A novel radial basis neural network for the Zika virus spreading model

dc.authorscopusid 56184182600
dc.authorscopusid 59253796300
dc.authorscopusid 59253949000
dc.authorscopusid 57203870179
dc.authorscopusid 23028598900
dc.authorwosid Umar, Dr Muhammad/HOH-8319-2023
dc.contributor.author Sabir, Zulqurnain
dc.contributor.author Rada, Tino Bou
dc.contributor.author Kassem, Zeinab
dc.contributor.author Umar, Muhammad
dc.contributor.author Salahshour, Soheil
dc.date.accessioned 2024-09-11T07:40:52Z
dc.date.available 2024-09-11T07:40:52Z
dc.date.issued 2024
dc.department Okan University en_US
dc.department-temp [Sabir, Zulqurnain; Rada, Tino Bou; Kassem, Zeinab] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon; [Umar, Muhammad; Salahshour, Soheil] Istanbul Okan Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkiye en_US
dc.description.abstract The motive of current investigations is to design a novel radial basis neural network stochastic structure to present the numerical representations of the Zika virus spreading model (ZVSM). The mathematical ZVSM is categorized into humans and vectors based on the susceptible S(q), exposed E(q), infected I(q) and recovered R (q), i.e., SEIR. The stochastic performances are designed using the radial basis activation function, feed forward neural network, twenty-two numbers of neurons along with the optimization of Bayesian regularization in order to solve the ZVSM. A dataset is achieved using the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) based on the process of training for solving the nonlinear ZVSM. The division of the data is categorized into training, which is taken as 78%, while 11 % for both authentication and testing. Three different cases of the nonlinear ZVSM have been taken, while the scheme's correctness is performed through the matching of the results. Furthermore, the reliability of the scheme is observed by applying different performances of regression, MSE, error histograms and state transition. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.compbiolchem.2024.108162
dc.identifier.issn 1476-9271
dc.identifier.issn 1476-928X
dc.identifier.pmid 39116703
dc.identifier.scopus 2-s2.0-85200871456
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1016/j.compbiolchem.2024.108162
dc.identifier.uri https://hdl.handle.net/20.500.14517/6217
dc.identifier.volume 112 en_US
dc.identifier.wos WOS:001295919200001
dc.identifier.wosquality Q2
dc.institutionauthor Salahshour S.
dc.language.iso en
dc.publisher Elsevier Sci Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 9
dc.subject Zika virus en_US
dc.subject Radial basis en_US
dc.subject Bayesian regularization en_US
dc.subject Mean square error en_US
dc.subject Neural network en_US
dc.subject Numerical solutions en_US
dc.title A novel radial basis neural network for the Zika virus spreading model en_US
dc.type Article en_US
dc.wos.citedbyCount 8

Files