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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Veröffentlicht in:Frontiers in artificial intelligence Jg. 7; S. 1479855
Hauptverfasser: Uc Castillo, José Luis, Marín Celestino, Ana Elizabeth, Martínez Cruz, Diego Armando, Tuxpan Vargas, José, Ramos Leal, José Alfredo, Morán Ramírez, Janete
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 07.01.2025
Schlagworte:
Artificial Intelligence
data science
Deep Learning
Machine Learning
Mexico
state-of-the-art
Mexico
state-of-the-art
data science
Machine Learning
Deep Learning
artificial intelligence
ISSN:2624-8212, 2624-8212
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
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Yejun Zeng, Beihang University, China, in collaboration with reviewer YM
Rui Su, Shanghai AI Lab, China
Edited by: Jinyang Guo, Beihang University, China
Reviewed by: Yuqing Ma, Beihang University, China
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2024.1479855