Research ArticleOpen accessEvery word in the titleDDoS Attack Detection Using Enhanced Neural Network Algorithm in Software-Defined NetworksHema Surendrakumar Dhadhal, Paresh P KotakWith the uncommon advancement of technology and networking, the world today is totally relying on it. The most obvious drawback of this technical progress resulted in several possible dangers. A Distributed Denial of Service (DDoS) attack is one in which a large number of compromised systems work together to prevent service from being provided to the targeted system. Consequently, to … …Journal of Computer Science, Vol. 19, Issue 6 · 24 May 2023 · 5,727 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSmart Ambulances for IoT Based Accident Detection, Tracking and ResponseAmreen Ayesha, Komalavalli ChakravarthiOne of the main reasons for death among young people in the current era is based on road accidents. The goal of current research on accident detection systems is to shorten reporting times, increase accident detection accuracy and address them through our modern technology. Our primary goal is to recommend a system that can successfully aid in preventing any kind … …Journal of Computer Science, Vol. 19, Issue 6 · 12 May 2023 · 6,333 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF DocumentsJosue Nguinabe, Franklin Tchakounte et al.Fake Portable Document Format (PDF) documents are disseminated in an incredible rhythm across social media. Negative incidences are obvious but effective solutions identifying falsified items in the PDF are still in need. Unlike determining malicious scripts inserted into the file, this research aims at identifying falsified objects from different layers of the document. Specifically, we introduce Fals-Ism, a novel approach … …Journal of Computer Science, Vol. 19, Issue 5 · 11 May 2023 · 5,534 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEstablishment and Application of Rapid Quantitative Detection Technology for Bacillus cereusFeilong Sun, Hanyu Li, Jingxue Qian et al.Infections caused by Bacillus cereus often occur, causing a certain degree of health hazards and economic losses to people around the world. Rapid quantitative detection is of great significance for prevention. This study aims to develop a simple and rapid quantitative detection method for B. cereus, which can be used to monitor the contamination of B. cereus in food samples. … …American Journal of Biochemistry and Biotechnology, Vol. 19, Issue 1 · 21 Mar 2023 · 5,567 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 · 548 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 · 683 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 · 1,233 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 · 1,761 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 · 1,518 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 · 1,560 accessesView PDFCite