# Evaluation of Different Plagiarism Detection Methods: A Fuzzy MCDM Perspective
## 2 Literature Review
Furthermore, the quality of the suggested approach was evaluated by the modern plagiarism identification Running Karp-Rabin Greedy-String-Tiling (RKR-GST) method. Fuzzy C-Means as well as the Adaptive-Neuro Fuzzy Inference System (ANFIS) were the foundations of their suggested algorithm. The suggested technique had the advantage of being programming-language-free, therefore there is no requirement to construct any permitted parsers or translators for the fuzzy-based prediction to recognize in multiple programming languages. Their findings showed that the suggested fuzzy-based technique outperforms all existing methods on popular source code datasets, revealing impressive outcomes as an effective and convenient solution to source-code plagiarism identification. [PERSON] et al. [24] provided a summary of efficient plagiarism-detection systems that have been utilized for natural-language textual plagiarism-finding, external plagiarism-finding, and clustering-based plagiarism-finding, as well as a few techniques utilized in code-source plagiarism identification. They correspondingly conducted a comparative analysis of five applications utilized for text-plagiarism identification. [PERSON] et al. [25] examined some plagiarism-detection methods in source-code documents. The tools were evaluated in terms of functionality and effectiveness. They conducted two tests for the performance analysis. To assess the sensitivity of the techniques for different plagiarism tactics, they applied the tools to a sample of purposefully plagiarised programs. To gain a sense of the tools' accuracy, they executed them through numerous incarnations of a student project and evaluated the top ten findings. According to our literature review, a wide variety of techniques and platforms have been created over the recent decades to encourage robust and efficient plagiarism identification. The most well-known techniques have been capable of addressing the important challenges concerning the retrieval of important syntactic as well as semantic features, managing both monolingual as well as cross-lingual plagiarism identification, and the identifying plagiarism in both textual documents and program-source code even without the use of references. Moreover, despite the accelerated advancement of technologies to ensure its reproduction, backup, as well as distribution of these techniques and platforms, some major concerns and research directions remain unaddressed. In this segment, we will highlight some of the challenges and opportunities. * Creating advanced plagiarism-checking techniques that can function without external references while maintaining good precision is a complex job. * There is still a lack of a detection approach for both textual data and source code which helps to ensure both evidence of accuracy and reliability and proof of thoroughness. * Another difficult task is the creation of a correct and comprehensive catalogue of references depending on the article writer traces. According to plagiarism-detection groups, until recent years, many studies were concentrated on verbatim plagiarism, such as word-for-word plagiarism as well as paraphrase plagiarising. Efficient academic plagiarism-detection method selection using the MCDM method was employed in this research for detailed comparison, co-occurrence similarity, language-model likelihood, and dependency-connection matching.