An effective Turkey marble classification system: Convolutional neural network with genetic algorithm -wavelet kernel - Extreme learning machine
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IIETA International Information and Engineering Technology Association
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Marble 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.
Açıklama
Anahtar Kelimeler
CNN, Genetic algorithm, Wavelet kernel-extreme learning machine, Marble classification
Kaynak
Traitement du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
38
Sayı
4
Künye
Avci, 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.380434
