Simple Journal Adviser for Scientific Articles

This study introduces a prototype recommendation system for efficient scientific information management in the era of big data. Using metadata and keyword filtering, the system intended to help researchers choose the most suitable journal for publication by analyzing factors such as citation counts and publication dates. It compiles a thematic list of significant scientific sources. The prototype leverages machine learning algorithms for accurate and personalized journal recommendations, enhancing scientific information retrieval and maximizing the researcher’s impact in the scientific community through publishing the articles in the most fit journals.