Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

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dc.contributor.authorAyyıldız, Mustafa
dc.contributor.authorÇetinkaya, Kerim
dc.date.accessioned2020-04-30T23:21:20Z
dc.date.available2020-04-30T23:21:20Z
dc.date.issued2017
dc.departmentDÜ, Teknik Eğitim Fakültesi, Makine Eğitimi Bölümüen_US
dc.descriptionWOS: 000414525000010en_US
dc.description.abstractIn this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg-Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients (R-2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.en_US
dc.description.sponsorshipUniversity of Karabuk [KBU-BAP-11-2-DR-001]en_US
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to thank the University of Karabuk (Project code: KBU-BAP-11-2-DR-001) for providing financial support to conduct this study.en_US
dc.identifier.doi10.1177/0954408916659310en_US
dc.identifier.endpage1216en_US
dc.identifier.issn0954-4089
dc.identifier.issn2041-3009
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1206en_US
dc.identifier.urihttps://doi.org/10.1177/0954408916659310
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4177
dc.identifier.volume231en_US
dc.identifier.wosWOS:000414525000010en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings Of The Institution Of Mechanical Engineers Part E-Journal Of Process Mechanical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGeometric shapeen_US
dc.subjectimage processingen_US
dc.subjectartificial neural networken_US
dc.subjectlearning algorithmsen_US
dc.subjectpredicten_US
dc.titlePredictive modeling of geometric shapes of different objects using image processing and an artificial neural networken_US
dc.typeArticleen_US

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