ANN Assisted-IoT Enabled COVID-19 Patient Monitoring; IEEE Access; Vol. 9

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring; IEEE Access; Vol. 9

Bibliographic Details
Parent link:IEEE Access
Vol. 9.— 2021.— [P. 42483-42492]
Corporate Author: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Научно-образовательный центр "Автоматизация и информационные технологии"
Other Authors: Ratkhor G. Gitandzhali, Garg S. Sakhil, Kaddum Zh. Zhorzh, Vu Yuley, Dzhayakodi (Jayakody) Arachshiladzh D. N. K. Dushanta Nalin Kumara, Alamri A. M. Atif M
Summary:Title screen
COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification 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. Artificial 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 classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.
Language:English
Published: 2021
Subjects:
труды учёных ТПУ
электронный ресурс
COVID-19
medical services
artificial intelligence
viruses (medical)
diseases
pandemics
viterbi algorithm
медицинские услуги
искусственный интеллект
вирусы
болезни
пандемии
Online Access:http://earchive.tpu.ru/handle/11683/72785
https://doi.org/10.1109/ACCESS.2021.3064826
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664985

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