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118 results for “hybrid learning”
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Research ArticleOpen accessIn the abstractArabic Fake News Detection Across Generational Text Representations: From Traditional Models to Transformer-Based MethodologiesNoor M. AlkudahThe rapid proliferation of fake news on Arabic social media has amplified societal and political risks, yet research on automatic detection in Arabic remains limited due to scarce datasets, morphological complexity, and underexplored preprocessing strategies. This study presents a comprehensive benchmark for Arabic fake news detection, unifying seven Machine Learning (ML) algorithms, three Deep Learning (DL) models, and a transformer-based … …Journal of Computer Science, Vol. 22, Issue 4 · 17 Apr 2026 · 1,877 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 · 2,333 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT-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 accessIn the abstractA Comprehensive Review of Medical Image Denoising: Techniques, Challenges, Applications and Future DirectionsMohit Sharma, Ayush Dogra, Anita Gupta et al.The concept of noise reduction in medical images plays a vital role in improving image quality and accuracy associated with diagnosis, and facilitating reliable automated analysis. This survey presents a structured overview of medical image denoising, beginning with foundational definitions and noise models for modalities such as MRI and CT. We critically evaluate persistent gaps, including the limited generalizability of … …Journal of Computer Science, Vol. 22, Issue 3 · 28 Mar 2026 · 1,861 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent System for Cardiovascular Disease Prediction Using Deep LearningDeepika Arora, Deepak Dudeja, B K AgrawalHeart disease remains among the major causes of death in the world. Hence, accurate and reliable prediction schemes are key. To detect the presence of diseases in early stages, data mining and deep learning algorithms offer powerful tools to determine important trends in large and complex data. To enhance the prediction of cardiac diseases, this paper proposes a hybrid deep … …Journal of Computer Science, Vol. 22, Issue 2 · 9 Mar 2026 · 1,613 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,882 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmploying Hybrid Serial Cascaded Adaptive Network for Anomaly Detection and Prevention in IoT Time Series Data With Optimal Interdomain RoutingKante Satyanarayana, K. VenkateshNowadays, emerging Internet of Things (IoT) in data processing becomes interested research topic and is used for various applications. This varies from big data processing and analytics for accumulating entire sensor data to detect and generate long-term trends. Nevertheless, this results in the requirement for resources like the execution of power, memory, and bandwidth, in which resource-constrained IoT tools suffers … …Journal of Computer Science, Vol. 22, Issue 1 · 21 Feb 2026 · 1,476 accessesView PDFCite
Research ArticleOpen accessIn the abstractTomato Leaf Disease Detection by Hybrid Ai and Ml TechnologyP. Loganathan, M. John Britto, Vinston RajaDiseases that affect plants contribute to productivity decline, but they can be managed with ongoing monitoring. It is time-consuming and prone to error to track plant diseases manually. Early disease detection using Artificial Intelligence (AI) and machine vision can lessen negative impacts while also overcoming some of the limitations of continuous human monitoring. Therefore, this study uses both Deep Learning … …Journal of Computer Science, Vol. 21, Issue 12 · 12 Feb 2026 · 1,889 accessesView PDFCite
Research ArticleOpen accessIn the abstractSelf-Supervised Contrastive Learning for Steering Angle Prediction in Autonomous Driving SimulationsMaxson Phang, Suryadiputra LiawatimenaPredicting steering angles is a crucial task in autonomous driving, with end-to-end deep learning being a widely used approach. However, such models often suffer from overfitting to learned trajectories, limiting their generalization to new data. To address this, self-supervised contrastive learning is explored as an alternative, allowing models to learn meaningful representations without labeled data. The primary objective is to … …Journal of Computer Science, Vol. 21, Issue 9 · 20 Oct 2025 · 2,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Study on Factors Influencing MBA Decision After Bachelor’s Using Hybrid Model Combining ANN With Principal Component AnalysisKittipol Wisaeng, Benchalak MuangmeesriThis study comprehensively examines the complex relationship between various individual characteristics and individuals’ expectations after obtaining a bachelor’s degree, particularly in determining whether to pursue a Master of Business Administration (MBA). Utilizing a well structured, hypothesized framework, we examine several key variables directly influencing this critical decision-making process. The examined variables include, but are not limited to, demographic information, academic … …Journal of Computer Science, Vol. 21, Issue 9 · 13 Oct 2025 · 2,247 accessesView PDFCite