Research ArticleOpen accessPhrase in the titleA Framework for Real-Time Healthcare System Performance in Developing CountriesRazi Ahmed, Eiad Yafi, Mazliham Mohd Su’ud et al.This study proposes a conceptual structure for the success of the real-time healthcare program in developing countries. Whilst developing and some developed countries face a significant challenge in adopting the application of real-time healthcare, this work aims to analyze the perspectives of healthcare practitioners in Malaysia. Since 1997, Malaysia has been putting efforts to introduce a real-time healthcare system, but … …Journal of Computer Science, Vol. 16, Issue 9 · 24 Sep 2020 · 6,990 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTrends in the Prevalence of Clostridioides difficile Using Real Time PCR in South Korea (2020-2024)Yonghee Lee, Yoo Na Chung, Jae Kyung KimClostridioides difficile is a major cause of infection in healthcare settings, and frequent antibiotic use exacerbates its occurrence. C. difficile infection (CDI) is a major public-health challenge owing to its complications and high mortality rate. We aimed to analyze current trends in C. difficile prevalence. This study was a retrospective, single center analysis of 7,371 samples from April 2020 to … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 2 · 3 Oct 2025 · 2,363 accessesView PDFCite
Research ArticleOpen accessIn the keywordsIntelliHealth: A Machine Learning Driven Disease Detectionand Diet recommendation SystemAnkita Wadhwan, Priyanka Chawla, Sandeep Kaur, Usha MittalPeople all around the world are afflicted with various ailments. An accurate diagnosis can lower the risk of significant health problems developing, but an inaccurate diagnosis could have adverse implications. In this study, an ensemble-based strategy "IntelliHealth" has been presented to identify disorders of the thyroid, liver, and breast cancer using three machine learning (ML) approaches consisting of Support Vector … …Journal of Computer Science, Vol. 21, Issue 6 · 28 May 2025 · 5,239 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 abstractRefining Chest X-ray Interpretation with Deep Transfer Learning TechniquesR. Sriramkumar, K. Selvakumar, J. JeganChest X-rays are essential diagnostic tools for thoracic diseases, but different doctors' interpretations can differ greatly, which frequently results in inconsistent diagnoses. By using deep transfer learning techniques, this study seeks to improve the accuracy of Chest X-ray interpretations. More accurate and effective analysis can be accomplished with the growing potential of artificial intelligence (AI) in medical imaging, especially through … …Journal of Computer Science, Vol. 21, Issue 10 · 27 Nov 2025 · 2,655 accessesView PDFCite
Research ArticleOpen accessIn the abstractPHASEY: A Contrastive Learning Approach for Enhanced Human Gait Phases RecognitionUrvashi, Deepak Kumar, Vinay Kukreja, Ayush DograHuman gait has gained much attention in behavioral biometrics as it possesses unique and distinctive characteristics. Gait phases, which describe the different patterns of human walking, are significant for the analysis and understanding of movement in an individual. Hence, the identification of gait phases is important for the accurate determination and interpretation of walking patterns, ranging from healthcare and security … …Journal of Computer Science, Vol. 21, Issue 8 · 16 Sep 2025 · 4,643 accessesView PDFCite
Research ArticleOpen accessIn the abstractTowards a Human-Like AGI Architecture: General Intelligence Framework (GIF)Jiran Kurian Puliyanmakkal, Rohini VArtificial Intelligence (AI) has achieved significant breakthroughs but remains limited by its specialization and inability to generalize across domains, unlike human cognition. Current models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) excel at specific tasks but struggle with real-time adaptability and cross-domain generalization. This paper introduces the General Intelligence Framework (GIF), an approach designed to … …Journal of Computer Science, Vol. 21, Issue 7 · 13 Jul 2025 · 4,443 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