Bibliometric Analysis of Global Scientific Literature on Robust Neural Network

Tengku Nurul Aimi Balqis Tengku, Malim Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat
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Abstract

The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.
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基于鲁棒神经网络的全球科学文献计量分析
该研究旨在对Scopus数据库中与鲁棒神经网络主题相关的出版物进行书目综述。截至2022年9月13日,本研究基于鲁棒神经网络搜索过程中的关键词,成功收集了2019-2023年的16篇文章。这三种工具被用来分析收集到的Scopus数据库,它们是Microsoft Excel, VOSviewer软件和Harzing的Publish and Perish软件。本研究报告了鲁棒神经网络研究的当前趋势和出版物的影响。根据文献计量学分析,出版物的数量一直在增加。本研究仅关注Scopus数据库。对于未来的研究,可以考虑其他数据库,如PubMed, Lens, Dimensions和Web of Science,这样研究结果将更有意义和影响力。本研究首次对神经网络相关文献进行综述。
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