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357 results for “data classification”
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Research ArticleOpen accessIn the abstractExplainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM VisualizationD. Vetrithangam, Pramod Kumar, Shilpasree S. et al.Kidney tumor classification from CT imaging remains challenging due to CT-specific intensity variations, tumor heterogeneity, and limited clinically meaningful interpretability in existing deep learning models. To address these limitations, we propose an Explainable MedAttn-ResNet50 framework that integrates radiology-driven preprocessing, multi-scale feature extraction, and medical attention mechanisms within a unified architecture. The preprocessing pipeline applies radiology-inspired intensity normalization, contrast enhancement, and … …Journal of Computer Science, Vol. 22, Issue 8 · 13 Aug 2026 · 1,120 accessesView PDFCite
Research ArticleOpen accessIn the abstractFuzzyNET: A Fuzzy Logic-Based Framework for Enhancing Urban Efficiency in Smart CitiesHemant Kumar, Safdar Tanweer, Parul Agarwal et al.The smart city paradigm emphasizes the strategic use of IoT, Big Data, and digital technologies to optimize urban operations and improve service delivery for citizens. Embedding digital technologies within operational workflows is driven by the objective of improving efficiency and long-term sustainability. The core transformation is Artificial Intelligence (AI) that revolutionizes management of cities through automation and instantaneous decisions based … …Journal of Computer Science, Vol. 22, Issue 7 · 19 Jul 2026 · 820 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnxiety-Focused Classification Using Smartphone-Based Gait Analysis and Deep Learning ModelsMinchan Kang, Gou-Sung Degbey, Hyunhwa Lee et al.Mood disorders, such as anxiety, significantly impact individual well-being and present challenges to healthcare systems globally. Traditional screening methods, including self-administered symptom questionnaires, provide valuable insights into symptoms but may miss dynamic fluctuations. This study explores the potential of smartphone inertial sensors and deep learning models for anxiety risk classification. Using gait data from 36 participants, collected via smartphone accelerometers … …Journal of Computer Science, Vol. 22, Issue 7 · 16 Jul 2026 · 821 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Machine Learning Models to Predict Student Academic Performance Using Structured Educational DataHardik Ishwarbhai Patel, Dharmendra PatelThis study analyses the use of machine learning for predicting the academic performance of students using their academic information from the institution, combined with socio-economic information that comes from outside sources. The collection of information is done using structured questionnaires as well as through data extraction from the Student Information System (SIS). To increase the reliability of models built, a … …Journal of Computer Science, Vol. 22, Issue 5 · 26 May 2026 · 1,419 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnimal Health Prediction Using Hybrid CNN Based BiLSTM Classification Model: A Deep Learning ApproachJ. Rathi, A. SumathiAccurate prediction of animal diseases is vital in veterinary medicine, as it can substantially enhance animal health outcomes and mitigate economic losses, making it a critical task that warrants attention and innovative solutions. This paper proposes a novel approach to animal condition classification, leveraging an Auto-encoder-based feature selection process and an Improved Hybrid Convolutional Neural Networks (CNN) with Bidirectional (Bi-LSTM) … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,481 accessesView PDFCite
Review ArticleOpen accessIn the abstractImproving Classification and Clustering Accuracy of Palembang Duku Fruit Quality via CNN-K-Means IntegrationHenny Hartono, Francka Sakti Lee, Herlina Herlina et al.Duku Palembang (Lansium domesticum Corr.) is a leading commodity from South Sumatra with high economic value. The quality assessment process for duku has traditionally relied on manual visual inspection, which is subjective, time-consuming, and inconsistent, especially at large production scales. This study proposes an integrative framework combining Convolutional Neural Network (CNN) and K-Means Clustering to improve the accuracy of classification … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 8 May 2026 · 1,475 accessesView PDFCite
Research ArticleOpen accessIn the abstractUncertainty-Aware Ensemble Models for Improved Defect Detection in Noisy DataMadhavi Perla, Gadi Lava Raju, A Radha Krishna et al.Software defect prediction plays a crucial role in ensuring software quality and reliability, especially as modern systems become more complex and data rich. This study introduces an uncertainty-aware ensemble learning framework aimed at improving defect classification performance in noisy and imbalanced datasets, particularly those from the PROMISE and NASA KC1 repositories. The proposed model integrates multiple classifiers in a multi-learner … …Journal of Computer Science, Vol. 22, Issue 4 · 27 Apr 2026 · 1,542 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Pipelines for Intelligent Human Resources Text Classification: LLMs, RAG, and Generative AISoumia Chafi, Mustapha Kabil, Abdessamad KamoussThe digital transformation of Human Resources Information Systems (HRIS) requires advanced approaches to process unstructured textual data originating from Curriculum Vitae (CVs), cover letters, and job postings. Traditional text classification methods exhibit limitations when faced with current needs for contextual understanding and fine-grained skill detection. This paper proposes a hybrid pipeline combining advanced text classification, contrastive learning (SimCSE, Contriever), Retrieval-Augmented … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,509 accessesView PDFCite
Research ArticleOpen accessIn the abstractMoney Laundering Detection in Financial Institutions Using Machine LearningNoor Samer Masood, Rasha Hassan Sakr, Amal Abou EleneenThis research aims to develop a system based on machine learning techniques for the accurate detection and classification of money laundering transactions. This system utilizes real and synthetic financial data, including the IBM AML dataset and synthetic financial databases. The methodology included data collection and cleaning, feature extraction from transaction behavior, and training several classification algorithms, including Random Forest, Support … …Journal of Computer Science, Vol. 22, Issue 4 · 16 Apr 2026 · 2,325 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning and Image Analytics for Forest Land Management: Classification, and Object DetectionDevam Dixit, Aditya Bhavsar, Shreyas Jani, Nilesh YadavRapid urbanization and industrialization have contributed to deforestation which has led to a severe degradation of the environment and biodiversity loss. Traditional field survey methods are not applicable in the monitoring of large forest areas due to the fact that such methods are manual, costly and time consuming as well as subject to human error. The current methods of remote … …Journal of Computer Science, Vol. 22, Issue 3 · 26 Mar 2026 · 1,590 accessesView PDFCite