Enhanced ore classification through optimized CNN ensembles and feature fusion

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dc.contributor.authorYurdakul, Mustafa
dc.contributor.authorUyar, Kübra
dc.contributor.authorTaşdemir, Şakir
dc.date.accessioned2026-01-24T12:20:51Z
dc.date.available2026-01-24T12:20:51Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractOre 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 method is the ensemble method, weighted by grid search. The ensemble model, which combined AlexNet, VGG16, and Xception models, achieves remarkable results with an overall accuracy of 98.11%, precision of 98.18%, recall of 98.11%, and f1-score of 98.11% on the publicly available Ore Images Dataset (OID). This study demonstrates that efficient DL approaches can classify ores with very high accuracy and have significant potential applications in the mining industry. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
dc.identifier.doi10.1007/s42044-025-00230-2
dc.identifier.endpage509
dc.identifier.issn2520-8438
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85217252596
dc.identifier.scopusqualityQ2
dc.identifier.startpage491
dc.identifier.urihttps://doi.org/10.1007/s42044-025-00230-2
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4642
dc.identifier.volume8
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing
dc.relation.ispartofIran Journal of Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260121
dc.subjectCNN
dc.subjectEnsemble learning
dc.subjectFeature fusion
dc.subjectFeature selection
dc.subjectOptimization
dc.subjectOre classification
dc.titleEnhanced ore classification through optimized CNN ensembles and feature fusion
dc.typeArticle

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