# Evaluation of Different Plagiarism Detection Methods: A Fuzzy MCDM Perspective
## 1 Introduction  
The ease with which information may be shared via global interactive communication platforms has motivated literature to explore the internet for information in the chosen manner. This capability has had a detrimental impact in that people seek to authorship their work (written, organized, and formatted documents, presentations, and scripts) by reproducing other people's concepts or research without proper acknowledgment, which has been seen, especially in the academic world. As of now, plagiarism detection is among the highly critical tasks with a multi-focus on academic literature standards and text mining, as well as NLP, with many unresolved concerns including standards and borderline sets. Some are very simple, while others necessitate the use of complex algorithms as well as scientific principles in order to provide the numerous advantages of plagiarism detection [1; 2; 3; 4; 5; 6; 7; 8].  
Plagiarism by researchers in papers as well as by students in projects is not really a new challenge, but it has been exacerbated by the simplicity of \"copying and pasting\" information from publications as well as other sources of information on the Web. Utilizingcontent created by others can be purposeful, but it is more typically an unexpected mistake. With the software applications that are presently available, publishers, educators, examiners, and others can better easily recognize plagiarism as well as do not having to depend on their own talents to recognize similarities to already-published work [9; 10; 11; 12]. Plagiarism-checking systems can analyze author documents as well as student papers in a matter of a few minutes, comparing what they have provided to previously published literature. This article explores technology and software and makes some tips for the editor as well as authors on how to use it correctly [13; 14; 15].  
Selecting the best reliable and efficient plagiarism-detection method can be challenging with so many options available nowadays. Therefore, this paper presents a fuzzy TOPSIS-based method for selecting an efficient academic-plagiarism-detection method. A quantitative research study on various types of plagiarism, indicated over a wide range of plagiarized scripts, revealed that every year, plagiarism checkers have managed to succeed in considering extrinsic plagiarism activities, whereas intrinsic plagiarism purely relies on stylometric functionality investigated by leveraging the arrangement of the papers. An in-depth assessment of the classification of plagiarism-checking methods that centered on textual characteristics, semantic structures, organizational characteristics, and candidate-information extraction prototypes, as well as plagiarism-finding procedures, was conducted. Concept plagiarism appears in the downstream hierarchical structure of smart plagiarism types and cannot be revealed as it is deprived of the textual semantics which convey the concept as well as the localization of perspective in the format.  
This study focuses on evaluating the functioning mechanism of different plagiarism-detection methods, a topic that has yet to be thoroughly investigated. The rest of the paper is organized as follows: Section 2 goes through some of the linked publications, including a summary of research projects on the assessment of plagiarism-detection methods. Section 3 outlines the evaluation approach developed for prioritizing various plagiarism-detection methods. Section 4 describes the investigational findings and results. Lastly, Section 5 covers the conclusion, including the societal benefits of the method used and research directions in this field of study.