Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IT JOURNAL RESEARCH AND DEVELOPMENT The Indonesian Journal of Computer Science
Kgote, Otshepeng
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Empirical Analysis of Deep Learning Models for Real-time Face Detection on Resource-constrained Devices Isong, Bassey; Ndouvhada, Sedzani; Kgote, Otshepeng
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.22402

Abstract

Face detection (FD) technology enables machines to identify human faces, playing a critical role in mobile device security and user interaction. However, achieving an optimal balance between speed and accuracy in FD algorithms remains a challenge, particularly for real-time applications on resource-limited devices. Factors such as variations in pose, lighting conditions, occlusions, dataset diversity, and hardware constraints often hinder effective deployment. This study presents a comprehensive empirical evaluation of deep learning-based object detection techniques, specifically YOLOv8, SSD, and Faster RCNN, to assess their effectiveness in addressing real-world scalability and performance demands. These models were trained on diverse datasets and evaluated using key performance metrics, including accuracy, precision, recall, and frames per second (FPS). YOLOv8 achieved superior performance, achieving 42.32 FPS with an accuracy of 86%, surpassing two-stage models in real-time processing speed while maintaining comparable accuracy. The findings underscore the importance of dataset quality and diversity in enhancing model performance and positioning YOLOv8 as an effective solution for balancing speed and accuracy on the COCO dataset. The study envisions a future exploration of hybrid models that integrate YOLOv8's efficiency with Faster RCNN's precision to develop more robust FD solutions tailored to real-world challenges.
Resource-Efficient Hybrid Ensemble ML Framework for Anomaly Detection in IoT Smart Homes Kgote, Otshepeng; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5122

Abstract

The Internet of Things (IoT) technologies are used to support smart home systems through device and sensor connectivity for data exchange. However, the growth in adoption increases exposure to cyber-attacks and device faults, which puts system reliability and user safety at risk. This study proposes a framework that uses a pre-trained hybrid ensemble model to detect and separate attacks and faults while supporting timely mitigation. Firstly, the study evaluates models on the CICIoT2023 and IntelLab fault-injected datasets using ensemble learning methods and traditional supervised classifiers. Extreme Gradient Boosting shows the strongest intrusion detection performance. Random Forest shows the strongest fault detection performance. Secondly, both models were fine-tuned and combined within a hybrid meta-model. The results show high accuracy, strong F1 scores, and low false positive rates. The framework was implemented as a web application using Flask and Streamlit to support real-time simulations of attack, fault, and normal events. Evaluation reports latency under 5 seconds and memory use under 400 KB, which supports deployment on resource constrained IoT devices. It was optimized using quantisation and compression. The paper proposes a hybrid ensemble approach for joint fault and intrusion detection, a deployable prototype for constrained environments, and methods to enhance model performance.
Co-Authors Isong, Bassey Ndouvhada, Sedzani