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 Karthick, K.K.; Ganesan, V.; Sivakumar, R.; Sivaraman, M.; Natarajan, Arul Kumar; Shahina, A. Srimathi Title A name given to the resource A Computing Assisted Test Method in Healthcare Industry Using Artificial Intelligence 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 Proceedings - 1st International Conference on Frontier Technologies and Solutions, ICFTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICFTS62006.2025.11031857" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICFTS62006.2025.11031857</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010828041?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010828041?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 Karthick K.K., Saveetha Engineering College, Tamilnadu, Chennai, India; Ganesan V., Dr.G.R.Damodaran College of Science, Tamilnadu, Coimbatore, India; Sivakumar R., Christ (Deemed to be University, Karnataka, Bengaluru, India; Sivaraman M., Dr. Sns Rajalakshmi College of Arts and Science, Tamilnadu, Coimbatore, India; Natarajan A.K., University of Technology and Applied Sciences (UTAS), Oman; Shahina A.S., Dr.G.R.Damodaran College of Science, Tamilnadu, Coimbatore, India Description An account of the resource New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This research goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The creation of prediction models and tools to assess risk has become an important field of research as it can assist healthcare workers in identifying individuals who are more likely to acquire breast cancer and adapting screening and preventative programs accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more in-depth exploration of the subject and the various technologies, methods and factors involved in breast cancer prediction. 2025 IEEE. Subject The topic of the resource Algorithms; Analysis; Breast Cancer; Data; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150276-8; 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