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147 results for “Deep Learning Models”
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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 · 1,873 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 abstractHybrid Deep Learning Model for Evaluating Subjective Answers Based on Semantic Textual SimilaritySiddhesh Kudtarkar, Kavita ShirsatSubjective answer evaluation requires accurately identifying semantic similarities between student and reference responses. This study introduces a Hybrid Deep Learning Model (HDLM) that integrates CNN, GRU, and LSTM architectures to assess Semantic Textual Similarity (STS) more effectively. The HDLM employs two parallel branches-CNN-GRU and CNN-LSTM to capture both local syntactic features and long-range contextual dependencies, followed by Manhattan distance for … …Journal of Computer Science, Vol. 22, Issue 4 · 7 Apr 2026 · 1,690 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 · 1,753 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 · 1,849 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Context Aware Deep Learning Framework for Multi-Level Arabic Fake News Detection Using Hybrid Linguistic RepresentationsNoor Mahmoud Alkudah, Somia Abufakher, Raja Masadeh et al.The rapid spread of false information on social media has made it even more important to be able to spot false news, especially in Arabic-speaking areas where language is more complicated and news is organized in a hierarchy. Recent research show that deep learning and transformer-based models work well, but most of them only look at performance metrics and don't … …Journal of Computer Science, Vol. 22, Issue 3 · 27 Mar 2026 · 1,566 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
Research ArticleOpen accessIn the abstractTransfer Learning-Driven Binary Classification of Chest X-ray for Pneumonia Using Deep Convolutional ArchitecturesNusrath Fathima, Pradeep KumarPneumonia is the most common infectious cause of lung inflammation, resulting from the presence of viruses or bacteria in the microscopic air sacs. In recent years, artificial intelligence, particularly deep learning, has gained significant traction in the field of medical imaging. Early detection and diagnosis of pneumonia using chest X-rays can support radiologists in clinical decision-making and subsequent treatment planning, … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,542 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Deep Learning for Kidney Stone Detection in CT Scans With Noise Reduction and Feature EnhancementU. M. Fernandes Dimlo, Sreenu Banoth, Priti Bihade et al.The identification of kidney stones using CT scans is an essential but difficult effort in medical diagnostics, frequently obstructed by imaging noise and the complexity of manual interpretation. Although conventional methods are widely used, they suffer from inaccuracies and inefficiencies, necessitating the development of automated diagnostic solutions. This study tackles these issues by presenting HDCNRNet, a hybrid deep learning network … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 2,161 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 · 1,607 accessesView PDFCite