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http://earchive.tpu.ru/handle/11683/72785
Полная запись метаданных
Поле DC | Значение | Язык |
---|---|---|
dc.contributor.author | Ratkhor, Gitandzhali | en |
dc.contributor.author | Garg, Sakhil | en |
dc.contributor.author | Kaddum, Zhorzh | en |
dc.contributor.author | Vu Yuley | en |
dc.contributor.author | Dzhayakodi (Jayakody) Arachshiladzh, Dushanta Nalin Kumara | en |
dc.contributor.author | Alamri, Atif M | en |
dc.date.accessioned | 2022-08-19T04:19:41Z | - |
dc.date.available | 2022-08-19T04:19:41Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | ANN Assisted-IoT Enabled COVID-19 Patient Monitoring / G. Ratkhor, S. Garg, Zh. Kaddum [et al.] // IEEE Access. — 2021. — Vol. 9. — [P. 42483-42492]. | en |
dc.identifier.uri | http://earchive.tpu.ru/handle/11683/72785 | - |
dc.description.abstract | 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. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | en |
dc.publisher | IEEE | en |
dc.relation.ispartof | IEEE Access. 2021. Vol. 9 | en |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.rights | Attribution-NonCommercial 4.0 International | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | - |
dc.source | IEEE Access | en |
dc.subject | медицинские услуги | ru |
dc.subject | искусственный интеллект | ru |
dc.subject | вирусы | ru |
dc.subject | болезни | ru |
dc.subject | пандемии | ru |
dc.subject | COVID-19 | en |
dc.subject | medical services | en |
dc.subject | artificial intelligence | en |
dc.subject | viruses (medical) | en |
dc.subject | diseases | en |
dc.subject | pandemics | en |
dc.subject | viterbi algorithm | en |
dc.title | ANN Assisted-IoT Enabled COVID-19 Patient Monitoring | en |
dc.type | Article | en |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dcterms.audience | Researches | en |
local.description.firstpage | 42483 | - |
local.description.lastpage | 42492 | - |
local.filepath | reprint-nw-36170.pdf | - |
local.filepath | https://doi.org/10.1109/ACCESS.2021.3064826 | - |
local.identifier.bibrec | RU\TPU\network\36170 | - |
local.identifier.perskey | RU\TPU\pers\37962 | - |
local.localtype | Статья | ru |
local.volume | 9 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3064826 | - |
Располагается в коллекциях: | Репринты научных публикаций |
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Файл | Описание | Размер | Формат | |
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reprint-nw-36170.pdf | 5,85 MB | Adobe PDF | Просмотреть/Открыть |
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