[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122155-en":3,"doc-seo-122155-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122155,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities","A systematic review synthesizes state-of-the-art Artificial Intelligence models—especially Machine Learning (ML) and Deep Learning (DL)—and their real-world development and applications in Mexico across diverse domains. The review consolidates 120 original research papers, analyzing publication trends, spatial distribution, institutions, subject areas, algorithms, and evaluation performance metrics. The study identifies 15 subject areas, with Social Sciences and Medicine as main application areas, and finds Artificial Neural Networks most frequently preferred, with Random Forest and Support Vector Machines also prominent.","TYPE Review  \nPUBLISHED 07 January 2025 DOI 10.3389/frai.2024.1479855  \nOPEN ACCESS  \nEDITED BY  \nJinyang Guo,  \nBeihang University, China  \nREVIEWED BY  \nYuqing Ma,  \nBeihang University, China Yejun Zeng,  \nBeihang University, China, in collaboration with reviewer YM  \nRui Su,  \nShanghai AI Lab, China  \n*CORRESPONDENCE  \nAna Elizabeth Marín Celestino  \n [ana.marin@ipicyt.edu. mx](ana.marin@ipicyt.edu. mx)  \nRECEIVED 12 August 2024  \nACCEPTED 16 December 2024  \nPUBLISHED 07 January 2025  \nCITATION  \nUc Castillo JL, Marín Celestino AE, Martínez Cruz DA, Tuxpan Vargas J, Ramos Leal JA and Morán Ramírez J (2025) A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.  \nFront. Artif. Intell. 7:1479855 .  \ndoi: 10.3389/frai.2024.1479855  \nCOPYRIGHT  \n© 2025 Uc Castillo, Marín Celestino, Martínez Cruz, Tuxpan Vargas,  \nRamos 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) and the copyright owner(s) 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.  \nA systematic review of Machine Learning and Deep Learning approaches in Mexico: challengesand opportunities  \nJosé Luis Uc Castillo 1, Ana Elizabeth Marín Celestino 2*, Diego Armando Martínez Cruz3, José Tuxpan Vargas 2, José Alfredo Ramos Leal 1 and Janete Morán Ramírez 2  \n1 Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, Mexico, 2CONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, Mexico, 3CONAHCYT-Centro de Investigación en Materiales Avanzados, Durango, Mexico  \nThis 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 directionsand 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.  \nKEYWORDS  \nartificial intelligence, data science, Deep Learning, Machine Learning, Mexico, state-of-the-art  \n1 Introduction  \nHuge amounts of data are produced every day and extracting its information is essential to predict, interpret a","cbCaijRk1lb9t9G6","https://ap.wps.com/l/cbCaijRk1lb9t9G6","pdf",2619381,1,15,"English","en",105,"# Introduction\n## Data growth and the need for advanced analysis\n## The rise of Artificial Intelligence and industrial context\n## ML and DL as core AI toolsets\n## Key events and common algorithms","[{\"question\":\"How many original research papers are included in the review?\",\"answer\":\"The review includes a total of 120 original research papers covering ML and DL in Mexico.\"},{\"question\":\"Which application areas are identified as the main domains?\",\"answer\":\"Fifteen subject areas are identified, with Social Sciences and Medicine highlighted as the main application areas.\"},{\"question\":\"Which algorithms and performance metrics are most frequently used?\",\"answer\":\"Artificial Neural Networks are most preferred, and accuracy and recall are the most employed performance metrics.\"}]","A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities | 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