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All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review
Oise, Godfrey perfectson
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management 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.  
Co-Authors Agwam Gladys IFEOMA Augustine Osazee AIRHIAVBERE Ejenarhome Otega Prosper Konyeha, Susan