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REVIEW article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1479855
This article is part of the Research Topic Learning Efficiently from Limited Resources in the Practical World View all articles

A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities

Provisionally accepted
José Luis Uc Castillo José Luis Uc Castillo 1Ana Elizabeth Marín Celestino Ana Elizabeth Marín Celestino 1*Diego Armando Martínez Cruz Diego Armando Martínez Cruz 2José Tuxpan Vargas José Tuxpan Vargas 1José Alfredo Ramos Leal José Alfredo Ramos Leal 1Janete Morán Ramírez Janete Morán Ramírez 1
  • 1 División de Geociencias Aplicadas. Camino a la Presa San José 2055, Col. Lomas 4ta Sección. CP. 78216, Instituto Potosino de Investigación Científica y Tecnológica (IPICYT), San Luis Potosí, Mexico
  • 2 Unidad Durango. CP. 34147., Centro de Investigación de Materiales Avanzados (CIMAV), Chihuahua, Chihuahua, Mexico

The final, formatted version of the article will be published soon.

    This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico. A total of 120 original research papers were included and details such as trends in publication, spatial location, institutions, publishing issues, subject areas, algorithms applied, and performance metrics were discussed. Furthermore, future directions and opportunities are presented. A total of fifteen subject areas were identified, where Social Sciences and Medicine were the main application areas. It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. Regarding the performance metrics applied, accuracy and recall were the most employed. This paper could assist the readers in understanding the several Machine Learning and Deep Learning techniques used and their subject area of application in the Artificial Intelligence field in the country. Moreover, the study could provide significant knowledge in the development and implementation of a national AI strategy, according to country needs.

    Keywords: artificial intelligence, data science, deep learning, machine learning, Mexico, State-of-the-art

    Received: 12 Aug 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Uc Castillo, Marín Celestino, Martínez Cruz, Tuxpan Vargas, Ramos Leal and Morán Ramírez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ana Elizabeth Marín Celestino, División de Geociencias Aplicadas. Camino a la Presa San José 2055, Col. Lomas 4ta Sección. CP. 78216, Instituto Potosino de Investigación Científica y Tecnológica (IPICYT), San Luis Potosí, Mexico

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.