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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 · 1,547 accessesView PDFCite
Research ArticleOpen accessIn the abstractEdge Computing Enabled Human Activity Recognition (ECEHAR) Using LSTM and CNNSuresh Kumar, M Y. Mohamed ParveesHuman Activity Recognition (HAR) is an important research area for various application domains such as healthcare, gaming, telemonitoring, and sports. However, executing HAR algorithms on remote servers or in the cloud have challenges in terms of high latency, increased bandwidth demand, and high energy consumption. Moving the computation to edge-assisted HAR is more effective and flexible solution to address the … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,584 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Approach for Differentiating AI-Generated and Real Images Using Gabor and Color Layout Filters (GF and CLF)G. Ayyappan, A. Thankaraj, S. Surendran et al.The emergence of generative AI images has profoundly upended the art world. Distinguishing AI-generated images from human-created artwork is increasingly challenging. If this issue remains unresolved, dishonest individuals may exploit those who are willing to pay more for original artwork and businesses whose stated policies exclude AI graphics. There are a number of methods for differentiating AI photos from human … …Journal of Computer Science, Vol. 22, Issue 2 · 3 Mar 2026 · 1,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Integrated Disease Progression Model to Analyze Electronic Health Records With Multimodal Datasets Using Deep Learning TechniquesThulaganyo Dimakatso, Venumadhav Kuthadi, Rajalakshmi Selvaraj, Othapile DinakenyaneIn contemporary clinical environments, precisely simulating the emergence of disease is an intricate task owing to heterogeneously derived data and variability in the record of diagnoses. The research presents a stable classification model that integrates image data with formalized patient data to track patterns in disease through various stages. The suggested approach combines a multi-stage analytical methodology that includes systematic … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,686 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassifcation of Social Media Posts X For Mental Health Symptoms Identification Using NLP Techniques and Transformers ModelAndika Dwi Asmoro Wicaksono, RojaliThe increasing use of social media has enabled large-scale analysis of user-generated content to monitor mental health trends. This study focuses on sentiment prediction of mental health-related posts on platform X using transformer-based models, specifically IndoBERT, DistilBERT, and IndoRoBERTa. The goal is to evaluate the effectiveness of these models in classifying social media posts into positive, negative, and neutral sentiment … …Journal of Computer Science, Vol. 22, Issue 1 · 11 Feb 2026 · 1,862 accessesView PDFCite
Research ArticleOpen accessIn the abstractFine-Tuned and Optimized Transformer-Based Model for EEG Seizure DetectionPuspanjali Mallik, Ajit Kumar Nayak, Kumar Janardan Patra et al.Epilepsy, a widely recognized neurological disorder, results from irregularities in the transmission of electrical impulses among neurons in the brain. Over the last two decades, significant efforts have been made by researchers and clinicians to develop effective methods for its early detection and management. The electroencephalogram (EEG), a non-invasive tool used to monitor brainwave activity, has become a central device … …Journal of Computer Science, Vol. 21, Issue 11 · 11 Feb 2026 · 1,424 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Dam Safety and Management: Long Short-Term Memory Based Predictive Models for Accurate Alert ForecastingNisha C. M, N. ThangarasuDam management and early alert systems are critical for effective water resource management. Accurate prediction of dam alert signals facilitates proactive decision-making, thereby aiding in the effective management and reduction of potential risks linked to dam operations. Within this research, Long Short-Term Memory (LSTM) networks are utilized to forecast dam alert signals issued from the dam by leveraging daily parameters, … …Journal of Computer Science, Vol. 22, Issue 1 · 6 Feb 2026 · 1,632 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 · 1,864 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 · 1,573 accessesView PDFCite
Research ArticleOpen accessIn the abstractBlockchain-Enabled Decisive Red Fox Optimizer-Based Feature Selection With Deep Learning-Based Intrusion Detection SystemC. Ananth, S. Sathiyarani, N. MohananthiniRecently, Intrusion Detection Systems (IDS) using Deep Learning (DL) models become useful in accomplishing network security. The selected features serve as input to the DL model, which is trained on labelled datasets to learn intrinsic patterns distinguishing malicious from normal network behaviour. DL techniques like Convolutional Neural Networks (CNN) are usually utilized. The seamless combination of Public Blockchain (BC) technology … …Journal of Computer Science, Vol. 21, Issue 12 · 20 Jan 2026 · 1,722 accessesView PDFCite