Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8894
Title: ANN Assisted-IoT Enabled COVID-19 Patient Monitoring
Authors: Rathee, Geetanjali
Garg, Sahil
Kaddoum, Georges
Wu, Yulei
Jayakody, Dushantha Nalin K.
Alamri, Atif
Keywords: Artificial neural network
Back propagation network
Multi-perceptron layer
COVID 19 patients
Security in healthcare
Issue Date: 2021
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identi cation of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Arti cial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classi cation parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.
URI: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8894
Appears in Collections:Journal Articles

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