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A bibliometric analysis on deep learning during 2007–2019
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2020-06-28 , DOI: 10.1007/s13042-020-01152-0
Yang Li , Zeshui Xu , Xinxin Wang , Xizhao Wang

As an emerging and applicable method, deep learning (DL) has attracted much attention in recent years. With the development of DL and the massive of publications and researches in this direction, a comprehensive analysis of DL is necessary. In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords “deep learning” and “machine learning” was published in 2007). By preprocessing, 5722 publications are exported from Web of Science and they are imported into the professional science mapping tools: VOS viewer and Cite Space. Firstly, the publication structures are analyzed based on annual publications, and the publication of the most productive countries/regions, institutions and authors. Secondly, by the use of VOS viewer, the co-citation networks of countries/regions, institutions, authors and papers are depicted. The citation structure of them and the most influential of them are further analyzed. Thirdly, the cooperation networks of countries/regions, institutions and authors are illustrated by VOS viewer. Time-line review and citation burst detection of keywords are exported from Cite Space to detect the hotspots and research trend. Finally, some conclusions of this paper are given. This paper provides a preliminary knowledge of DL for researchers who are interested in this area, and also makes a conclusive and comprehensive analysis of DL for these who want to do further research on this area.



中文翻译:

关于2007-2019年深度学习的文献计量分析

作为一种新兴且可应用的方法,深度学习(DL)近年来引起了很多关注。随着DL的发展以及朝着这个方向的大量出版物和研究,对DL进行全面分析是必要的。本文从文献计量学的角度,对DL出版物进行了综合分析,从2007年到2019年(第一个包含“深度学习”和“机器学习”关键字的出版物于2007年出版)。通过预处理,从Web of Science导出了5722种出版物,并将它们导入专业的科学制图工具:VOS Viewer和Cite Space。首先,根据年度出版物以及生产力最高的国家/地区,机构和作者的出版物来分析出版物的结构。其次,通过使用VOS浏览器,描述了国家/地区,机构,作者和论文的共引网络。进一步分析了它们的引文结构及其影响力。第三,VOS浏览器展示了国家/地区,机构和作者的合作网络。从Cite Space导出关键字的时间线审阅和引文猝发检测,以检测热点和研究趋势。最后,给出了本文的一些结论。本文为对这一领域感兴趣的研究人员提供了有关DL的初步知识,并对希望在该领域进行进一步研究的人员进行了DL的结论性和综合性分析。VOS查看器说明了国家/地区,机构和作者的合作网络。从Cite Space导出关键字的时间线审阅和引文猝发检测,以检测热点和研究趋势。最后,给出了本文的一些结论。本文为对这一领域感兴趣的研究人员提供了有关DL的初步知识,并对希望在该领域进行进一步研究的人员进行了DL的结论性和综合性分析。VOS查看器说明了国家/地区,机构和作者的合作网络。从Cite Space导出关键字的时间线审阅和引文猝发检测,以检测热点和研究趋势。最后,给出了本文的一些结论。本文为对这一领域感兴趣的研究人员提供了有关DL的初步知识,并对希望在该领域进行进一步研究的人员进行了DL的结论性和综合性分析。

更新日期:2020-06-28
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