Research ArticleOpen accessIn the keywordsComparative Analysis of Fraud Detection Methods in Banking Using Machine Learning TechniquesYoussef Tounsi, Ennouri TaziFraud detection in banking requires algorithms that balance classification performance, computational efficiency, and regulatory interpretability, criteria that are rarely benchmarked together. We present a comprehensive evaluation of nine machine learning approaches (Logistic Regression, Decision Tree, Random Forest, SVM, SGD, XGBoost, CatBoost, LightGBM, and MLP) across three datasets differing in size, imbalance severity, and feature type (synthetic, real-world PCA-anonymized, and large-scale … …Journal of Computer Science, Vol. 22, Issue 6 · 22 Jun 2026 · 1,341 accessesView PDFCite
Research ArticleOpen accessIn the abstractLightweight 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 accessIn the abstractOptimized YOLOv8 for Insect Detection in Agricultural Farms Using Edge Vision SystemsDeebalakshmi R., Adhithyaa S., Yoga Vignesh V., Balaji Ganesh RajagopalFarmers started to adopt smart gadgets like automated pest detection and kiosks for their work in light of the AI revolution in agriculture. These kinds of technologies help farmers identify pest infestations early, which allows the farmers to take action accordingly and prevent crop loss. This results in improvement in both yield and sustainability. However, running an advanced model such … …Journal of Computer Science, Vol. 21, Issue 11 · 28 Jan 2026 · 2,640 accessesView PDFCite
Research ArticleOpen accessIn the abstractReal-Time Endoscopic Image Semantic Segmentation for Efficient Polyp IdentificationM. Al-AsliThe advancement of endoscopic procedures has significantly enhanced diagnostic capabilities in polyp identification and removal, which is critical for colorectal cancer prevention. However, the success of these procedures hinges on the accuracy and speed of real-time image analysis. Traditional image processing methods often fail to provide timely and precise segmentation of endoscopic images. This paper presents the implementation of endoscopic … …Journal of Computer Science, Vol. 21, Issue 10 · 10 Dec 2025 · 2,397 accessesView PDFCite
Research ArticleOpen accessIn the abstractPHASEY: A Contrastive Learning Approach for Enhanced Human Gait Phases RecognitionUrvashi, Deepak Kumar, Vinay Kukreja, Ayush DograHuman gait has gained much attention in behavioral biometrics as it possesses unique and distinctive characteristics. Gait phases, which describe the different patterns of human walking, are significant for the analysis and understanding of movement in an individual. Hence, the identification of gait phases is important for the accurate determination and interpretation of walking patterns, ranging from healthcare and security … …Journal of Computer Science, Vol. 21, Issue 8 · 16 Sep 2025 · 4,638 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Conceptual Framework for Efficient and Sustainable Pruning Techniques in Deep Learning ModelsNyalalani Smarts, Rajalakshm Selvaraj, Venumadhav KuthadiThis research paper proposes a conceptual framework and optimization algorithm for pruning techniques in deep learning models, its focus is on key challenges such as model size, computational efficiency, inference speed and sustainable technology development. The aim of the framework is to transition from large neural networks to sparse, efficient models, indicating the benefits of pruning in improving model scalability … …Journal of Computer Science, Vol. 21, Issue 5 · 26 Apr 2025 · 5,268 accessesView PDFCite
Research ArticleOpen accessIn the abstractRF-CNNS: Thin Deep Learning Networks For Accelerating Traffic Signs RecognitionBtissam Bousarhane, Driss BouzidiRoad scene analysis is a wide domain of research that aims to ameliorate the environmental perception in intelligent transportation systems, including autonomous vehicles and advanced driver-assistance systems. It also plays a crucial role in road safety improvement, by contributing to the reduction of traffic accidents' rate. Ensuring an efficient recognition of traffic signs contributes enormously to making cars safer, which … …Journal of Computer Science, Vol. 19, Issue 10 · 18 Sep 2023 · 5,335 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Approach based on Winter's Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal DataSuhartono, Muhammad Hisyam LeeProblem statement: In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 18 Jul 2011 · 7,942 accessesView PDFCite