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• W2182775343 abstract "Plagiarism involves reproducing the existing information in modified format or sometimes the original document as it is. This is quiet common among students, researchers and academicians. This has made some strong influence on research community and awareness among academic peoples to prevent such a kind of malpractice. Though there exits some commercial tools to detect plagiarism, still plagiarism is tricky and quiet challenging task due to abundant information available online. Commercially existing software adopt methods like paraphrasing, sentence matching or keyword matching. Such techniques are not too good in identifying the plagiarized contents effectively. However this paper focuses its attention on identifying some key parameters that would help to identify plagiarism in a better manner. The results seem to be promising and have further scope in detecting the plagiarism." @default.
• W2182775343 created "2016-06-24" @default.
• W2182775343 creator A5087787170 @default.
• W2182775343 date "2012-01-01" @default.
• W2182775343 modified "2023-09-26" @default.
• W2182775343 title "Automatic Plagiarism Detection Using Similarity Analysis" @default.
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