Machine Learning-Based Analysis For The Characterization And Phenomenological Study Of Two-Phase Fluids: A Bibliometric Study Using Vosviewer And Scopus

Bibliometric analyses have been the primary way of examining and evaluating the literature within a field of study. By focusing on citation count and source, researchers have been able to identify journal articles that are considered high impact in scope and relevance, qualifying them as "citation classics" in a field. In this context, this article analyzed the characteristics of publications related to Deep Learning Based Analysis for the characterization and Phenomenological Study of multiphase fluids. The research was conducted in the Scopus database to identify the academic participation in this topic and the data were analyzed using the VOSviewer software, with a scientific mapping methodology.