Version 1
: Received: 30 August 2024 / Approved: 30 August 2024 / Online: 2 September 2024 (03:31:23 CEST)
How to cite:
Chigarev, B. On Visual Data Analysis of IEEE Xplore Bibliometric Records on Machine Learning and Artificial Intelligence for Power Systems. Preprints2024, 2024090003. https://doi.org/10.20944/preprints202409.0003.v1
Chigarev, B. On Visual Data Analysis of IEEE Xplore Bibliometric Records on Machine Learning and Artificial Intelligence for Power Systems. Preprints 2024, 2024090003. https://doi.org/10.20944/preprints202409.0003.v1
Chigarev, B. On Visual Data Analysis of IEEE Xplore Bibliometric Records on Machine Learning and Artificial Intelligence for Power Systems. Preprints2024, 2024090003. https://doi.org/10.20944/preprints202409.0003.v1
APA Style
Chigarev, B. (2024). On Visual Data Analysis of IEEE Xplore Bibliometric Records on Machine Learning and Artificial Intelligence for Power Systems. Preprints. https://doi.org/10.20944/preprints202409.0003.v1
Chicago/Turabian Style
Chigarev, B. 2024 "On Visual Data Analysis of IEEE Xplore Bibliometric Records on Machine Learning and Artificial Intelligence for Power Systems" Preprints. https://doi.org/10.20944/preprints202409.0003.v1
Abstract
This paper addresses the lack of analytical tools for visual data analysis in IEEE Xplore, an abstract database of publications on technical problems in electronics and electrical engineering. The openness of the platform and the good fillability of a large number of bibliometric record fields make it attractive for visual data analysis. The topic of machine learning and artificial intelligence applied to power systems, which are critical issues in the energy transition process, is chosen as an example for visual data analysis. The programs and utilities used for data analysis were VOSviewer, Scimago Graphica, FP-Growth algorithm, Inkscape, and MultiDendrograms. The use of IEEE Terms field data to identify publication topics in the exported data is analyzed in the most comprehensive way. This paper can be considered a reference for professionals interested in bibliometric analysis and visualization of IEEE Xplore data.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
