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@article{184999,
        author = {G.Narendra Reddy and B.sai Deepak and S.Nikhil and G.Bharath},
        title = {Removing Multiple Votes By Using De-Duplication Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {4415-4419},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184999},
        abstract = {Free and fair elections are the backbone of every democracy. Ensuring the uniqueness and reliability of voter records is a critical step in guaranteeing this fairness. Duplicate votes—arising from data entry mistakes, fraudulent registrations, or integration of multiple regional databases—undermine the transparency of the electoral process. This paper presents an ex- tended study and a practical framework for voter de-duplication using advanced computational techniques. The framework incorporates string similarity algorithms, hashing mechanisms, and clustering models to efficiently detect and eliminate redundant records. Our implementation in Python with MySQL backend demonstrates over 96% accuracy in duplicate detection across synthetic datasets. In addition to technical performance, we discuss implications for electoral trust, governance, and large- scale deployment. The proposed system thus ensures accuracy, reduces fraud, and strengthens democratic values by preventing multiple voting attempts.},
        keywords = {De-duplication, Election Integrity, Data Cleaning, Voter Database, Machine Learning, Data Mining, Electoral Transparency},
        month = {September},
        }