A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities
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 s...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1479855/full |
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| author | José Luis Uc Castillo Ana Elizabeth Marín Celestino Diego Armando Martínez Cruz José Tuxpan Vargas José Alfredo Ramos Leal Janete Morán Ramírez |
| author_facet | José Luis Uc Castillo Ana Elizabeth Marín Celestino Diego Armando Martínez Cruz José Tuxpan Vargas José Alfredo Ramos Leal Janete Morán Ramírez |
| author_sort | José Luis Uc Castillo |
| collection | DOAJ |
| description | 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 15 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. |
| format | Article |
| id | doaj-art-efb5fa7a4428445899a796a515ad755d |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-efb5fa7a4428445899a796a515ad755d2025-01-07T06:40:21ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.14798551479855A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunitiesJosé Luis Uc Castillo0Ana Elizabeth Marín Celestino1Diego Armando Martínez Cruz2José Tuxpan Vargas3José Alfredo Ramos Leal4Janete Morán Ramírez5Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, MexicoCONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, MexicoCONAHCYT-Centro de Investigación en Materiales Avanzados, Durango, MexicoCONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, MexicoInstituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, MexicoCONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, MexicoThis 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 15 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.https://www.frontiersin.org/articles/10.3389/frai.2024.1479855/fullartificial intelligencedata scienceDeep LearningMachine LearningMexicostate-of-the-art |
| spellingShingle | José Luis Uc Castillo Ana Elizabeth Marín Celestino Diego Armando Martínez Cruz José Tuxpan Vargas José Alfredo Ramos Leal Janete Morán Ramírez A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities Frontiers in Artificial Intelligence artificial intelligence data science Deep Learning Machine Learning Mexico state-of-the-art |
| title | A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities |
| title_full | A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities |
| title_fullStr | A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities |
| title_full_unstemmed | A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities |
| title_short | A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities |
| title_sort | systematic review of machine learning and deep learning approaches in mexico challenges and opportunities |
| topic | artificial intelligence data science Deep Learning Machine Learning Mexico state-of-the-art |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1479855/full |
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