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Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique

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dc.contributor.author Faiz, Zeshan
dc.contributor.author Javeed, Shumaila
dc.contributor.author Ahmed, Iftikhar
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Bilal Riaz, Muhammad
dc.contributor.author Sabir, Zulqurnain
dc.date.accessioned 2024-01-17T13:29:27Z
dc.date.available 2024-01-17T13:29:27Z
dc.date.issued 2023-07
dc.identifier.citation Faiz, Zeshan;...et.al. (2023). "Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique", Results in Physics, Vol.50. tr_TR
dc.identifier.issn 22113797
dc.identifier.uri http://hdl.handle.net/20.500.12416/6905
dc.description.abstract The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (An∗), Wolbachia-uninfected adult female mosquitoes (Fn∗), Wolbachia-infected aquatic mosquitoes (Aw∗), and Wolbachia-infected adult female mosquitoes (Fw∗). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.rinp.2023.106602 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Levenberg-Marquardt tr_TR
dc.subject Mathematical Model tr_TR
dc.subject Mean Square Error tr_TR
dc.subject Neural Network tr_TR
dc.subject Reference Solutions tr_TR
dc.subject Wolbachia tr_TR
dc.title Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique tr_TR
dc.type article tr_TR
dc.relation.journal Results in Physics tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 50 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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