The increasing use of artificial intelligence
(AI) in higher education has opened up new possibilities for enhancing student
achievement through data-driven decision making and predictive analytics.
Higher education institutions can identify students who are at risk of academic
failure or dropout and undertake timely interventions to increase retention and
graduation rates by using early prediction of student academic outcomes.
However, many machine learning models employed for student outcome prediction
operate as black-box systems, limiting their transparency and reducing
stakeholders' confidence in their predictions. This study proposes an
explainable machine learning framework for predicting student academic outcomes
in higher education using the Predict Students' Dropout and Academic Success
dataset obtained from the UCI Machine Learning Repository. Five supervised
machine learning algorithms, Logistic Regression, Decision Tree, Random Forest,
XGBoost, CatBoost, and LightGBM were developed and evaluated for predicting
three academic outcome categories: Dropout, Enrolled, and Graduate. The
proposed framework provides an interpretable and effective decision-support
tool that can assist higher education institutions in identifying at-risk
students and implementing evidence-based academic interventions.
Mubashir Haruna, Abubakar Bello Bada, Ede Ifesinachi Chizzy et al.· Journal of Studies in Scienc...· 0 citations
Credit card fraud remains a major challenge for financial institutions, both financially and operationally, as digital transactions continue to grow and fraud datasets remain highly imbalanced. This study compares the performance of several supervised machine learning models for fraud detection, using a unified data preprocessing pipeline. The approach includes removing duplicates, applying RobustScaler normalization, engineering features and using the Synthetic Minority Oversampling Technique (SMOTE) to balance classes before training. Four models were developed and tested Logistic Regression, Decision Tree, Random Forest and Artificial Neural Network (ANN) using the publicly available Kaggle Credit Card Fraud Detection dataset. Their performance was measured with Accuracy, Precision, Recall, F1-score and ROC-AUC metrics. Results showed that thorough preprocessing combined with SMOTE significantly improved the models ability to detect fraudulent transactions. Among them, the Random Forest model delivered the strongest overall performance, proving especially effective at handling highly imbalanced financial data. The comparative analysis also highlighted that ensemble learning methods generally outperform single classifiers in both accuracy and minority-class recognition. These findings emphasize the importance of pairing robust preprocessing strategies with machine learning techniques to boost fraud detection in real-world financial systems. The proposed system offers institutions a scalable and practical solution for building intelligent fraud detection systems, while laying the groundwork for future integration of Explainable AI (XAI) and real-time detection tools.
Nafiu Yahuza, Ahmad Baita Garko, Abubakar Atiku Muslim et al.· Lead Sci Journal of Manageme...· 0 citations
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