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http://hdl.handle.net/10609/147218
Title: | Análisis de la aplicación de machine learning en sistemas de defensa |
Author: | Alcántara Suárez, Evaldo Jorge |
Tutor: | Monzon Baeza, Victor |
Abstract: | Machine learning, according to the research and analysis of the literature, is already applied, among others, in sectors such as: healthcare, for image diagnostics; entertainment, Netflix algorithm to recommend content to customers; and the home, with Alexa or Siri robots and their voice recognition. As a result, it is clearly important to analyze its use and application in military systems because, in the not too distant future, this technology will make a difference on the battlefield. Countries such as the United States and China are at the forefront in the development of military projects of this type and are investing heavily in R&D and innovation. On the other hand, the European Union and Spain finance little research, although it is gradually increasing. In this TFM is analyzed, as an example, the development of some of these projects that are being funded and the evolution of several military systems such as radars, firing directions, unmanned vehicles and surveillance systems, to subsequently identify the added value provided by the application of this technology in these systems and the possible uses in tactical operations. Finally, after an extensive review of references related to defense and ML, the results obtained from the analysis are presented, highlighting the scarcity of articles on the subject, a series of challenges to overcome, a guide to the application of ML for new defense projects and the economic, legal and ethical impact of this tool. |
Keywords: | machine learning defence militaries tactical environments |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 9-Jan-2023 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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ealcsuaTFM0123memoria.pdf | Memoria del TFM | 2,2 MB | Adobe PDF | View/Open |
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