Special issue on Machine Learning

Machine learning, a scientific discipline deals with developing systems which can learn from data and can make decisions by using the knowledge derived from the data. The discipline has been an important pillar of Artificial Intelligence, and has earned considerable attention from researchers worldwide because of its ability to extract knowledge from raw data by using sound statistical principles.

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. The indicators to mark research performance use citation information in a well-defined way. Citations have become a key component in evaluating performance for authors, articles and journals. To evaluate the role of Machine Learning in Scientometrics, ML techniques can help in predicting citation count, can provide useful insights on computing new bibliometric indexes and also, in finding associations among them. The usage of powerful statistical tools like multiple linear regression, convex/concave optimization and gradient ascent/descent algorithms can be explored in scientometric and bibliographic analysis.

The special issue aims to capture the baseline, set the tempo for future research in India and abroad and prepare a scholastic primer that would serve as a standard document for future research. we hope to learn about methods that are applicable to Scientometrics but are not currently used, and also making Computer Science practitioners aware of the interesting problems that complex Scientometric/Bibliometric data sets provide. We welcome original and unpublished contributions (adhering to the journal format) that discuss new developments in efficient models for complex computer experiments  and data analytic techniques which can be used in Scientometric data analysis as well as related branches in physical, statistical and computational sciences.

Topics: Specific topics of interest include, but are not limited to:

  • Bibliometrics, scientometrics, webometrics, and altmetrics
  • Computational Intelligence methods in Scientometric data fitting
  • Econometric Models in Scientometrics
  • Big data in Scientometrics
  • Machine Classification methods
  • Bayesian and Probabilistic models in Scientometrics
  • Machine Learning tools in Scientometric time series analysis
  • Interpolation methods for data fitting problems
  • Influence Modeling

The Issue will be online soon

Papers in this issue
Editor’s Note

Special Issue on Machine Learning in Scientometrics
Snehanshu Saha, Saibal Kar

Machine Learning
On the Implications of Artificial Intelligence and its Responsible Growth
Harsha Devaraj, Simran Makhija, Suryoday Basak

Analyzing the Common Wisdom of Binarization Doctrine in Internationality Classification of Journals: A Machine Learning Approach
Gambhire Swati Sampatrao, Sudeepa Roy Dey, Abhishek Bansal, Sriparna Saha

Relevance of Innovations in Machine Learning to Scientometrics
Gowri Srinivasa

SES-RREF: The Machine Learning Approach to Credible Metrics of Scholastic Evidence via Recursive Referencing
Archana Mathur, Snehanshu Saha, Saibal Kar, Gouri Ginde, Ankit Sinha

Treatment Repurposing using Literature-Related Discovery
Ronald N. Kostoff

Some Salient Aspects of Machine Learning Research: A Bibliometric Analysis
Sujit Bhattacharya

Editors-Special Issue:

Snehanshu Saha, PES University South Campus, Bangalore


Saibal Kar, Centre for Studies in Social Sciences, Calcutta

Associate Editor-Special Issue:

Archana Mathur, Indian Statistical institute, Bangalore


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