# Evaluation of Different Plagiarism Detection Methods: A Fuzzy MCDM Perspective
## 2 Literature Review  
[PERSON] and [PERSON] [15] presented a critical review of existing approaches for paraphrase detection, as well as their use of automated plagiarism findings. It described the classifications of weird occurrences and the basic methodologies, as well as the groups of attributes that each technique utilized. All of the approaches and characteristics employed were reviewed as well as being listed in a table for comparative purposes. The performance of available plagiarism-detection methods capable of detecting paraphrases in benchmark corpora was evaluated and reviewed. Their significant observation was that word overlapping and structural interpretations, as well as MT procedures, are feature subcategories that contribute to the greatest presentation outcomes for a support vector machine (SVM) in both paraphrase recognition as well as plagiarism detection in corpora. A study of the effectiveness of deep learning methods demonstrated that they are the most interesting study direction in this discipline. [PERSON] et al. [16] introduced a novel technique named citation-based plagiarism finding, that was tested with the help of a Ph.D. thesis [17], and in which a volunteering crowdsourcing effort termed GuttenPlag [18] found significant levels of plagiarism through meticulous manual review. They demonstrated that citation-based plagiarism detection outperforms text-based techniques in detecting robust paraphrasing and conversion, as well as certain-idea plagiarism. They showed that merging citation-based, as well as text-based plagiarism identification, can enhance finding capability. [PERSON] et al. [19] investigated and compared the differences among several systems, as well as how their effectiveness compared to manual testing. They looked at the various strategies students used to attempt plagiarism. Afterwards they looked more deeply at the technologies that help with plagiarism detection, from their features to how they operate. During the process, they evaluated how these methods compared to their own, as well as their potential for assisting in the detection of entries that were plagiarized in the beginning C++ course. [PERSON] et al. [20] presented a freshly produced large-scale corpus of artificial plagiarism that may be used to evaluate both intrinsic and external plagiarism detection. Furthermore, novel detection performance metrics for evaluating plagiarism-detection algorithms were provided. [PERSON] and [PERSON] [21] developed a hierarchical classification of the most popular types of plagiarism found in student papers. Their purpose-built trial set comprised texts that contain examples of various regularly used plagiaristic strategies. Although Sherlock was definitely the greatest hermetic detection tool overall, SafeAssignment was the strongest at identifying web-based plagiarism. Turnitin was discovered to be a highly effective method for identifying semi-automatic types of plagiarism, as the replacement of Cyrillic counterparts for particular letterings or the addition of bogus whitespaces. According to their research, none of the solutions is able to successfully identify plagiarism from both local as well as web sources while also identifying the technical methods that plagiarizers employ to disguise plagiarism. [PERSON] et al. [22] introduced a detection approach for source code-based plagiarism depending on the transitional representation as well as demonstrating its application in e-learning. The technique was validated on a sufficient amount of test cases representing the most common code-change strategies. The findings and effectiveness of the method were matched to the contemporary source code-based plagiarism-detection techniques used in a few of the most well-known plagiarism finding tools and systems. [PERSON] and [PERSON] [23] suggested a unique fuzzy-based method to source-code-based plagiarism findings that takes advantage of the popular Fuzzy C-Means (FCM) as well as adaptive neuro-fuzzy inference system methods.