An effective Turkey marble classification system: Convolutional neural network with genetic algorithm -wavelet kernel - Extreme learning machine

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dc.authorid0000-0002-8611-701Xen_US
dc.contributor.authorAvcı, Derya
dc.contributor.authorSert, Eser
dc.date.accessioned2021-10-15T11:21:34Z
dc.date.available2021-10-15T11:21:34Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMarble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.en_US
dc.identifier.citationAvci, D., Sert, E. (2021). An effective turkey marble classification system: Convolutional neural network with genetic algorithm -wavelet kernel - extreme learning machine. Traitement du Signal, Vol. 38, No. 4, pp. 1229-1235. https://doi.org/10.18280/ts.380434en_US
dc.identifier.doi10.18280/ts.380434
dc.identifier.endpage1235en_US
dc.identifier.issn0765-0019en_US
dc.identifier.issn1958-5608en_US
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85116676081en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1229en_US
dc.identifier.urihttps://doi.org/10.18280/ts.380434
dc.identifier.urihttps://hdl.handle.net/20.500.12899/450
dc.identifier.volume38en_US
dc.identifier.wosWOS:000703007300034en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSert, Eser
dc.language.isoenen_US
dc.publisherIIETA International Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectGenetic algorithmen_US
dc.subjectWavelet kernel-extreme learning machineen_US
dc.subjectMarble classificationen_US
dc.titleAn effective Turkey marble classification system: Convolutional neural network with genetic algorithm -wavelet kernel - Extreme learning machineen_US
dc.typeArticleen_US

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