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@article{194268,
        author = {Ayush Singh and Sheetal Borhade and Yash Sinkar and Aniket Swami},
        title = {PLAGISHIELD: A DEEP LEARNING APPROACH FOR PARAGRAPH LEVEL PARAPHRASE GENERATION PLAGIARISM DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4044-4050},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194268},
        abstract = {A Deep Learning Plagiarism detection has become increasingly vital in both academic and professional settings, where ensuring originality is a top priority. PlagiShield is a React-based web application created to spot and highlight duplicated content with high accuracy. It leverages Natural Language Processing (NLP) techniques such as text tokenization, stop-word elimination, and similarity checks through approaches like TF- IDF (Term Frequency–Inverse Document Frequency) and Cosine Similarity. With a clean and user-friendly interface, users can input their text and instantly receive a similarity score along with highlighted areas that may indicate plagiarism. Unlike traditional tools, PlagiShield emphasizes being lightweight, scalable and easy to integrate, making it ideal for use by academic institutions, businesses, and content creators alike},
        keywords = {Plagiarism Detection, Natural Language Processing (NLP), Semantic Similarity, TF-IDF, Cosine Similarity, BERT, Sentence Embeddings, Multilingual Detection, Academic Integrity, Text Mining.},
        month = {March},
        }