Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Muthukrishnan, R.; Kalaivani, S. Title A name given to the resource A New Approach to Robust Weighted Support Vector Regression and Its Applications in Medical MRI Image Processing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.347-356 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_29</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010607542?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010607542?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Muthukrishnan R., Bharathiar University, Tamil Nadu, Coimbatore, India; Kalaivani S., Christ University, Karnataka, Bangalore, India Description An account of the resource In recent years, the field of machine learning has experienced significant growth, with the emergence of various advanced technologies leveraging its principles. Among these, Support Vector Regression (SVR) has established itself as a widely recognized and robust regression technique. This article introduces a novel approach, Robust Hampel Weight-Based Support Vector Regression (RH-SVR), designed to enhance the resilience and efficiency of traditional SVR. The study investigates and compares several regression methods, including the Robust Linear Model (RLM), SVR, RH-SVR, and Least Squares Regression (LS). An experimental analysis was conducted using MRI images of the human heart and brain, both in their original form and with added noise at varying levels (10, 20, and 30%). Performance metrics such as Mean Square Error (MSE), Median Absolute Error (MDAE), Relative Standard Error (RSE), and Peak Signal-to-Noise Ratio (PSNR) were evaluated. The results consistently demonstrate that the proposed RH-SVR method achieves lower error rates and higher PSNR values, showcasing superior accuracy and robustness, particularly when processing noisy images. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Image processing; Robust weight function; Support vector regression Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online