IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1397-d799446.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Steven Morse & Kevin Talty & Patrick Kuiper & Michael Scioletti & Steven B Heymsfield & Richard L Atkinson & Diana M Thomas, 2020. "Machine learning prediction of combat basic training injury from 3D body shape images," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-12, June.
    3. Svajone Bekesiene & Rasa Smaliukiene & Ramute Vaicaitiene, 2021. "Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts," Mathematics, MDPI, vol. 9(6), pages 1-23, March.
    4. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    5. M.J. Cobo & A.G. López-Herrera & E. Herrera-Viedma & F. Herrera, 2012. "SciMAT: A new science mapping analysis software tool," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(8), pages 1609-1630, August.
    6. Carlos de las Heras-Rosas & Juan Herrera, 2021. "Innovation and Competitive Intelligence in Business. A Bibliometric Analysis," IJFS, MDPI, vol. 9(2), pages 1-18, June.
    7. Noelia Cámara & David Tuesta, 2018. "Measuring financial inclusion: a multidimensional index," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The role of data in supporting financial inclusion policy, volume 47, Bank for International Settlements.
    8. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    9. de Neufville, Robert & Baum, Seth D., 2021. "Collective action on artificial intelligence: A primer and review," Technology in Society, Elsevier, vol. 66(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mario A. Morales-Parragué & Rodrigo A. Varela-Laso & Luis Araya-Castillo & Fidel Molina-Luque, 2023. "Corporate Social Responsibility: Where Does It Come from, and Where Does It Go? Evolution of the Conceptual Structure from 1975 to 2021," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    2. Gessler, Michael & Bohlinger, Sandra & Zlatkin-Troitschanskaia, Olga, 2021. "International vocational education and training research: An introduction to the special issue," International Journal for Research in Vocational Education and Training (IJRVET), European Research Network in Vocational Education and Training (VETNET), European Educational Research Association, vol. 8(4), pages 1-15.
    3. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    4. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    5. Tuba Bircan & Almila Alkim Akdag Salah, 2022. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    6. Kangyin Dong & Yalin Han & Yue Dou & Muhammad Shahbaz, 2022. "Moving toward carbon neutrality: Assessing natural gas import security and its impact on CO2 emissions," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(4), pages 751-770, August.
    7. Livio Cricelli & Michele Grimaldi & Silvia Vermicelli, 2022. "Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda," Review of Managerial Science, Springer, vol. 16(5), pages 1269-1310, July.
    8. Zoltán Lakner & Brigitta Plasek & Gyula Kasza & Anna Kiss & Sándor Soós & Ágoston Temesi, 2021. "Towards Understanding the Food Consumer Behavior–Food Safety–Sustainability Triangle: A Bibliometric Approach," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    9. Gallego-Losada, María-Jesús & Montero-Navarro, Antonio & García-Abajo, Elisa & Gallego-Losada, Rocío, 2023. "Digital financial inclusion. Visualizing the academic literature," Research in International Business and Finance, Elsevier, vol. 64(C).
    10. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    11. Zhichao Wang & Valentin Zelenyuk, 2021. "Performance Analysis of Hospitals in Australia and its Peers: A Systematic Review," CEPA Working Papers Series WP012021, School of Economics, University of Queensland, Australia.
    12. Dyna Heng & Anna Ivanova & Rodrigo Mariscal & Ms. Uma Ramakrishnan & Joyce Wong, 2016. "Advancing Financial Development in Latin America and the Caribbean," IMF Working Papers 2016/081, International Monetary Fund.
    13. Mikel Alayo & Txomin Iturralde & Amaia Maseda & Gloria Aparicio, 2021. "Mapping family firm internationalization research: bibliometric and literature review," Review of Managerial Science, Springer, vol. 15(6), pages 1517-1560, August.
    14. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    15. Zhichao Wang & Bao Hoang Nguyen & Valentin Zelenyuk, 2024. "Performance analysis of hospitals in Australia and its peers: a systematic and critical review," Journal of Productivity Analysis, Springer, vol. 62(2), pages 139-173, October.
    16. Shahid Manzoor Shah & Amjad Ali, 2023. "Macro Dimensions of Financial Inclusion Index and its Status in Developing Countries," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(1), pages 1-12, March.
    17. Yadav, Vishal & Singh, Shishir Kumar & Velan, Nirmala & Aftab, Md Asif, 2020. "Impact Assessment of Pradhan Mantri Jan-Dhan Yojana in Augmenting Financial Inclusion in India - A District-Level Analysis," MPRA Paper 105064, University Library of Munich, Germany.
    18. Raquel Ruiz-Rodríguez & Marta Ortiz-de-Urbina-Criado & Rafael Ravina-Ripoll, 2023. "Neuroleadership: a new way for happiness management," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    19. Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    20. Priya Shah & Richa Singh Dubey & Shashikant Rai & Douglas W. S. Renwick & Saurabh Misra, 2024. "Green human resource management: A comprehensive investigation using bibliometric analysis," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 31-53, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1397-:d:799446. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.