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159 results for “Features extraction”
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Review ArticleOpen accessIn the keywordsA Review Study of Brain Activity-Based Biometric AuthenticationAla Abdulhakim Alariki, Abdul Wasi Ibrahimi, Mohammad Wardak, John WallBiometrics is the process of identifying an individual among others by biological means. Concerning security, biometric system is one of the best options available in this technology driven era. Places such as nuclear facilities, airports, banks etc. are on the frontline of security threats. Therefore, biometrics such as Iris, face and fingerprint recognition is frequently used to avoid any security … …Journal of Computer Science, Vol. 14, Issue 2 · 27 Jan 2018 · 9,471 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features FusionTanzila SabaArabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number … …Journal of Computer Science, Vol. 14, Issue 2 · 7 Feb 2018 · 8,305 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSegmentation and Recognition of Handwritten Numeric ChainsSalim Ouchtati, Mouldi Bedda, Abderrazak LachouriAutomatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of … …Journal of Computer Science, Vol. 3, Issue 4 · 30 Apr 2007 · 7,636 accessesView PDFCite
Research ArticleOpen accessIn the abstractEpsilon-Insensitive Primal Loss With Support Vector Machine for Robust Cotton Leaf Disease ClassificationPavana Kumar Eramachetty Dhanajayalu, Malathi Sivasamy, Chitveli Siva Balaji YadavCotton diseases significantly reduce both yield and quality of crops, making early and accurate identification of the disease crucial for effective prevention and control. However, achieving a balance between lightweight models and precise classification of cotton diseases remains challenging. This difficulty arises primarily from the dispersed nature of disease symptoms and interference from noisy backgrounds, which complicate the detection process. … …Journal of Computer Science, Vol. 22, Issue 6 · 5 Aug 2026 · 755 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 abstractHybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer DetectionAanchal Vij, Kuldeep Singh Kaswan, Anand NayyarEarly detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning model that combines convolutional, attention-based, and transformer-guided representations to address these challenges in multi-class lung cancer classification. For deep feature extraction, we use the EfficientNetV2-S architecture augmented with a Convolutional Block … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jun 2026 · 1,528 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDFCite
Research ArticleOpen accessIn the abstractAuto Weight Dilated Convolutional Ensemble Network for the Severity Analysis of Lung Cancer in CT ImagesL. Sandhya, K. MarimuthuLung cancer is one of the world's leading causes of morbidity and mortality; improving patient outcomes requires an early and precise diagnosis. Lesion and tumor segmentation remains a challenging task in CT images due to their inherent imaging limitations, such as the small size of nodules, heterogeneous textures, blurry boundaries, and adjacent structures, leading to misclassification and difficulty in delineating … …Journal of Computer Science, Vol. 22, Issue 1 · 9 Feb 2026 · 1,541 accessesView PDFCite
Research ArticleOpen accessIn the abstractCPF-Net: Cross-modal CT and Pathology Guided Feature Learning for CT-based Lung Cancer Subtype ClassificationPeizhi Tan, Debiao YanAccurate classification of lung cancer subtypes from CT images remains challenging due to the subtle radiological differences between adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). We propose CPF-Net, a deep learning framework that integrates CT and pathological information through a Linear Spatial Reduction Attention (LSRA) module. The framework processes whole slide images using a modified CTransPath architecture for pathological feature … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 3 · 23 Jan 2026 · 1,856 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector AlgorithmFizhan Kausar, Ramamurthy B.The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, … …Journal of Computer Science, Vol. 21, Issue 12 · 22 Jan 2026 · 1,563 accessesView PDFCite