JSCIRES 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welcome original and unpublished contributions (adhering to the journal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
IMPORTANT DATES:
- Paper Submissions: June 05, 2018
- Acceptance Notification: September 05, 2018
- Revised Submission: October 15, 2018
- Final Acceptance Notification: November 15, 2018
- Camera Ready Submission: November 30, 2018
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
Click to see Profile of Editors of Special Issue
*Further Details about the Issue
| *Journal
of Scientometric Research
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