Placement score estimation of secondary education transition system (SETS) using artificial neural networks

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dc.contributor.authorUcar, E.
dc.contributor.authorSen, B.
dc.contributor.authorBayir, S.
dc.date.accessioned2024-09-29T16:22:12Z
dc.date.available2024-09-29T16:22:12Z
dc.date.issued2012
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study offers an approach based on artificial neural networks for predicting the placement score of secondary education transition system (SETS). Artificial neural networks have recently become a very important method in the classification and prediction of the problems. Therefore, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) which are among the most preferred artificial neural network architectures were used in this study. Created expert system was trained and tested on a database including 25000 randomly selected records of primary education 8th grade students. Results of this training and testing are comparatively presented within the study. © Sila Science.en_US
dc.identifier.endpage20en_US
dc.identifier.issn1308-772X
dc.identifier.issueSPEC .ISS.1en_US
dc.identifier.scopus2-s2.0-84885044915en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage13en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9872
dc.identifier.volume30en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofEnergy Education Science and Technology Part A: Energy Science and Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectRadial Basis Functionen_US
dc.subjectSETSen_US
dc.titlePlacement score estimation of secondary education transition system (SETS) using artificial neural networksen_US
dc.typeArticleen_US

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