International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Driven Platform for Women Empowerment |
|---|---|
| ABSTRACT | Women in India continue to encounter serious obstacles when trying to find clear, reliable, and timely information about government welfare schemes, largely because information is scattered, eligibility rules are complex, many resources are not available in local languages, and digital skills remain limited for a large segment of the population. Existing portals usually list schemes in a broad, non-targeted way, rarely tailoring suggestions to an individual woman’s profile and often including programs that do not concern women at all. This study proposes a dedicated, women centric recommendation platform powered by generative AI to help Indian women identify government schemes that match both their needs and their eligibility. A curated and verified dataset serves as the unified source of information, and rigorous rule-based filters are applied to exclude inapplicable and male-only schemes. The recommendation engine adopts a hybrid strategy that blends deterministic eligibility checks, semantic similarity analysis, and generative AI–driven explanations to improve both precision and user understanding. In addition, the platform supports multiple Indian languages and voice-based interaction to reach rural users and women with low literacy levels. Experimental results indicate that the platform provides more relevant recommendations, clearer reasoning, and higher user engagement than conventional government scheme portals. Keywords Women Empowerment, Government Schemes, Generative AI, Recommendation System, Eligibility Filtering, Multilingual Assistant, Social Welfare Technology. |
| AUTHOR | GANGAMMA HEDIYALAD, NOOR FATIMA, R S CHAITRA, RAMYA VARSHINI M, DIA V JAIN Assistant Professor, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India UG Student, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312035 |
| pdf/35_AI-Driven Platform for Women Empowerment.pdf | |
| KEYWORDS |
