Needs of Scientometry and Possibilities of Modern Machine Learning as a Field of Artificial Intelligence
-
Published:2023-06
Issue:2
Volume:50
Page:114-120
-
ISSN:0147-6882
-
Container-title:Scientific and Technical Information Processing
-
language:en
-
Short-container-title:Sci. Tech. Inf. Proc.
Subject
General Computer Science
Reference27 articles.
1. Nalimov, V.V., Naukometriya. Izuchenie razvitiya nauki kak informatsionnogo protsessa (Scientometry: Studying the Development of Science As Information Process), Moscow: Nauka, 1969. 2. Ozcan, S., Boye, D., Arsenyan, J., and Trott, P., A scientometric exploration of crowdsourcing: Research clusters and applications, IEEE Trans. Eng. Manage., 2020, vol. 69, no. 6, pp. 3023–3037. https://doi.org/10.1109/tem.2020.3027973 3. Giliarevski, R.S. and Melnikova, E.V., Rejection of the priority of international science citation indices in the evaluation of results of scientific activity in China, Sci. Tech. Inf. Process., 2020, vol. 47, no. 3, pp. 194–199. https://doi.org/10.3103/S0147688220030107 4. Eykens, J., Guns, R., and Engels, T., Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches, Quant. Sci. Stud., 2021, vol. 2, no. 1, pp. 89–110. https://doi.org/10.1162/qss_a_00106 5. Huang, H., Zhu, D., and Wang, X., Evaluating scientific impact of publications: Combining citation polarity and purpose, Scientometrics, 2021, vol. 127, no. 9, pp. 5257–5281. https://doi.org/10.1007/s11192-021-04183-8
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|