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Military Applications of Machine Learning: A Bibliometric Perspective

Author

Listed:
  • José Javier Galán

    (Faculty of Statistics, Complutense University, Puerta de Hierro, 3728040 Madrid, Spain)

  • Ramón Alberto Carrasco

    (Department of Management and Marketing, Faculty of Commerce and Tourism Complutense, University of Madrid, 28223 Madrid, Spain)

  • Antonio LaTorre

    (Center for Computational Simulation (CCS), Universidad Politécnica de Madrid, 28660 Madrid, Spain)

Abstract

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

Suggested Citation

  • José Javier Galán & Ramón Alberto Carrasco & Antonio LaTorre, 2022. "Military Applications of Machine Learning: A Bibliometric Perspective," Mathematics, MDPI, vol. 10(9), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1397-:d:799446
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    References listed on IDEAS

    as
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    Cited by:

    1. Juan Vidal & Ramón A. Carrasco & Manuel J. Cobo & María F. Blasco, 2024. "Data Sources as a Driver for Market-Oriented Tourism Organizations: a Bibliometric Perspective," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 7588-7621, June.

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