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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
References
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Details
Primary Language
Turkish
Subjects
Electrical Engineering
Journal Section
Research Article
Authors
Turab Selçuk
*
0000-0002-8445-2105
Türkiye
Publication Date
September 3, 2023
Submission Date
April 18, 2023
Acceptance Date
June 10, 2023
Published in Issue
Year 1970 Volume: 26 Number: 3