JSCIRES Special issue on Machine Learning
<br>
<br>Machine learning, a scientific discipline deals with developing systems
<br>which can learn from data and can make decisions by using the knowledge
<br>derived from the data. The discipline has been an important pillar of
<br>Artificial Intelligence, and has earned considerable attention from
<br>researchers worldwide because of its ability to extract knowledge from raw
<br>data by using sound statistical principles.
<br>
<br>Scientometrics is a domain that performs a quantitative and qualitative
<br>assessment of research and scientific progress. The field has earned
<br>popularity in last few years owing to the need to measure research outputs
<br>at individual, institutional and geographical levels. As a result of this
<br>need, different parameters are brought-up and various databases like
<br>Scopus, Web of Science and Google scholar are built for computation of
<br>these parameters. The data generated and stored as a result of
<br>proliferation of research papers and other scientific activities is vast.
<br>Analysis of the data cannot be performed without the intervention of
<br>sophisticated tools and techniques. Consequently, the use of Machine
<br>leaning algorithms for carrying out tasks like classification, regression,
<br>clustering and associations on these databases becomes imminent. The
<br>indicators to mark research performance use citation information in a
<br>well-defined way. Citations have become a key component in evaluating
<br>performance for authors, articles and journals. To evaluate the role of
<br>Machine Learning in Scientometrics, ML techniques can help in predicting
<br>citation count, can provide useful insights on computing new bibliometric
<br>indexes and also, in finding associations among them. The usage of powerful
<br>statistical tools like multiple linear regression, convex/concave
<br>optimization and gradient ascent/descent algorithms can be explored in
<br>scientometric and bibliographic analysis.
<br>
<br>The special issue aims to capture the baseline, set the tempo for future
<br>research in India and abroad and prepare a scholastic primer that would
<br>serve as a standard document for future research. we hope to learn about
<br>methods that are applicable to Scientometrics but are not currently used,
<br>and also making Computer Science practitioners aware of the interesting
<br>problems that complex Scientometric/ Bibliometric data sets provide. We
<br>welcome original and unpublished contributions (adhering to the journal
<br>format) that discuss new developments in efficient models for complex
<br>computer experiments and data analytic techniques which can be used in
<br>Scientometric data analysis as well as related branches in physical,
<br>statistical and computational sciences.
<br>
<br>*Topics: Specific topics of interest include, but are not limited to:*
<br>
<br> - Bibliometrics, scientometrics, webometrics, and altmetrics
<br> - Computational Intelligence methods in Scientometric data fitting
<br> - Econometric Models in Scientometrics
<br> - Big data in Scientometrics
<br> - Machine Classification methods
<br> - Bayesian and Probabilistic models in Scientometrics
<br> - Machine Learning tools in Scientometric time series analysis
<br> - Interpolation methods for data fitting problems
<br> - Influence Modeling
<br>
<br>*IMPORTANT DATES:*
<br>
<br> - Paper Submissions: June 05, 2018
<br> - Acceptance Notification: September 05, 2018
<br> - Revised Submission: October 15, 2018
<br> - Final Acceptance Notification: November 15, 2018
<br> - Camera Ready Submission: November 30, 2018
<br>
<br>*Editors-Special Issue:*
<br>
<br>Snehanshu Saha, PES University South Campus, Bangalore
<br>
<br>Saibal Kar, Centre for Studies in Social Sciences, Calcutta
<br>
<br>*Associate Editor-Special Issue:*
<br>
<br>Archana Mathur, Indian Statistical institute, Bangalore
<br>
<br>*Click to see Profile of Editors of Special Issue
<br>*
<br>
<br>*Further Details about the Issue
<br> | **Journal
<br>of Scientometric Research *
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