A novel approach based to neural network and flower pollination algorithm to predict number of Covıd-19 cases

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dc.contributor.author Baştemur Kaya, Ceren
dc.contributor.author Kaya, Ebubekir
dc.date.accessioned 2022-06-17T07:24:47Z
dc.date.available 2022-06-17T07:24:47Z
dc.date.issued 2021
dc.identifier.citation Kaya, C. B., & Ebubekir, K. A. Y. A. (2021). A Novel Approach Based to Neural Network and Flower Pollination Algorithm to Predict Number of COVID-19 Cases. Balkan Journal of Electrical and Computer Engineering, 9(4), 327-336. tr_TR
dc.identifier.uri https://dergipark.org.tr/en/download/article-file/1749720
dc.identifier.uri http://hdl.handle.net/20.500.11787/7091
dc.description.abstract Flower Pollination Algorithm (FPA) is one of the popular heuristic algorithms that model pollination in the natural environment. Since 2012, it has been used in the solution of many difficult real world problems and successful results have been achieved. In this study, FPA is utilized for the training of neural network to predict number of COVID-19 cases. Namely, a model based on FPA and neural network (FPA_NN) is proposed. Within the scope of application, the data belonging to Turkey are estimated using the proposed model. A data set is created with the data between 1 April 2020 and 15 September 2020. A time series is created with these data and the nonlinear dynamic systems are obtained to model the problem. In order to determine the performance of the proposed model, RMSE (root mean square error) are used. The output graphs of the results are also examined in detail. The results are compared with neural network approaches based on PSO and HS. The Wilcoxon signed rank test is utilized to determine the significance of the results. The results show that FPA is generally more effective than PSO and HS to predict number of COVID-19 cases based on neural network tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.17694/bajece.932391 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject COVID-19 tr_TR
dc.subject Flower pollination algorithm tr_TR
dc.subject Neural network tr_TR
dc.subject Swarm intelligence tr_TR
dc.title A novel approach based to neural network and flower pollination algorithm to predict number of Covıd-19 cases tr_TR
dc.type article tr_TR
dc.relation.journal Balkan journal of electrical & computer engineering tr_TR
dc.contributor.department Nevşehir Hacı Bektaş Veli Üniversitesi/meslek yüksekokulu/bilgisayar teknolojileri bölümü/bilgisayar programcılığı pr. tr_TR
dc.contributor.authorID 108482 tr_TR
dc.identifier.volume 9 tr_TR
dc.identifier.issue 4 tr_TR
dc.identifier.startpage 327 tr_TR
dc.identifier.endpage 336 tr_TR


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