Teja; Chanthirasekaran, K.; Patra, Indrajit; Pughazendi, N.; Kiranbala, B.; Nikitha, R.; Saranya, M.
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Cloud computing -- COVID-19, Fog computing -- Internet of Things -- Temporal RNN, Virus; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108117
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The main objective of this research is to predict Covid-19 virus. IoT is used to collect real time notification data for preprocessing stage. RNN model is utilized to evaluate classification, reliability and prediction accuracy. Abstract: 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. Graphical abstract: Image, graphical abstract