Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications
The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V...
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MDPI AG
2025-02-01
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| author | Paraskevas Tsangaratos Ioanna Ilia Nikolaos Spanoudakis Georgios Karageorgiou Maria Perraki |
| author_facet | Paraskevas Tsangaratos Ioanna Ilia Nikolaos Spanoudakis Georgios Karageorgiou Maria Perraki |
| author_sort | Paraskevas Tsangaratos |
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| description | The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V2—designed to identify multiple minerals within a single frame and output probabilities for various mineral types, including Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite. Among these, MobileNet V2 demonstrated exceptional performance, achieving the highest accuracy (98.98%) and the lowest loss (0.0202), while Xception and VGG-16 also performed competitively, excelling in feature extraction and detailed analyses, respectively. Gradient-weighted Class Activation Mapping visualizations illustrated the models’ ability to capture distinctive mineral features, enhancing interpretability. Furthermore, a stacking ensemble approach achieved an impressive accuracy of 99.71%, effectively leveraging the complementary strengths of individual models. Despite its robust performance, the ensemble method poses computational challenges, particularly for real-time applications on resource-constrained devices. The application of this methodology in Mineral Quest, an educational Python-based game, underscores its practical potential in geology education, mining, and geological surveys, offering an engaging and accurate tool for real-time mineral classification. |
| format | Article |
| id | doaj-art-e45c585c05104f8fa158afb99a8d5f32 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e45c585c05104f8fa158afb99a8d5f322025-08-20T03:12:04ZengMDPI AGApplied Sciences2076-34172025-02-01154187110.3390/app15041871Machine Learning Approaches for Real-Time Mineral Classification and Educational ApplicationsParaskevas Tsangaratos0Ioanna Ilia1Nikolaos Spanoudakis2Georgios Karageorgiou3Maria Perraki4Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15773 Zografou, GreeceDepartment of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15773 Zografou, GreeceSchool of Production Engineering and Management, Technical University of Crete (TUC), University Campus, Akrotiri, 73100 Chania, GreeceSchool of Electrical and Computer Engineering, Technical University of Crete (TUC), University Campus, Akrotiri, 73100 Chania, GreeceDepartment of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15773 Zografou, GreeceThe main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V2—designed to identify multiple minerals within a single frame and output probabilities for various mineral types, including Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite. Among these, MobileNet V2 demonstrated exceptional performance, achieving the highest accuracy (98.98%) and the lowest loss (0.0202), while Xception and VGG-16 also performed competitively, excelling in feature extraction and detailed analyses, respectively. Gradient-weighted Class Activation Mapping visualizations illustrated the models’ ability to capture distinctive mineral features, enhancing interpretability. Furthermore, a stacking ensemble approach achieved an impressive accuracy of 99.71%, effectively leveraging the complementary strengths of individual models. Despite its robust performance, the ensemble method poses computational challenges, particularly for real-time applications on resource-constrained devices. The application of this methodology in Mineral Quest, an educational Python-based game, underscores its practical potential in geology education, mining, and geological surveys, offering an engaging and accurate tool for real-time mineral classification.https://www.mdpi.com/2076-3417/15/4/1871mineral classificationpre-trained CNN-based modelsGrad-CAM heatmapsquiz game |
| spellingShingle | Paraskevas Tsangaratos Ioanna Ilia Nikolaos Spanoudakis Georgios Karageorgiou Maria Perraki Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications Applied Sciences mineral classification pre-trained CNN-based models Grad-CAM heatmaps quiz game |
| title | Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications |
| title_full | Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications |
| title_fullStr | Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications |
| title_full_unstemmed | Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications |
| title_short | Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications |
| title_sort | machine learning approaches for real time mineral classification and educational applications |
| topic | mineral classification pre-trained CNN-based models Grad-CAM heatmaps quiz game |
| url | https://www.mdpi.com/2076-3417/15/4/1871 |
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