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A Visualized Bibliometric Analysis for Mapping Research Trends of Machine Learning in Academic Research
1
Zitationen
4
Autoren
2023
Jahr
Abstract
This study aims to conduct a comprehensive bibliometric analysis of machine learning research within the academic research from 2013 to 2023. The goals include comprehending how publication trends have changed over time, pinpointing important fields of interest, and clarifying how machine learning and scholarly research interact. This study's methodology combines a bibliometric framework with a qualitative descriptive approach. The data were collected from Scopus database, which consist of 4.655 documents for analysis. The analysis is executed using R studio and Biblioshiny. The result shows that the rise of machine learning in academic research between 2013 and 2023 suggests that this field of study will continues to be a trending area for academic exploration.
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