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All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review Scientific Journal of Engineering Research Scientific Journal of Computer Science Methods in Science and Technology Studies
Oise, Godfrey perfectson
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Optimization of E-Waste Sorting Process Using Deep Learning Oise, Godfrey perfectson; Konyeha, Susan
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 2 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i2.503

Abstract

The exponential growth of electronic waste (e-waste) has created urgent environmental and health challenges, demanding advanced solutions for efficient sorting and recycling. This study presents a novel hybrid deep learning framework that integrates EfficientNet, MobileNet, and a Sequential Neural Network (SNN) to automate e-waste classification with high accuracy and speed. The model was trained and evaluated on a diverse dataset of 3,859 images spanning 12 e-waste categories, including batteries, printed circuit boards, and household electronics. Experimental results demonstrate exceptional performance, achieving 97.8% accuracy, 98.1% precision, 97.8% recall, and a 97.8% F1 score, surpassing traditional methods and single-model approaches. The system’s lightweight design (48 MB) enables real-time processing (0.12 seconds per image) on standard CPUs, ensuring scalability for industrial applications. By automating the sorting process, the framework reduces human exposure to hazardous materials, enhances material recovery efficiency, and supports sustainable waste management practices. Its modular architecture allows seamless integration into existing recycling workflows, making it a practical solution for facilities with limited resources. The study underscores the model’s potential to advance circular economy initiatives by improving resource reuse and minimizing environmental contamination. Future research will focus on real-time IoT deployment, federated learning for decentralized training, and expanding classification capabilities to include rare and unconventional e-waste items. This work contributes a scalable, cost-effective, and environmentally responsible solution to the global e-waste crisis.
Student Success Prediction in Digital Learning Environments Oise, Godfrey perfectson; Ejenarhome Otega PROSPER; Augustine Osazee AIRHIAVBERE; Agwam Gladys IFEOMA
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 6 (2025): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v4i6.592

Abstract

Acknowledging the risk of perpetuating bias in AI-driven student success prediction, this study introduces a fairness-conscious machine learning framework that aims to balance predictive accuracy with ethical responsibility in digital learning settings. Using a dataset of 5,000 anonymized student records, three models, Random Forest (RF), Gradient Boosting (GBM), and Support Vector Machine (SVM), were developed to forecast academic outcomes. Model evaluation combined standard metrics (accuracy, precision, recall, and F1-score) with fairness measures such as demographic parity, equal opportunity, and disparate impact ratio to explore trade-offs between accuracy and fairness. Results indicated that while RF and GBM had slightly higher accuracy, SVM demonstrated more consistent fairness across demographic groups, emphasizing its stronger balance between predictive power and equity. A fairness-centered optimization method was applied to embed fairness constraints directly into model training, showing that both accuracy and fairness can be improved simultaneously rather than being in opposition. The framework integrates fairness throughout data preprocessing, model development, and post-prediction review, promoting transparent and responsible decision-making. By aligning with international ethical AI standards from UNESCO and the OECD, this research provides a practical pathway for creating educational prediction systems that enhance inclusion, minimize bias, and build trust in digital learning environments.  
DistilBERT-Based Detection of AI-Generated Text in Online Assessments: Ethical and Pedagogical Implications EJENARHOME, Prosper Otega; Oise, Godfrey Perfectson; AIRHIAVBERE, Augustine Osazee; Odimayomi, Joy Akpowehbve
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 9 (2026): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v4i9.651

Abstract

The rapid shift toward online and distance learning has positioned digital assessment as a cornerstone of higher education while also challenging academic integrity due to the accessibility of generative artificial intelligence (GenAI). While technical research into AI detection is expanding, there remains a critical gap in understanding how detection outcomes can be ethically and pedagogically integrated into digital learning environments. This study evaluates a fine-tuned DistilBERT-based model for detecting AI-generated text, situating its technical performance within a learning-centered framework. Using a large-scale dataset of over 28,000 human-written and AI-generated essays, the model demonstrated exceptional robustness, achieving an overall accuracy of 99%, an AUC of 0.9999, and balanced F1 scores of 0.99. Beyond technical metrics, this research redefines AI detection by shifting the narrative from a punitive, surveillance-oriented mechanism to a supportive learning analytics tool. By interpreting detection results alongside instructional indicators, the study demonstrates how these technologies can inform assessment redesign, enhance transparency, and foster learner trust. The findings contribute to the field of digital education by providing a roadmap for the responsible integration of AI detection into assessment ecosystems, ensuring that technological precision serves the broader goals of fairness and pedagogical innovation.
A Convolutional Neural Network Framework for Intelligent Intrusion Detection Oise, Godfrey Perfectson; Olanrewaju, Babatunde Seyi; Orukpe, Oshoiribhor Austin; Pius, Kevin Chinedu; Airhiavbere, Augustine Osazee
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.404

Abstract

The rapid expansion of cloud computing, Internet of Things (IoT), and distributed network environments has significantly increased vulnerability to sophisticated cyber threats, exposing the limitations of traditional signature-based intrusion detection systems. Although deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising performance in intrusion detection, challenges related to validation transparency, statistical reliability, and interpretability remain inadequately addressed. This study proposes an intelligent CNN-based intrusion detection framework designed to improve detection accuracy, robustness, and model explainability. The framework is evaluated using the UNSW-NB15 benchmark dataset, which reflects realistic modern cyber-attack scenarios. A comprehensive preprocessing pipeline involving data cleaning, categorical encoding, feature normalization, and data reshaping is applied to enhance learning efficiency. To ensure unbiased evaluation, stratified k-fold cross-validation and an independent held-out test set are employed. Experimental results demonstrate that the proposed CNN achieves a test accuracy of 91.8%, with balanced precision, recall, and F1-score across benign and malicious traffic classes. Multi-class detection analysis further confirms the model’s capability to distinguish among diverse attack categories. Statistical validation using mean performance metrics, standard deviation, and confidence intervals demonstrates stable generalization performance. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to enhance interpretability by identifying network-level features that influence classification decisions. An ablation study further validates the effectiveness of key architectural components. The results indicate that the proposed framework provides a reliable, scalable, and interpretable solution for intelligent intrusion detection in modern high-dimensional network environments.
Interpretable Academic Outcome Prediction Using Explainable Boosting Machines Oise, Godfrey Perfectson; Uloko, Felix Oshiorenoya; Pius, Kevin Chinedu; Eferoba–Idio, Enovwo; Edobor, Michael Uyiosa; Mintah, Evans; Ukpebor, Osahon; Sokoya, Oludare; Jessa, Tejiri
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

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 Oise, Godfrey Perfectson; Konyeha, Susan; Uloko, Felix Oshiorenoya; Pius, Kevin Chinedu; Eferoba–Idio, Enovwo; Edobor, Michael Uyiosa; Mintah, Evans; Ukpebor, Osahon; Sokoya, Oludare; Jessa, Tejiri
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
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 Agwam Gladys IFEOMA AIRHIAVBERE, Augustine Osazee Augustine Osazee AIRHIAVBERE Edobor, Michael Uyiosa Eferoba–Idio, Enovwo Ejenarhome Otega Prosper EJENARHOME, Prosper Otega Jessa, Tejiri Konyeha, Susan Mintah, Evans Odimayomi, Joy Akpowehbve Olanrewaju, Babatunde Seyi Orukpe, Oshoiribhor Austin Pius, Kevin Chinedu Sokoya, Oludare Ukpebor, Osahon Uloko, Felix Oshiorenoya