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- W3112850470 abstract "Plagiarism refers to the use of other people’s ideas and informationwithout acknowledging the source. In this research, anti-plagiarism softwarewas designed especially for the university and its campuses to identify plagiarizedtext in students’ written assignments and laboratory reports. The proposed frameworkcollected original documents to identify plagiarized text using natural languageprocessing. Our research proposes a method to detect plagiarism byapplying the core concept of text, which is semantic associations of words andtheir syntactic composition. Information on the browser was obtained throughRequest application programming interface by name Url.AbsoluteUri, and it isstored in a centralized Microsoft database Server. A total of 55,001 data sampleswere collected from 2015 to 2019. Furthermore, we assimilated data from a universitywebsite, specifically from the psau.edu.sa network, and arranged the datainto students’ categories. Furthermore, we extracted words from source documentsand student documents using the WordNet library. On a benchmark datasetconsisting of 785 plagiarized text and 4,716 original text data, a significant accuracyof 90.2% was achieved. Therefore, the proposed framework demonstratedbetter performance than the other available tools. Many students mentioned thatworking on assignments using the framework was suitable because they were ableto work on the assignments in harmony, as per their timeframe and from differentnetwork locations. The framework also recommends procedures that can be usedto avoid plagiarism." @default.
- W3112850470 created "2020-12-21" @default.
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- W3112850470 date "2021-01-01" @default.
- W3112850470 modified "2023-09-23" @default.
- W3112850470 title "University Learning and Anti-Plagiarism Back-end Services" @default.
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- W3112850470 doi "https://doi.org/10.32604/cmc.2020.012658" @default.
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