DESIGN OF NEURAL PREDICTORS FOR PREDICTING AND ANALYSING COVID-19 CASES IN DIFFERENT REGIONS

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dc.authoridYILDIRIM, SAHIN/0000-0002-7149-3274
dc.contributor.authorYildirim, S.
dc.contributor.authorDurmusoglu, A.
dc.contributor.authorSevim, C.
dc.contributor.authorBingol, M. S.
dc.contributor.authorKalkat, M.
dc.date.accessioned2024-11-07T13:34:05Z
dc.date.available2024-11-07T13:34:05Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractNowadays, some unexpected viruses are affecting people with many troubles. COVID-19 virus is spread in the world very rapidly. However, it seems that predicting cases and death fatalities is not easy. Artificial neural networks are employed in many areas for predicting the system's parameters in simulation or real-time approaches. This paper presents the design of neural predictors for analysing the cases of COVID-19 in three countries. Three countries were selected because of their different regions. Especially, these major countries' cases were selected for predicting future effects. Furthermore, three types of neural network predictors were employed to analyse COVID-19 cases. NAR-NN is one of the pro-posed neural networks that have three layers with one input layer neurons, hidden layer neurons and an output layer with fifteen neurons. Each neuron consisted of the activation functions of the tan-sigmoid. The other proposed neural network, ANFIS, consists of five layers with two inputs and one output and ARIMA uses four iterative steps to predict. The proposed neural network types have been selected from many other types of neural network types. These neural network structures are feed-forward types rather than recurrent neural networks. Learning time is better and faster than other types of networks. Finally, three types of neural pre-dictors were used to predict the cases. The R2 and MSE results improved that three types of neural networks have good performance to predict and analyse three region cases of countries.
dc.identifier.doi10.14311/NNW.2022.32.014
dc.identifier.endpage251
dc.identifier.issn1210-0552
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85151348042
dc.identifier.scopusqualityQ4
dc.identifier.startpage233
dc.identifier.urihttps://doi.org/10.14311/NNW.2022.32.014
dc.identifier.urihttps://hdl.handle.net/11480/15784
dc.identifier.volume32
dc.identifier.wosWOS:000932050700001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcad Sciences Czech Republic, Inst Computer Science
dc.relation.ispartofNeural Network World
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectCOVID-19
dc.subjectNAR-NN
dc.subjectANFIS
dc.subjectARIMA
dc.subjectprediction
dc.subjectmodelling of the pandemic
dc.titleDESIGN OF NEURAL PREDICTORS FOR PREDICTING AND ANALYSING COVID-19 CASES IN DIFFERENT REGIONS
dc.typeArticle

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