Predicting secondary school students' academic accomplishments is crucial for early intervention and personalized learning strategies. This study develops a machine learning-based system to forecast student performance, including grade and percentage prediction, while analyzing the impact of various socio-economic, educational, personal, and technological factors. The dataset was collected through a structured survey, incorporating aspects such as family income, parental education, access to private tutoring, school infrastructure, learning environment, mental health, career guidance, geographic constraints, government policies, and digital literacy. Pre-processing was done using five machine learning algorithms: Random Forest, Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gradient BoostingTo assess model performance, various evaluation metrics, such as accuracy and root mean squared error, were utilized. The findings suggest that machine learning methods are capable of accurately forecasting student performance, with the Random Forest algorithm demonstrating the greatest level of precision. This study lays the groundwork for AI-based educational resources aimed at recognizing students who are at risk and facilitating focused interventions.
Shravani P.Pawar, S. Deshmukh, Priya Chandran· Enterprise Development and M...· 0 citations
Abstract Although SDN provides a programmable, centrally managed framework for modern networks, that same centralization leaves it exposed to attacks such as Distributed Denial of Service (DDoS). This paper proposes an intrusion detection framework that couples Z-Isomorphic Sigmoid Graph Neural Networks (ZIS-GNN) with Bonobo-Optimization-based (EKPC-BOA) feature selection. The sigmoid-based activation strengthens the graph representation relative to conventional GNNs, capturing complex traffic patterns more faithfully, while the hybrid selector – combining the Bonobo Optimization Algorithm with an entropy score and Pearson correlation – distils the most informative features from the traffic data and thereby improves both efficiency and accuracy. Experiments demonstrate that the proposed ZIS-GNN+EKPC-BOA model attains an accuracy of 97.36%, a precision of 97.37%, and an F1-score of 97.58%, outperforming baseline models such as DNN (89.60%), LSTM (91.68%), BiLSTM (93.77%), and GNN (95.86%), as well as the standard graph baselines GCN (96.18%) and the attention-based GAT (96.58%). The results show the effectiveness of combining graph-based learning with hybrid feature selection for intrusion detection in SDN.
Zahir Mulani, Suhasini Vijaykumar, Priya Chandran· Journal of Information Assur...· 0 citations
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