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All Journal Scientific Journal of Engineering Research Methods in Science and Technology Studies
Oludare Sokoya
National University
Engineering

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A Hybrid Machine Learning–Optimization Framework for Energy Demand Forecasting and Decision Support in Smart Infrastructure Godfrey Perfectson Oise; Tejiri Jessa; Evans Mintah; Felix Oshiorenoya Uloko; Oludare Sokoya; Osahon Ukpebor
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.440

Abstract

This study addresses the growing need for accurate and actionable energy demand forecasting in smart infrastructure systems, where data-driven decision-making is essential for efficiency, sustainability, and system reliability. Despite advances in machine learning-based forecasting, most approaches remain prediction-centric and are rarely integrated with operational optimization and decision-support mechanisms, limiting their real-world applicability. To address this gap, this study proposes a sequentially integrated hybrid machine learning–optimization framework that combines ensemble-based forecasting, optimization-driven energy allocation, and explainable artificial intelligence (XAI) within a unified architecture. The term hybrid denotes the integration of heterogeneous methodological components, while the framework is implemented as a pipeline in which forecasting outputs inform downstream optimization. The predictive module incorporates XGBoost and Long Short-Term Memory (LSTM) models, alongside an ensemble approach that operates within the forecasting stage to enhance robustness and generalization. The optimization component utilizes forecasted demand to minimize energy cost under demand and capacity constraints, while SHAP-based analysis improves interpretability and transparency. Empirical evaluation using the UCI Building Energy Efficiency dataset shows that XGBoost achieves the highest predictive accuracy (MAE = 0.429, RMSE = 0.613, R² = 0.996), while the ensemble model provides strong robustness (R² = 0.994). The integrated framework effectively smooths demand fluctuations, improves allocation efficiency, and identifies relative compactness and glazing area as dominant features. The results demonstrate that sequential integration of forecasting, optimization, and interpretability enhances predictive reliability, operational efficiency, and decision transparency.
Interpretable Academic Outcome Prediction Using Explainable Boosting Machines Godfrey Perfectson Oise; Felix Oshiorenoya Uloko; Kevin Chinedu Pius; Enovwo Eferoba–Idio; Michael Uyiosa Edobor; Evans Mintah; Osahon Ukpebor; Oludare Sokoya; Tejiri Jessa
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.441

Abstract

Predictive analytics has become an important component of learning analytics in higher education, enabling institutions to identify academic risks and support student success through data-driven decision making. However, many existing academic outcome prediction models rely on complex black-box machine learning techniques that provide high predictive performance but limited transparency and interpretability. The lack of explainability restricts the practical adoption of such models in educational environments where accountability, trust, and ethical decision-making are essential. This study proposes an interpretable machine learning framework for multi-class academic outcome prediction using the Explainable Boosting Machine (EBM), a glass-box model that combines the predictive power of ensemble boosting with the transparency of generalized additive models. The proposed framework was evaluated using a publicly available Student Performance and Learning Behavior dataset consisting of 6,519 student records containing academic, behavioral, and demographic attributes. Academic outcomes were formulated as a four-class classification task: Distinction, Pass, Fail, and Withdrawn. Model performance was assessed using multiple evaluation metrics including accuracy, precision, recall, F1-score, and ROC–AUC analysis. Experimental results demonstrate that the proposed EBM model achieves an accuracy of 88% and an overall ROC–AUC score of 0.91, indicating strong predictive capability across outcome categories. Furthermore, the model provides native interpretability through feature contribution functions and SHAP-based explanations, enabling both global and instance-level understanding of predictions. The results demonstrate that reliable academic outcome prediction can be achieved without sacrificing interpretability, providing a transparent and trustworthy decision-support framework for educational analytics.
Isolation Forest–Based Intrusion Detection for Cyber-Physical Systems Godfrey Perfectson Oise; Susan Konyeha; Felix Oshiorenoya Uloko; Kevin Chinedu Pius; Enovwo Eferoba–Idio; Michael Uyiosa Edobor; Evans Mintah; Osahon Ukpebor; Oludare Sokoya; Tejiri Jessa
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.434

Abstract

Cyber-physical engineering systems (CPES) form the backbone of critical infrastructures such as power generation, industrial automation, and water treatment facilities. Because cyber intrusions in these environments can directly disrupt physical processes, reliable intrusion detection mechanisms are essential for maintaining operational safety and system resilience. However, many existing intrusion detection approaches rely on supervised learning techniques that require large volumes of labeled attack data, which are rarely available in real industrial environments. In addition, advanced detection methods often introduce significant computational overhead, limiting their practicality for deployment in resource-constrained cyber-physical systems. To address these challenges, this study proposes a one-class anomaly detection framework based on the Isolation Forest algorithm for monitoring cyber-physical engineering systems. The proposed approach learns the statistical distribution of normal operational behavior using multivariate sensor, actuator, and control signals, and identifies deviations from this learned pattern as potential cyber intrusions. The framework is evaluated using the Hardware-in-the-Loop–based Augmented Industrial Control System (HAI) Security Dataset, which provides realistic industrial process measurements under both normal and attack scenarios. Experimental results show that the model achieves overall accuracy (0.89) and strong performance in identifying normal operational states (F1-score = 0.94). However, attack detection shows moderate recall (0.48) but low precision (0.04) due to class imbalance and overlapping anomaly score distributions. These findings indicate that Isolation Forest serves as a computationally efficient baseline anomaly detection mechanism for real-time CPS monitoring, while highlighting the need for hybrid and temporally aware detection strategies to improve attack discrimination in industrial cyber-physical environments.
Co-Authors Enovwo Eferoba–Idio Evans Mintah Felix Oshiorenoya Uloko Godfrey Perfectson Oise Kevin Chinedu Pius Michael Uyiosa Edobor Osahon Ukpebor Susan Konyeha Tejiri Jessa