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This study proposes an ML architectural model for a military organization while grounding it in a bibliometric analysis. Publications indexed in ISI WoS up to 2021 are analyzed using SciMat, Excel, and VOSviewer to build a strategic diagram. Results inform a conceptual ML architecture for practical military use and highlight key research areas and recent advances.","Article  \nMilitary Applications of Machine Learning: A Bibliometric Perspective  \nJosé Javier Galán 1,*, Ramón Alberto Carrasco 2 and Antonio LaTorre 3  \nCitation: Galán, J.J.;  \nCarrasco, R.A.; LaTorre, A. Military Applications of Machine Learning: A Bibliometric Perspective.  \nMathematics 2022, 10, x.  \n[https://doi.org/10.3390/xxxxx](https://doi.org/10.3390/xxxxx)  \nAcademic Editor: Alexander Ryzhov  \nReceived: 28 February 2022  \nAccepted: 19 April 2022  \nPublished: date  \nPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/li](https://creativecommons.org/li)censes/by/4.0/) .  \n1 Faculty of Statistics, Complutense University, Puerta de Hierro, 3728040 Madrid, Spain  \n2 Department of Management and Marketing, Faculty of Commerce and Tourism Complutense, University of Madrid, 28223 Madrid, Spain; [ramoncar@ucm.es](ramoncar@ucm.es)  \n3 Center for Computational Simulation (CCS), Universidad Politécnica de Madrid, 28660 Madrid, Spain; [a.latorre@upm.es](a.latorre@upm.es)  \n* Correspondence: josejgal@ucm.es  \nAbstract: 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, ISIWoS. 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.  \nKeywords: machine learning; military; artificial intelligence; bibliometric analysis  \nMSC: 68T01  \n1. Introduction  \nMachine learning (ML) allows the automation of many tasks by taking advantage of the large amount of information available from different sources, including big data applications. Its use is currently widely spread, and ML has become an important part of our daily lives [1] .  \nIn the military, the use of intelligent applications has also accelerated [2] . For example, the South Korean Ministry of National Defense has increased its information significantly, and with fewer and fewer intelligence analysts they need to apply artificial intelligence (AI) technology to process all the information in an accurate and timely manner [3]. Another example to note is the dependence on oil by military equipment and machinery. This is also where ML comes in, as military logistics must be intelligently based on informed deductions [4]; thus, we see how ML is integrated into the military world.  \nThe objective of this paper is to present an architectural model that ref","cbCaijOa12X4v86D","https://ap.wps.com/l/cbCaijOa12X4v86D","pdf",5391343,1,28,"English","en",105,"# Introduction\n## Related work and bibliometric scope\n## Research objective and architectural model overview\n# Bibliometric methodology\n## SciMat-based scientific mapping\n# Bibliometric results by analysis dimensions\n## Scientific areas, authors, citations, and countries\n## Time periods and publication trends\n# Military-focused conceptual architecture\n## Architecture redfined for military organizations\n# Conclusions","[{\"question\":\"What problem does machine learning address in the military context in this study?\",\"answer\":\"The work focuses on how ML processes the large volume of military data to learn, predict scenarios, and provide automatic decision support.\"},{\"question\":\"How is the bibliometric analysis conducted, and what tools are used?\",\"answer\":\"The analysis uses publications indexed in ISI WoS up to 2021, applying SciMat for bibliometric mapping, supported by Excel and VOSviewer to interpret relationships and construct a strategic diagram.\"},{\"question\":\"What kinds of insights does the strategic diagram provide?\",\"answer\":\"The strategic diagram is used to identify the main areas where ML is applied to the military field and to interpret the structure of research domains over time.\"}]","Military Applications of Machine Learning - 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