The Application of Machine Learning in Detecting Plagiarism in Academic Works

Abstract

In the academic realm, the proliferation of digital content and the ease of access to information have exacerbated the issue of plagiarism, posing a significant challenge to the integrity of scholarly work. Traditional methods of plagiarism detection often fall short in effectively identifying instances of academic dishonesty amidst the vast volume of online resources. To address this challenge, researchers and educators have turned to machine learning techniques as a promising solution.This paper explores the application of machine learning algorithms in detecting plagiarism in academic works. It begins by discussing the evolution of plagiarism detection methods, highlighting their limitations and the need for more sophisticated approaches. Subsequently, it delves into the principles of machine learning and its relevance in developing robust plagiarism detection systems.Various machine learning models, including but not limited to, supervised learning, unsupervised learning, and deep learning, are examined in the context of plagiarism detection. The paper elucidates how these models leverage features such as textual similarity, semantic analysis, and syntactic patterns to identify plagiarized content accurately.