Machine learning in industrialization: a bibliometric analysis

Loayza-Delgado G.A.; Tejada-Montalvo X.L.; Carnero-Quispe M.F.; Gárate-Rodríguez C.F. · DYNA (Colombia) (2025) · DOI: 10.15446/dyna.v92n235.115909

Resumen

Machine learning is currently emerging as one of the most rapidly advancing technologies, with a recent upward trend in its use for process automation across industrial processes. The objective of this study was to conduct a bibliometric analysis to identify research trends in machine learning. The Scopus database was used to identify scientific production. Bibliometric indicators of visibility, impact, and concurrence were analyzed. The analysis of 7,335 documents, involving 22,383 authors, showed a growth rate of 20.86% from 1988 to early 2024. Three dominant research trends were identified: the first based on machine learning applications in industrial processes, the second referring to the human factor and artificial intelligence, and the third related to convolutional neural networks.

Instituciones: Universidad Católica San Pablo