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
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