Content analysis and duplication avoidance system using machine learning

In many academic institutions, the process of reviewing and approving student project titles and content is still handled manually. This traditional approach is time-consuming, prone to oversight, and often results in unintentional duplication of projects. The current system lacks an organized way to track previously completed work, leading to repetitive submissions and limited innovation. This paper proposes an efficient, centralized repository system for storing past student projects. The aim is to automate the review process, enable easy comparison of new proposals against existing work, and encourage students to explore novel ideas. By leveraging this structured framework, institutions can significantly reduce redundancy and promote originality in academic project development. This paper describes the development and deployment of a custom-built plagiarism detection system aimed at upholding academic integrity within educational institutions. The system leverages natural language processing (NLP) techniques, specifically utilizing the cosine similarity algorithm, to perform accurate textual comparisons. Experimental evaluations demonstrate that the system is both reliable and efficient in identifying instances of plagiarism in student assignments and research documents. Core functionalities include comprehensive text preprocessing, similarity measurement, and an intuitive user interface for ease of use. The results affirm that implementing such dedicated plagiarism detection tools is vital for ensuring originality and fostering a culture of ethical academic practices in universities. This approach not only enhances the detection of textual redundancy but also supports content originality, making it valuable for academic institutions, content management systems, and publishing platforms. The system aims to promote ethical content usage and improve the overall quality of digital information.