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 Joan Sandeep Larson, M.; Sivakumar, R.; Murugesan, Ranjani Title A name given to the resource AI-Enabled Brand-to-Generic Medication Recommendation System for Pharmacy: Addressing Consumer Brand-Name Bias Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 International Conference on Communication, Computing and Emerging Technologies, IC3ET 2026;pp.358-363 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3ET64989.2026.11467238" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3ET64989.2026.11467238</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037427915?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037427915?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 Joan Sandeep Larson M., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India; Sivakumar R., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India; Murugesan R., Statistics and Data Science, CHRIST (Deemed to Be University), Bangalore, India Description An account of the resource In countries like India, many people choose branded medicines over equally effective generic options, which drives up healthcare costs. This paper introduces a smart recommendation system that can tell whether a medication query is about a brand-name or a generic drug. It then offers more affordable alternatives based on the active ingredients. By using natural language processing and retrieval-augmented generation (RAG) with a detailed medicine database, the system accurately classifies and recommends options. Its conversational interface mimics a real pharmacy interaction, helping users make informed choices while saving money. Tests show the system responds quickly, usually within 7 seconds, and provides accurate answers over 80 % of the time for straightforward queries. Ultimately, this tool addresses the information gaps about generic drugs and branded ones and is easy to use for both consumers and pharmacists. 2026 IEEE. Subject The topic of the resource brand-generic classification; healthcare cost reduction; medicine recommendation; natural language processing; pharmaceutical alternatives; retrieval-augmented generation (RAG) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158105-3; 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