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Stress and Machine Learning-Future with Possibilities: A Bibliometric Approach

1★, 1, 2★ Corresponding author

  1. 1School of Computing, DIT University, Dehradun, Uttarakhand, INDIA.
  2. 2Devbhoomi Uttarakhand University, Uttarakhand, INDIA.

CORRESPONDENCE

Pooja Gupta

School of Computing, DIT University, Dehradun, Uttarakhand, INDIA.

pooja.gupta@dituniversity.edu.in

Received: 14-09-2021; Revised: 15-11-2021; Accepted: 16-01-2022.

Volume 11, Issue 1 · pp. 37-46 · 2022 · DOI: 10.5530/jscires.11.1.4

ABSTRACT

Stress in human life is a global health concern and machine learning based models have been applied extensively for the stress prediction. This work is an attempt to present a bibliometric analysis in the field of stress prediction using machine learning. The dataset to conduct this study was taken from the Web of Science database and research papers were selected from the year 2005 to 2021. Then, bibliometric analysis tool, VOS Viewer 1.6.14 was applied for generating a co-authorship network map, inter-country coauthorship network map, and keywords co-occurrences network maps. The outcomes of this study visually highlight the important research details like the most prolific journal, most cited paper, most prolific country, institution and interesting research driving points in the stress prediction using machine learning. This study attempts to portray the existing literature on stress prediction using machine learning more comprehensively and systematically by showing research collaboration among countries, authors, co-citations analysis and, bibliographic coupling. The findings of this study can be useful to conduct future research on a similar area.

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    Gupta, P., Maji, S., & Mehra, R. (2022). Stress and Machine Learning-Future with Possibilities: A Bibliometric Approach. Journal of Scientometric Research, 11(1), 37-46. https://doi.org/10.5530/jscires.11.1.4

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    © 2022 Author(s)

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. CC BY-NC-SA 4.0