Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

dc.contributor.authorAlqarni, Manal M.
dc.contributor.authorNasir, Arooj
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorRaza, Ali
dc.contributor.authorCheema, Tahir Nawaz
dc.contributor.authorAhmed, Nauman
dc.contributor.authorRafiq, Muhammad
dc.contributor.authorFatima, Umbreen
dc.contributor.authorMahmoud, Emad E.
dc.contributor.authorID56389tr_TR
dc.date.accessioned2024-01-18T13:09:02Z
dc.date.available2024-01-18T13:09:02Z
dc.date.issued2023
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractArtificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model's dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.en_US
dc.identifier.citationAlqarni, Manal M.;...et.al. "Optimization of Coronavirus Pandemic Model Through Artificial Intelligence", Computers, Materials and Continua, Vol.74, No.3 pp.6807-6822.en_US
dc.identifier.doi10.32604/cmc.2023.033283
dc.identifier.endpage6822en_US
dc.identifier.issn15462218
dc.identifier.issue3en_US
dc.identifier.startpage6807en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/6935
dc.identifier.volume74en_US
dc.language.isoenen_US
dc.relation.ispartofComputers, Materials and Continuaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnalysisen_US
dc.subjectArtificial Techniquesen_US
dc.subjectCoronavirus Modelen_US
dc.titleOptimization of Coronavirus Pandemic Model Through Artificial Intelligencetr_TR
dc.titleOptimization of Coronavirus Pandemic Model Through Artificial Intelligenceen_US
dc.typeArticleen_US
dspace.entity.typePublication

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