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Artificial intelligence computing analysis of fractional order COVID-19 epidemic model

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dc.contributor.author Raza, Ali
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Cheema, Tahir Nawaz
dc.contributor.author Fadhal, Emad
dc.contributor.author Ibrahim, Rashid I. H.
dc.contributor.author Abdelli, Nouara
dc.date.accessioned 2023-11-28T08:11:32Z
dc.date.available 2023-11-28T08:11:32Z
dc.date.issued 2023-08-01
dc.identifier.citation Raza, Ali...et.al. (2023). "Artificial intelligence computing analysis of fractional order COVID-19 epidemic model", AIP Advances, Vol.13, No.8. tr_TR
dc.identifier.issn 21583226
dc.identifier.uri http://hdl.handle.net/20.500.12416/6656
dc.description.abstract Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances in machine learning. Machine learning is a sphere of artificial intelligence where machines are responsible for doing daily chores and are believed to be more intelligent than humans. Furthermore, artificial intelligence is significant in behavioral, social, physical, and biological engineering, biomathematical sciences, and many more disciplines. Fractional-order modeling of a real-world problem is a powerful tool for understanding the dynamics of the problem. In this study, an investigation into a fractional-order epidemic model of the novel coronavirus (COVID-19) is presented using intelligent computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs are exploited to predict the transmission dynamics of COVID-19 disease by taking the dataset from a fractional numerical method based on the Grünwald-Letnikov backward finite difference. The datasets for the fractional-order mathematical model of COVID-19 for Wuhan and Karachi metropolitan cities are trained with BRBFNs for biased and unbiased input and target values. The proposed technique (BRBFNs) is implemented to estimate the integer and fractional-order COVID-19 spread dynamics. Its reliability, effectiveness, and validation are verified through consistently achieved accuracy metrics that depend on error histograms, regression studies, and mean squared error. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1063/5.0163868 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Artificial Intelligence tr_TR
dc.subject Artificial Neural Networks tr_TR
dc.subject Fractional Calculus tr_TR
dc.subject Mathematical Modeling tr_TR
dc.subject Coronaviruses tr_TR
dc.title Artificial intelligence computing analysis of fractional order COVID-19 epidemic model tr_TR
dc.type article tr_TR
dc.relation.journal AIP Advances tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 13 tr_TR
dc.identifier.issue 8 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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