Evaluation of The Classification Performance of Methods Developed in ANN Training

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dc.authorid0000-0001-9012-7950en_US
dc.authorid0000-0002-5743-4593en_US
dc.contributor.authorYasar, Ali
dc.contributor.authorSaritas, Ismail
dc.date.accessioned2023-01-29T16:18:46Z
dc.date.available2023-01-29T16:18:46Z
dc.date.issued2021en_US
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, the performance and success of some models used in the training of artificial neural networks are compared. The UCI machine learning database was used for the performance evaluation and comparison process and for the IRIS dataset), which we often encounter in performance evaluations. In the study, the classification performances of our data sets were calculated using Feedforward Neural Network Cross-Validation (FNN-CV), Probabilistic Neural Network Cross-Validation (PNN-CV) and Recurrent Neural Network Cross-Validation (RNNCV) learning techniques. Our data has been classified using a 10-fold cross validation technique. The RNN-CV method for the IRIS data set proved to be a good classification method by achieving 100% accuracy success which shows that it should be used in classification problems.en_US
dc.identifier.citationYasar, A., Saritas, I., (2021). Evaluation of The Classification Performance of Methods Developed in ANN Training. Selcuk University Journal of Engineering Sciences, 20 (01), 1-6.en_US
dc.identifier.endpage6en_US
dc.identifier.issn2757-8828en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/45055
dc.identifier.volume20en_US
dc.institutionauthorYasar, Ali
dc.institutionauthorSaritas, Ismail
dc.language.isoenen_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofSelcuk University Journal of Engineering Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neural Networken_US
dc.subjectCross-Validationen_US
dc.subjectFeedforward Neural Networken_US
dc.subjectProbabilistic Neural Networken_US
dc.subjectRecurrent Neural Networken_US
dc.titleEvaluation of The Classification Performance of Methods Developed in ANN Trainingen_US
dc.typeArticleen_US

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