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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
Konyeha, Susan
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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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The Integration of Internet of Things (IoT) in Smart Classrooms: Opportunities, Challenges, and Future Trajectories Oise, Godfrey; Cyprian C. KONYEHA; COMFORT, Olayinka Tosin; Konyeha, Susan; Emmanueld, Chukwuma Ozobialu
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 3 (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.v4i3.537

Abstract

The integration of the Internet of Things (IoT) into educational environments signifies a transformative shift towards smart classrooms, enabling real-time data-driven instruction, environmental optimization, and personalized learning experiences. This study explores the opportunities, challenges, and future directions of IoT deployment in academic settings through a mixed-methods approach that combines quantitative analysis, qualitative interviews, and IoT-edge data assessment. Survey responses from 150 educators and interviews with 20 key stakeholders revealed significant adoption rates and pedagogical benefits, including enhanced engagement and individualized feedback. However, critical challenges such as data privacy, cybersecurity risks, limited teacher training, and infrastructure disparities hinder widespread implementation. A machine learning framework utilizing Random Forest classification was applied to a custom IoT-edge dataset, uncovering correlations between environmental variables and student behavior. High temperatures negatively affected classroom occupancy, while increased light intensity correlated with heightened engagement. Model evaluation yielded strong performance metrics, including an accuracy of 95% and an AUC of 0.99, highlighting the predictive power of features like learning outcomes and engagement scores. The findings emphasize the dual importance of technical readiness and pedagogical adaptation, advocating for policy support, ethical data governance, and teacher capacity-building to fully realize IoT’s potential in shaping adaptive, equitable, and intelligent learning ecosystems.
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.
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 COMFORT, Olayinka Tosin Cyprian C. KONYEHA Edobor, Michael Uyiosa Eferoba–Idio, Enovwo Emmanueld, Chukwuma Ozobialu Jessa, Tejiri Mintah, Evans Oise, Godfrey Oise, Godfrey perfectson Pius, Kevin Chinedu Sokoya, Oludare Ukpebor, Osahon Uloko, Felix Oshiorenoya