MADENLERİN SINIFLANDIRILMASINA YÖNELİK HİBRİD BİR CNN MODELİN OLUŞTURULMASI

Turab Selçuk

doi:10.17780/ksujes.1285080

EN TR

CREATING A HYBRID CNN MODEL FOR MINES CLASSIFICATION

Abstract

The impact of mines on the economy of countries is quite large. For this reason, the detection and identification of ore deposits in mining is an important research topic. Computer-based decision support systems are also used in ore classification processes. In this study, a hybrid CNN model consisting of four stages was created for the classification of seven different ores. These stages are feature extraction, feature aggregation, feature selection and classification. ResNet50, MobileNetV2 and DenseNet201 architectures were used in feature extraction. By combining the extracted features, a feature vector of 1x3000 dimensions was obtained. In order to increase the classification performance, NCA, ReliefF and mRMR algorithms were applied to the feature vector and features with high distinctiveness were determined. These features are classified by support vector machines. According to the results obtained, it showed an accuracy value of 91.34 for MRMR, 92.42 for NCA and 93.14 for ReliefF. As a result, the proposed hybrid CNN model has higher performance in ore classification than the classical CNN models in the literature. It is thought that the proposed hybrid CNN model will provide decision support to researchers in studies on ore classification in the field of geology

Keywords

MADENLERİN SINIFLANDIRILMASINA YÖNELİK HİBRİD BİR CNN MODELİN OLUŞTURULMASI

Öz

Madenlerin ülkelerin ekonomisindeki yeri oldukça büyüktür. Bu nedenle madencilikte cevher yataklarının tespiti ve tanımlanması önemli bir araştırma konusudur. Cevher sınıflandırılması işlemlerinde de bilgisayar tabanlı karar destek sistemleri kullanılmaktadır Bu çalışmada yedi farklı cevherin sınıflandırılmasına yönelik dört aşamadan oluşan hibrid bir CNN model oluşturulmuştur. Bu aşamalar, özellik çıkarımı, özellik birleştirme, özellik seçimi ve sınıflandırmadır. Özellik çıkarımı için, sınıflandırma problemlerinde yüksek başarım gösteren ResNet50, MobileNetV2 ve DenseNet201 mimarileri kullanılmıştır. Çıkarılan özellikler birleştirilerek 1x3000 boyutlarında kapsamlı özellik vektörü elde edilmiştir. Sınıflandırma başarımını arttırmak için özellik vektörüne NCA, ReliefF ve mRMR algoritmaları uygulanarak ayırt ediciliği yüksek özellikler belirlenmiştir. Bu özellikler destek vektör makineleri ile sınıflandırılmıştır. Elde edilen sonuçlara göre MRMR için 91.34, NCA için 92.42 ve ReliefF için 93,14 doğruluk değeri göstermiştir. Sonuç olarak önerilen hibrid CNN modelinin cevher sınıflandırılmasında literatürdeki klasik CNN modellere göre daha yüksek başarım sağlamıştır. Önerilen hibrid CNN modelin jeoloji alanında cevher sınıflandırılmasına yönelik çalışmalarda araştırmacılara karar desteği sağlayacağı düşünülmektedir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Elektrik Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Turab Selçuk ^*
0000-0002-8445-2105
Türkiye

Yayımlanma Tarihi

3 Eylül 2023

Gönderilme Tarihi

18 Nisan 2023

Kabul Tarihi

10 Haziran 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1285080

IZ

https://izlik.org/JA55DF95RS

Kaynak Göster

RIS / Bibtex

APA

Selçuk, T. (2023). MADENLERİN SINIFLANDIRILMASINA YÖNELİK HİBRİD BİR CNN MODELİN OLUŞTURULMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 685-693. https://doi.org/10.17780/ksujes.1285080