Classifying of research papers based on their research areas or topics using machine learning

With the rapid advancement of computer and information technologies, a vast number of research papers are now available both online and offline. As new research fields continue to emerge, users face significant challenges in finding and categorizing papers of interest. To address these issues, this paper presents a research paper classification system that groups papers into meaningful categories based on shared topics. The system identifies representative keywords from each paper and uses the Term Frequency-Inverse Document Frequency (TF-IDF) method to measure the importance of words and measure of how many times a word appears in a document. The logistic regression algorithm is then employed to classify papers with similar topics.