Research ArticleOpen accessIn the keywordsFuzzy Method for Online Learning of Bayesian Network ParametersMariana D.C. Lima, Silvia M. NassarIn learning problems, there are situations where training data is not fully available at the learning time. They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data. In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional … …Journal of Computer Science, Vol. 15, Issue 3 · 16 Mar 2019 · 7,498 accessesView PDFCite
Research ArticleOpen accessIn the abstractCrop Disease Detection via an Improved Residual NetworkGuolin Chen, Weifeng Liu, Xinyu Zou, Xiaoxia LiDeep learning has shown substantial potential across many domains, and crop disease detection is no exception. This study investigates the application of deep learning to crop disease detection, systematically evaluating the performance of convolutional neural networks of different depths. Building on the classical ResNet-50 as the base model, we introduce a Squeeze-and-Excitation (SE) attention mechanism to enhance feature extraction. The … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 2 · 31 Jul 2026 · 898 accessesView PDFCite
Research ArticleOpen accessIn the abstractCuckoo Search Optimized Random Forest for Breast Cancer PrognosisPrasad S. Sase, Debabrata Swain, Shailesh KumarBreast cancer remains a major global health concern due to its high mortality rate, particularly when diagnosis occurs at an advanced stage. Accurate and early differentiation between benign and malignant tumors is therefore critical for improving patient outcomes. Conventional diagnostic practices largely depend on manual assessment and clinical expertise, which may lead to subjective variability in decision-making. To overcome this … …Journal of Computer Science, Vol. 22, Issue 1 · 31 Jan 2026 · 2,098 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Data Enhancement Method Based on Generative Adversarial Networks for Soybean Leaf Disease IdentificationXiao Yu, Cong Chen, Qi Gong, Lina LuSoybean leaf disease data collection is an expensive and time-consuming task. Convolutional neural network training requires a large amount of data, but traditional data enhancement methods (such as rotation, flipping, translation) are restricted by fixed rules and cannot generate images with diversity and variability. Aiming at the problem of the lack of soybean leaf disease data set, this study proposes … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 4 · 26 Nov 2022 · 5,207 accessesView PDFCite
Research ArticleOpen accessIn the abstractBoosting Kernel Discriminative Common Vectors for Face RecognitionC. Lakshmi, M. PonnavaikkoProblem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 6,571 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordDual-Branch Shape Texture Learning With Pyramid Residual and Sobel Edge Layers for Rice Variety ClassificationM Pradeep, M SiddappaAutomated rice variety classification categorizes rice types based on features, such as grain texture, shape, color, and size. Traditional handcrafted feature-based models fail at rice classification due to lighting variations, background noise, and environmental conditions, leading to overlapping visual characteristics and frequent misclassification. To address these challenges, this research proposes a novel Dual-Branch Convolutional Neural Network with Pyramid Residual Units … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 627 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordAn Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container TerminalMostafa Al Uahabi, Hicham Attariuas, Mohammed Saleh, Mohamed ChentoufContainer terminals are critical nodes within the maritime transportation system that have a vital function in global merchandise trade, handling a significant volume of cargo through the use of various equipment and personnel. Thus, the efficiency of container terminal operations relies heavily on the ability to collect, analyze, and utilize operational data. However, such data can be corrupted by noise, … …Journal of Computer Science, Vol. 20, Issue 3 · 25 Jan 2024 · 5,585 accessesView PDFCite