https://jcmm.co.in/index.php/jcmm <p>The <em>Journal of Computers, Mechanical and Management (JCMM)</em> [e-ISSN: 3009-075X] is a peer-reviewed, open-access journal published by AAN Publishing, Malaysia. It publishes research in Engineering, Basic Sciences, Humanities, and Management, providing a platform for researchers to share new ideas and findings.</p> <p>There is <strong>no Article Processing Charge (APC) until December 2025</strong>. From <strong>January 2026 onwards</strong>, an <strong>APC of USD 250 per article</strong> will apply.</p> <p>For more details, visit our <a href="https://jcmm.co.in/index.php/jcmm/about" target="_blank" rel="noopener">About JCMM</a> page.</p> AAN Publishing en-US Journal of Computers, Mechanical and Management 3009-075X <p>The <em>Journal of Computers, Mechanical and Management</em> applies the <a href="http://creativecommons.org/licenses/by-nc/4.0/"><em>CC Attribution- Non-Commercial 4.0 International License</em> </a>to its published articles. While retaining copyright ownership of the content, the journal permits activities such as downloading, reusing, reprinting, modifying, distributing, and copying of the articles, as long as the original authors and source are appropriately cited. Proper attribution is ensured by citing the original publication.</p> https://jcmm.co.in/index.php/jcmm/article/view/148 <p>The global concern over diabetes-related eye diseases continues to grow significantly. Diabetic retinopathy, caused by heightened glucose levels in retinal capillaries, leads to vision clouding and eventual blindness. Early detection through regular screening enables intervention with medication, preventing further vision deterioration. Therefore, this study introduces a smart application that utilizes digital retinal image processing to aid in the prompt identification of diabetic retinopathy. The application streamlines the analysis of eye images, with the goal of automatically classifying the severity of diabetic retinopathy. Through initial image processing, specific features such as blood vessels, microaneurysms, and hard exudates are identified and extracted for classification using a support vector machine (SVM). Evaluation performed on a dataset of 400 retinal images graded on a 4-grade scale of non-proliferative diabetic retinopathy achieved a maximum sensitivity rate of 95%. This application holds significant potential for enabling timely intervention in the treatment of diabetic retinopathy by healthcare professionals. Additionally, the AI-driven approach proposed in this study empowers patients to easily access support services, while providing physicians and researchers with advanced tools for analyzing and predicting diabetic retinopathy data. The resulting reports play a crucial role in assessing the severity of the disease in affected individuals.</p> S. K. Mydhili R. Ramitha Devi T. A. Benazir R. Poornima Copyright (c) 2025 Journal of Computers, Mechanical and Management https://creativecommons.org/licenses/by-nc/4.0 2025-01-27 2025-01-27 4 1 1 10 10.57159/jcmm.4.1.25148