Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

28-06-2020 | Original Article | Issue 12/2020

International Journal of Machine Learning and Cybernetics 12/2020

A bibliometric analysis on deep learning during 2007–2019

Journal:
International Journal of Machine Learning and Cybernetics > Issue 12/2020
Authors:
Yang Li, Zeshui Xu, Xinxin Wang, Xizhao Wang
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

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.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 12/2020

International Journal of Machine Learning and Cybernetics 12/2020 Go to the issue