Research ArticleOpen accessEvery word in the titleSecuring IoT Networks: Multi-Attack Detection of RPL Routing Threats Using Deep LearningAyoub Krari, Abdelmajid Hajami, Ayoub Toubi, Marouane Ait SaidThe growing frequency of cyber threats in Internet of Things (IoT) networks, including attacks on RPL routing, requires the creation of strong detection systems to safeguard network integrity and provide dependable communication. This study is driven by the pressing necessity to tackle the security weaknesses in IoT networks, where threats such as black holes, version number alteration, DIS flooding, and … …Journal of Computer Science, Vol. 21, Issue 4 · 6 Mar 2025 · 5,397 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDifferSqueezeNet: A Leaner Defect Detection ModelJose Joao Manrique Franco, Marcelo RudekIn the industrial environment, the inherent limitations of manual inspection have caused the ascent of automated computer vision systems that aim to match or surpass human performance. In this context, the artificial intelligence field has been interested in defect detection with the creation of many machine learning techniques, focusing on Unsupervised and Semi-supervised learning methods. Since most of these methods … …Journal of Computer Science, Vol. 21, Issue 3 · 4 Feb 2025 · 5,727 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBenchmarking Lightweight Deep Learning Models for Field Based Sorghum Leaf Disease DetectionPrince Daughin Ngqabutho NcubeSorghum is a climate-resilient cereal crop central to food and livelihood security across Southern Africa, yet its productivity is frequently reduced by foliar diseases that smallholder farmers struggle to diagnose reliably in the field. While deep learning has demonstrated strong potential for automated plant disease identification, sorghum remains underrepresented in the literature, and no prior study has provided a comprehensive, … …Journal of Computer Science, Vol. 22, Issue 10 · 29 Sep 2026 · 535 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleLightweight Deep Learning Model for TFT-LCD Pixel Defect Detection Using MobileNetV2J. Sheikshabjan, K. Dhanakodi, A.T. Rajamanickam et al.The proposed methodology aims to achieve high classification accuracy while maintaining computational efficiency and minimizing inference latency, making it highly suitable for real-time industrial inspection scenarios. This study evaluated five traditional transfer learning models (VGG16, ResNet50, MobileNetV2, Xception, and InceptionV3) for comparison in pixel defect detection. MobileNetV2-based implementation demonstrated a competitive average test accuracy of 0.9916, outperforming several baselines while … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 673 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOptimization of Non-Technical Electricity Loss Detection Using Artificial Neural Networks: Case of Cameroon Distribution NetworkLekini Nkodo Claude Bernard, Bell Serge Samuel, Nyemb Nsoga Valjacques et al.Non-Technical Losses (NTL) represent a critical challenge for power utilities, particularly in developing countries. This paper proposes an unsupervised deep learning approach based on an autoencoder to detect abnormal electricity consumption patterns in the Cameroonian distribution network. The model learns normal customer behavior using historical consumption data and identifies anomalies through reconstruction error analysis. Statistical thresholds are applied to classify … …American Journal of Engineering and Applied Sciences, Vol. 19, Issue 1 · 23 Jul 2026 · 1,225 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleIoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and DiagnosisYamini Kalva, R. Ganesh Babu, Sindhu V. et al.Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome … …Journal of Computer Science, Vol. 22, Issue 4 · 30 Mar 2026 · 1,753 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleIoT Anomaly Detection Using Picture Fuzzy Clustering ApproachFehmin Nadira Laskar, Vijo Arul Selvi M., Fokrul Alom Mazarbhuiya et al.Improving the effectiveness of security systems without slowing them down is a major challenge in cybersecurity. Many methods have been explored for detecting anomalous behaviour in network data, with fuzzy set-based approaches standing out for their potential. The Internet of Things (IoT) consists of many connected devices that constantly generate large amounts of data and perform tasks in real time. … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,515 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Distillation GAN Model for Network Intrusion DetectionChandrakala C B, Sai Sreevall, Raghudathesh G PThis study presents a distillation-based Generative Adversarial Network (GAN) model designed for intrusion detection in network systems. A Teacher-Student architecture is proposed, in which a single teacher generator is utilized to train both the teacher discriminator and the student discriminator. The model was trained to identify anomalies present in network traffic intrusions by tuning the min-max loss function in GANs. … …Journal of Computer Science, Vol. 22, Issue 2 · 5 Mar 2026 · 1,549 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCheating Detection in Examinations Using Improved YOLOv8 with Attention MechanismYan Zuo, Soo See Chai, Kok Luong GohExaminations are among the most widely used and effective methods for assessing knowledge mastery, both domestically and internationally, and are extensively used in various talent-selection processes. Currently, offline exam venues usually rely on on-site manual invigilation combined with exam-monitoring videos to further strengthen invigilation efforts. However, this invigilation method not only utilizes large amounts of human and material costs but … …Journal of Computer Science, Vol. 20, Issue 12 · 6 Nov 2024 · 6,253 accessesView PDFCite