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    Öğe
    Enhanced ore classification through optimized CNN ensembles and feature fusion
    (Springer International Publishing, 2025) Yurdakul, Mustafa; Uyar, Kübra; Taşdemir, Şakir
    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 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.
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    Öğe
    Smart Classroom Attendance and Management System with Deep Learning
    (2024) Ardıç, İbrahim Tuğrul; Yıldızhan, Batuhan; Uyar, Kübra
    The evolution of traditional educational methods highlights the necessity to adapt to new technologies. This study aims to facilitate the attendance-taking processes in the education sector through automation. Addressing challenges such as time loss, accuracy issues, and the fragmentation of class periods associated with paper-based attendance methods, we introduce the Smart Classroom Attendance and Management System. Our study utilizes facial recognition technology to scan the facial features of each student, providing a unique biometric identification and automatically enrolling students in the class. This allows students to be automatically recorded upon entering the classroom, eliminating the need for paper-based attendance. Additionally, it is supported by a mobile application, we provide two different panels for teachers and students, minimizing human errors. Students can view and verify attendance information through the application at the end of the class, while teachers can approve the recorded attendance, thereby enhancing the reliability of the system. In conclusion, the Smart Classroom Attendance and Management System offers an innovative approach to overcome the challenges posed by traditional methods and make educational processes more efficient. Representing the transformation of automation in the education sector, this study aims to contribute to a more effective learning experience for both students and teachers.