https://ijcset.co.in/index.php/ijcset/issue/feed 2026-07-24T09:24:36+00:00 Manging Editor editor@ijcset.co.in Open Journal Systems <p><strong><span class="style6">International Journal of Computer Science Engineering and Technology</span> (IJCSET-Peer Reviewed)</strong> will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology.</p> <p>The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects.</p> <p>The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.</p> <p>All submissions must describe original research, not published or currently under review for another conference or journal.</p> <p>is a leading international journals for publication of new ideas, the state of the art research results and fundamental advances in all aspects of computer science and engineering. IJCSET is a scholarly open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications related to Computer Science and Engineering</p> <p> </p> https://ijcset.co.in/index.php/ijcset/article/view/243 2026-07-24T09:24:36+00:00 Dr. B. Selvanandhini editor@ijcset.co.in

<p><strong>Students </strong>Performance prediction using data mining techniques has become an essential approach for analyzing large-scale datasets and extracting meaningful insights for decision-making. This study presents a Student Performance Prediction using Data Mining Techniques (SPPDMT) model that integrates feature selection and classification algorithms to improve prediction accuracy. The proposed system utilizes Dynamic Feature Ensemble Evolution (DE-FS) to identify the most relevant features by combining multiple statistical methods such as correlation analysis, information gain, and chi-square techniques. These selected features are then used to train classification models such as Decision Tree (DT) and Support Vector Machine (SVM). The hybrid SPPDMT model (DE-FS) enhances prediction performance by reducing dimensionality and improving model generalization. Experimental results demonstrate that the proposed method outperforms traditional models in terms of accuracy, precision, recall, and F1-score. The system provides efficient prediction results and supports better decision-making in performance evaluation systems.</p>

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