Advancements and Applications of Machine Learning in Detecting Radon Nuclear Tracks from 2001 to 2023: A Bibliometric Analysis

Abstract

We present a bibliometric analysis of the advancements in machine learning for detecting radon nuclear tracks, using publications from 2001 to 2023 sourced from Scopus and Web of Science databases. We analyze the growth in research output, particularly highlighting contributions from China and the United States, and identify key themes such as "machine learning", "radon", "neural networks", and emerging methods like "xgboost" and "long short-term memory networks". Our findings underscore the collaborative efforts within the field, as evidenced by the global authorship networks. The research landscape is mapped out, revealing core and peripheral areas of study that define the current state and prospects of radon detection research. The present study encapsulates the evolution of the field and emphasizes the necessity for continued interdisciplinary collaboration to enhance radon risk assessment methods.