Advertisement

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

  • Fang Gao
  • Xiaofeng JiaEmail author
  • Zhiyun Zhao
  • Chih-Cheng ChenEmail author
  • Feng Xu
  • Zhe Geng
  • Xiaotong Song
Technical Paper

Abstract

Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and society in the future. By means of bibliometric method, the study aims at providing an overview on the research tendency and the most concerned topics of AI during the past decade. The database of Web of Science was chosen and the articles published in AI journals were retrieved. Top 10% of the yearly high-citation articles (12,301 articles) published since the year of 2008 were selected as sampling articles for the analysis. The bibliographic records were used for the overall analysis, and the core keywords were studied and classified into three categories (algorithm, general technology and application technology) for topics analysis. As results, number of articles in AI by year and country, the country collaboration and well-known institutes and researchers in AI were presented. Also we proposed and concluded the five most concerned topics, which are perception intelligence (1st), human mind simulated intelligence (2nd), classical model based machine learning (3rd), bio-inspired intelligence (4th), and big-data based intelligence (5th). It is the authors’ wish that the study were helpful for researchers to have an overall grasp of the recent status of AI development.

Notes

Acknowledgements

This study is supported by grants from The National Key Research and Development Program of China (No. 2016YFC1201702). All the authors declare that they have no financial interest in the findings of this study.

References

  1. Aličković E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Appl 28(4):753–763CrossRefGoogle Scholar
  2. Amodei D, Anubhai R, Battenberg E et al (2016) Deep speech 2: end-to-end speech recognition in english and mandarin. In: Proceedings of the 33rd international conference on machine learning, JMLR: workshop and conference proceedings, vol 48, New York, NY, USAGoogle Scholar
  3. An X, Wu Q (2011) Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics 88:133–144CrossRefGoogle Scholar
  4. Archambault E, Gagne EV (2004) The use of bibliometrics in the social sciences and humanities, Final report for the Social Sciences and Humanities Research Council of Canada. Science-Metrix Publisher, MontrealGoogle Scholar
  5. Baskin II, Madzhidov TI, Antipin IS, Varnek AA (2017) Artificial intelligence in synthetic chemistry: achievements and prospects. Russ Chem Rev 86(11):1127–1156CrossRefGoogle Scholar
  6. Brunette ES, Flemmer RC, Flemmer CL (2009) A review of artificial intelligence. In International conference on autonomous robots and agents, pp 385–392Google Scholar
  7. Callon M, Courtial JP, Laville F (1991) Co-word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymer chemistry. Scientornetrics 22(1):155–205CrossRefGoogle Scholar
  8. Campbell M, Hoane AJ Jr, Hsu FH (2002) Deep blue. Artif Intell 134(1):57–83CrossRefzbMATHGoogle Scholar
  9. Cronin B, Shaw D, La Barre K (2014) Visible, less visible, and invisible work: patterns of collaboration in 20th century chemistry. J Assoc Inf Sci Technol 55(2):160–168CrossRefGoogle Scholar
  10. Ding Y, Chowdhury GG, Foo S (2001) Bibliometric cartography of information retrieval research by using co-word analysis. Inf Process Manag 37(6):817–842CrossRefzbMATHGoogle Scholar
  11. Eslami M, Shareef H, Mohamed A (2011) Application of artificial intelligent techniques in PSS design: a survey of the state-of-the-art methods. Przeglad Elektrotechniczny 87(87):188–197Google Scholar
  12. Gao F, Han P, Zhai YJ, Chen LX (2011) Application of support vector machine and ant colony algorithm in optimization of coal ash fusion temperature. In: International conference on machine learning and cybernetics, pp 666–672Google Scholar
  13. Glänzel W, Schubert A (2004) Analysing scientific networks through co-authorship. In: Moed HF, Glänzel W, Schmoch U (eds) Handbook of quantitative science and technology research. Springer, Dordrecht, pp 257–276Google Scholar
  14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, NV, USA , 27–30 June 2016.  https://doi.org/10.1109/CVPR.2016.90
  15. Jia X, Dai T, Guo X (2014) Comprehensive exploration of urban health by bibliometric analysis: 35 years and 11,299 articles. Scientometrics 99(3):881–894CrossRefGoogle Scholar
  16. Kostoff RN, Toothman DR, Eberhart HJ, Humenik JA (2001) Text mining using database tomography and bibliometrics: a review. Technol Forecast Soc Chang 68(3):223–253CrossRefGoogle Scholar
  17. Lazer D, Kennedy R, King G, Vespignani A (2014) Big data. The parable of Google flu: traps in big data analysis. Science 343(6176):1203CrossRefGoogle Scholar
  18. Lee EJ, Kim YH, Kim N, Kang DW (2017) Deep into the brain: artificial intelligence in stroke imaging. J Stroke 19(3):277–285CrossRefGoogle Scholar
  19. Lu H, Li Y, Chen M, Kim H, Serikawa S (2017) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375CrossRefGoogle Scholar
  20. Mansiaux Y, Carrat F (2012) Contribution of genome-wide association studies to scientific research: a Bibliometric Survey of the Citation Impacts of GWAS and Candidate Gene Studies Published during the Same Period and in the Same Journals. PLoS One 7(12):e51408CrossRefGoogle Scholar
  21. Mellit A, Kalogirou SA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34(5):574–632CrossRefGoogle Scholar
  22. Mnih V, Badia AP, Mirza M, Graves A, Lillicrap TP, Harley T, Silver D, Kavukcuoglu K (2016) Asynchronous methods for deep reinforcement learning. In: Proceedings of the 33rd international conference on machine learning, JMLR: workshop and conference proceedings, vol 48, New York, NY, USAGoogle Scholar
  23. Neff MW, Corley EA (2009) 35 years and 160,000 articles: a bibliometric exploration of the evolution of ecology. Scientometrics 80(3):657–682CrossRefGoogle Scholar
  24. Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Inf Comput Sci 48(48):284–300Google Scholar
  25. Niu J, Tang W, Xu F, Zhou X, Song Y (2016) Global research on artificial intelligence from 1990-2014: spatially-explicit bibliometric analysis. ISPRS Int J Geo-Inf 5(5):66CrossRefGoogle Scholar
  26. Pan Y (2016) Heading toward artificial intelligence 2.0. Engineering 2(4):409–413CrossRefGoogle Scholar
  27. Pasandideh SHR, Niaki STA, Nia AR (2011) A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Syst Appl 38(3):2708–2716CrossRefGoogle Scholar
  28. Poria S, Cambria E, Bajpai R, Hussain A (2017) A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fus 37:98–125CrossRefGoogle Scholar
  29. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van DG, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489CrossRefGoogle Scholar
  30. Sunil KJ, Bilalovic J, Jha A, Patel N, Zhang H (2017) Renewable energy: present research and future scope of Artificial Intelligence. Renew Sustain Energy Rev 77:297–317CrossRefGoogle Scholar
  31. Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold Co., New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Scientific and Technical Information of China (ISTIC)BeijingChina
  2. 2.New-Generation Artificial Intelligence Development Research Center of Ministry of Science and Technology of the People’s Republic of China (MOST)BeijingChina
  3. 3.School of Information EngineeringJimei UniversityXiamenChina

Personalised recommendations