![]() Subsequently, we apply three similarity estimation models including η-gram overlap, Longest Common Subsequence, and Vector Space Model to compute the degree of similarity between manual-automatic textual description pairs. To answer that question, in this study we have generated textual descriptions of 552 process models using two approaches (1) manual textual description approach and (2) automatic textual description approach. Once the answer to this question is known, it may lead to several conclusions, such as, the two descriptions can be used as an alternative to each other or not. However, it is not clear, how similar or different are the two descriptions (manually generated and automatic generated). Both ways of generating descriptions have their strengths and weaknesses. Textual description of business process models can be generated either manually or automatically. Plagiarism detection on text based electronic assignments. Therefore, we present AntiPlag, a fast and effective tool for Isolate plagiarized text based assignments from non-plagiarised assignmentsĮasily. Times faster than the commercial tool considered. In addition, to improve theĭetection latency, AntiPlag applies a data clustering technique making it four Pre-processing steps performed in AntiPlag. ![]() Results in terms of false positives compared to the commercial tool due to the Three sets of textīased assignments were tested by AntiPlag and the results were compared againstĪn existing commercial plagiarism detection tool. Our plagiarism detection tool named AntiPlag isĭeveloped using the tri-gram sequence matching technique. On creating an effective and fast tool for plagiarism detection for text basedĮlectronic assignments. ![]() Worse with the availability of ample resources on the web. In Universities and other academic institutions. Plagiarism is one of the growing issues in academia and is always a concern
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