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Adaptive Machine Learning System for Contextual Plagiarism Identification

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Adaptive Machine Learning System for Contextual Plagiarism Identification


Khilendra Shivprasad Thakre



Khilendra Shivprasad Thakre "Adaptive Machine Learning System for Contextual Plagiarism Identification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.504-513, URL: https://www.ijtsrd.com/papers/ijtsrd101638.pdf

In the digital age we live in now, the amount of information available is rapidly increasing due to the rapid growth of online resources; this makes finding knowledge easier than ever; however, it also raises some very serious issues concerning plagiarism. Plagiarism, defined as taking or copying someone else's work without their permission, is becoming a serious problem for universities and colleges, research organizations, and businesses that rely on content as part of their product/service. In the past, plagiarism detection relied heavily on exact-matching techniques and the time-consuming processes of manually comparing the suspected plagiarized material to source material, which often miss paraphrased or similar semantics. This proposed system will provide accurate and efficient plagiarism detection using Artificial Intelligence based methods such as Machine Learning and Natural Language Processing. The system will preprocess documents submitted by users to provide a cleaned document (using tokenization, stop word removal, and normalization), and then extract relevant features (meaningful linguistic patterns in the cleaned document) before applying advanced similarity measurement algorithms and trained ML models to the extracted features to identify duplicate, paraphrased and contextually similar material. In contrast to traditional approaches, the proposed approach uses semantic analysis to derive meaning from the textual content, rather than just comparing words on the surface. The output of the system is a detailed report of the similarity of the input docu-ments to documents that produced matches, with an accompanying percentage of plagiarism, to aid in determining the extent of plagiarism to assist users in assessing their respective submissions in terms of likely academic integrity violations.

Plagiarism Detection, Artificial Intelligence, Natural Language Processing, Machine Learning, Text Similarity, Semantic Analysis


IJTSRD101638
Special Issue | Smart Innovations in Computer Science and Applications, March 2026
504-513
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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