Editor’s NoteJournal of Scientometric ResearchVol. 8 | Issue 2s | 2019 | pp. s1Open access
Special Issue on Machine Learning in Scientometrics
- 1*,
- 2
- 1 PES University and Center for AstroInformatics, Bangalore, Karnataka, INDIA.
- 2 Centre for Studies in Social Sciences, Calcutta and University of Bonn, GERMANY.
Published in Journal of Scientometric Research
Correspondence: Snehanshu Saha
PES University and Center for AstroInformatics, Bangalore, Karnataka, INDIA.
Email: snehanshusaha@pes.edu
Copyright: © 2019 Manuscript Technomedia. This is an open access article.
- Published:
- Nov 1, 2019
How to cite
Saha, S., & Kar, S. (2019). Special Issue on Machine Learning in Scientometrics. Journal of Scientometric Research, 8(2s), s1. https://doi.org/10.5530/jscires.8.2.20
Abstract
Scientometrics is a domain that performs a quantitative and qualitative assessment of research and scientific progress. The field has earned popularity in last few years owing to the need to measure research outputs at individual, institutional and geographical levels. As a result of this need, different parameters are brought-up and various databases like Scopus, Web of Science and Google scholar are built for computation of these parameters. The data generated and stored as a result of proliferation of research papers and other scientific activities is vast. Analysis of the data cannot be performed without the intervention of sophisticated tools and techniques. Consequently, the use of Machine leaning algorithms for carrying out tasks like classification, regression, clustering and associations on these databases becomes imminent.
Subject
Article metadata
| Title | Special Issue on Machine Learning in Scientometrics |
|---|---|
| Authors | Snehanshu Saha; Saibal Kar |
| Affiliations | PES University and Center for AstroInformatics, Bangalore, Karnataka, INDIA.; Centre for Studies in Social Sciences, Calcutta and University of Bonn, GERMANY. |
| Corresponding author | snehanshusaha@pes.edu |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 8, Issue 2s (2019) |
Also in this issue
- On the Implications of Artificial Intelligence and its Responsible Growthpp. s2–s6
- Analyzing the Common Wisdom of Binarization Doctrine in Internationality Classification of Journals: A Machine Learning Approachpp. s7–s38
- Relevance of Innovations in Machine Learning to Scientometricspp. s39–s43
- SES-RREF: The Machine Learning Approach to Credible Metrics of Scholastic Evidence via Recursive Referencingpp. s44–s73
- Treatment Repurposing using Literature-related Discoverypp. s74–s84
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