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Felix Oshiorenoya Uloko

Veritas University

Author-ID : 9878788

Engineering

Published : 3 Documents Claim Missing Document

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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 Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

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

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 Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

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

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.

Energy-Efficient Federated Learning with Temporal Convolutional Networks for Intrusion Detection Godfrey Perfectson Oise; Felix Oshiorenoya Uloko; Kevin Chinedu Pius; Roli Lydia Oshasha; Eric Edeigue Osemwegie; Immunhierokene Clinton Obrorindo
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.462

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface of modern network infrastructures, necessitating intelligent and scalable intrusion detection systems. Federated Learning (FL) has emerged as a promising paradigm for distributed model training without centralized data sharing; however, challenges such as energy efficiency, data heterogeneity, and privacy preservation remain inadequately addressed. Existing studies often emphasize optimization objectives theoretically without validating them under realistic constraints. This paper proposes an energy-aware federated learning framework integrating Temporal Convolutional Networks (TCNs) for intrusion detection using distributed network traffic data. The framework incorporates differential privacy for secure model updates and a conceptual energy-aware client participation strategy. Experiments are conducted on the UNSW-NB15 dataset under a controlled setting with fixed client participation and communication parameters. The results demonstrate that the proposed model achieves improved classification accuracy and stable convergence behavior across communication rounds while operating under a fixed energy budget. However, energy consumption remains constant due to controlled experimental conditions, indicating that the study evaluates performance under energy constraints rather than dynamic energy optimization. The findings highlight the effectiveness of TCN-based federated models for intrusion detection in resource-constrained environments. Future work will focus on dynamic energy modeling, heterogeneous client environments, and comprehensive multi-objective evaluation.

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