A Comparative Study on Machine Learning based Prediction of Citations of Articles

Authors can use predictions to create very accurate estimations about the likely outcomes of a query based on past data, which can be about anything from customer churn to possible fraudulent conduct. The citation count indicates to the number of times publication has been cited. One of the most important considerations for a writer or author when publishing an article is how to make a significant effect on the content. The impact of a paper is broad, which increases the opportunity for fresh ideas and progress. Future paper citation counts will be useful for researchers in selecting representative literature because they are an important indicator for estimating possible influences of published papers. This is a regression problem. Predicting and comprehending article citation numbers, on the other hand, is a difficult problem to solve, both theoretically and empirically, as evidenced by decades of research. The influence of each work is predicted based on its previous citations. The goal is to precisely anticipate the number of citations that will be received over time. The proposed research study also provides a comparative analysis on the prediction of citations for articles.