Enhanced ore classification through optimized CNN ensembles and feature fusion
Yazarlar (3)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Dr. Öğr. Üyesi Kübra Uyar Alanya Alaaddin Keykubat University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Iran Journal of Computer Science | ||
| Dergi ISSN | 2520-8438 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Kabul Tarihi | 03-01-2025 | Yayınlanma Tarihi | 29-01-2025 |
| Cilt / Sayı / Sayfa | 8 / 2 / 491–509 | DOI | 10.1007/s42044-025-00230-2 |
| Makale Linki | https://doi.org/10.1007/s42044-025-00230-2 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Ore is a type of natural stone that contains economically valuable minerals or metals. Accurate classification of ore minerals is crucial for improving operational efficiency in mining, reducing environmental impacts, and determining market value. Traditional methods for classifying ores are often time-consuming, labor-intensive, and error-prone. Therefore, computer-aided systems offer a significant advantage in this field. In this study, various efficient Deep Learning (DL) approaches are utilized for the detection of ore types. Within the scope of the study, four different experiments (transfer learning, feature extraction and classification with SVM, feature selection with optimization algorithms, and ensemble methods) are conducted, and the methods are compared in terms of classification metrics. As a result of the experimental case studies, high accuracy rates between 95 and 98% are achieved. The most successful … |
| Anahtar Kelimeler |
| CNN | Ensemble learning | Feature fusion | Feature selection | Optimization | Ore classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 8 |
| Google Scholar | 12 |