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@article{190645,
        author = {Ruturaj Khedkar and Vishal Kandhare and Aniket Lingayat and Mohan Bothikar and Shital Gaikwad},
        title = {Title Similarity Verification System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1808-1817},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190645},
        abstract = {With the increasing number of research papers submitted to conferences and journals, checking for similar or duplicate paper titles has become an important but time-consuming task. In many cases, different authors unintentionally submit titles that are very similar in meaning, which can create confusion during the review and indexing process. To address this issue, this paper presents an Automatic Title Similarity Verification System that helps in identifying closely related research paper titles in an efficient manner. The proposed system uses basic Natural Language Processing (NLP) techniques to process and compare titles. Initially, the titles are cleaned by converting them into a standard format through steps such as removing stop words and reducing words to their root forms. The processed titles are then converted into numerical representations using the cosine similarity and TF-IDF method. Finally, cosine similarity is applied to measure how closely two titles are related. Based on the similarity score, the system can determine whether titles are highly similar, moderately similar, or different. The experimental results show that the system is effective in detecting similar titles with good accuracy while requiring minimal computational resources. Due to its simplicity and reliability, the proposed system can be easily integrated into conference submission portals and journal management systems. This approach reduces manual effort and supports editors and reviewers in maintaining originality and quality in academic publications.},
        keywords = {Title Similarity, Natural Language Processing, Cosine Similarity, TF-IDF.},
        month = {January},
        }