Bibliometric analysis on tendency and topics of artificial intelligence over last decade
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.
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.
- 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
- 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
- Brunette ES, Flemmer RC, Flemmer CL (2009) A review of artificial intelligence. In International conference on autonomous robots and agents, pp 385–392Google Scholar
- 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
- 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
- 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
- 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
- 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
- Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Inf Comput Sci 48(48):284–300Google Scholar
- Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold Co., New YorkGoogle Scholar