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Item type:Publication, Thematic mapping of artificial intelligence in management: A bibliometric approach using co-word analysis (2015–2024)(Pro-Metrics, 2025); Objective: The objective of this study was twofold: first, to map the main themes in the literature on artificial intelligence in management, and second, to explore the relationships between these themes. Design/Methodology/Approach: A co-word analysis was performed on 15,835 articles indexed in Scopus (2015–2024), with the author’s keywords in the field of administration constituting the unit of analysis. The semantic network under consideration was constructed using the 50 most frequent terms, applying normalization by association and the Walktrap algorithm for cluster detection. Results/Discussion: The results of the analysis indicated that the extant literature was organized around three thematic groups. The first of these focused on conversational interfaces, the second on digital transformation, and the third adopted a computational approach. The thematic structure identified reflected a field in the process of consolidation, with a predominance of technical approaches and limited functional specialization. Conclusion: Contemporary research endeavors prioritized methodological development over strategic implementation in particular organizational contexts. These findings underscored the necessity for more comprehensive approaches that articulated technology, management, and governance. Moreover, they called for a future agenda that was oriented toward its adoption from sociotechnical perspectives. ©The authors ©Iberoamerican Journal of Science Measurement and Communication (Revista Iberoamericana de Medición y Comunicación de la Ciencia) ©Pro Metrics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics(MDPI, 2025); Antunes, AgostinhoOvercoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as corals, sea anemones, and jellyfish, are a promising valuable resource of these bioactive peptides due to their robust innate immune systems yet are still poorly explored. Hence, we employed an in silico proteolysis strategy to search for novel AMPs from omics data of 111 Cnidaria species. Millions of peptides were retrieved and screened using shallow- and deep-learning models, prioritizing AMPs with a reduced toxicity and with a structural distinctiveness from characterized AMPs. After complex network analysis, a final dataset of 3130 Cnidaria singular non-haemolytic and non-toxic AMPs were identified. Such unique AMPs were mined for their putative antibacterial activity, revealing 20 favourable candidates for in vitro testing against important ESKAPEE pathogens, offering potential new avenues for antibiotic development. ©The authors. ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Determining the medical Spanish translation capabilities of three artificial intelligence translation models for Mohs micrographic surgical instructions(Elsevier Inc., 2024)5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using Social Robotics to Identify Educational Behavior: A Survey(MDPI, 2024)8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preface : Advances in Soft Computing : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part II(Springer, 2024-01-01); The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI community. This year, MICAI 2023 was graciously hosted by the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) and the Universidad Autónoma del Estado de Yucatán (UAEY). The conference presented a cornucopia of scientific endeavors. ©Springer.14 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence and Its Application in the Study of the Legal Complexity of the Value Added Tax Act in Mexico(2022); Carriles Álvarez, AlonsoThe text is a raw material, researchers need to extract information and patterns of value. Through the use of AI tools in conjunction with the hard sciences, it is now possible to access significant sources of knowledge that previously remained hidden in the form of patterns of ideas and feelings stored in large volumes of text. The analysis of the raw text of the Law of Value-Added Tax (VAT) considered the three elements: structure, language, and interdependence. With these three elements, a legal complexity index was constructed, and the results of the model’s parameters show the following: the value for the legal complexity variable was negative (−1.39), which means that when the legal complexity index per unit increases, tax collection will decrease 1.39%. It is helpful to remember that interdependence is the component that outweighs the rest within the legal complexity index. The GDP estimator showed a positive sign, and its magnitude was 4.51; this means that when this estimator increases 1%, VAT collection could increase a 4.5%. © 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.24 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Feature Selection Methods Evaluation for CTR Estimation(2016); The most widespread payment model in online advertising is Cost-per-click (CPC). In this model the advertisers pay each time that a user generates a click. In order to enhance the income of CPC Advertising Networks, it is necessary to give priority to the most profitable adverts. The most important factor in the profitability of an advert is Click-through-rate (CTR), which is the probability that a user generates a click in a given advert. In this paper we find which feature selection method between PCA, RFE, Gain ratio and NSGA-II is better suited, or if otherwise, the machine learning classification methods work best without any feature selection method. ©2016 IEEScopus© Citations 1 19 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A New method to optimize BGP routes using SDN and reducing latency(2018)Scopus© Citations 5 23 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Versatility of Artificial Hydrocarbon Networks for Supervised Learning(2018); Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied. © Springer Nature Switzerland AG 2018.Scopus© Citations 1 13 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design and Equilibrium Control of a Force-Balanced One-Leg Mechanism(2018); The problem of equilibrium is critical for planning, control, and analysis of legged robot. Control algorithms for legged robots use the equilibrium criteria to avoid falls. The computational efficiency of the equilibrium tests is critical. To comply with this it is necessary to calculate the horizontal momentum rotation for every moment. For arbitrary contact geometries, more complex and computationally-expensive techniques are required. On the other hand designing equilibrium controllers for legged robots is a challenging problem. Nonlinear or more complex control systems have to be designed, complicating the computational cost and demanding robust actuators. In this paper, we propose a force-balanced mechanism as a building element for the synthesis of legged robots that can be easily balance controlled. The mechanism has two degrees of freedom, in opposition to the more traditional one degree of freedom linkages generally used as legs in robotics. This facilitates the efficient use of the “projection of the center of mass” criterion with the aid of a counter rotating inertia, reducing the number of calculations required by the control algorithm. Different experiments to balance the mechanism and to track unstable set-point positions have been done. Proportional error controllers with different strategies as well as learning approaches, based on an artificial intelligence method namely artificial hydrocarbon networks, have been used. Dynamic simulations results are reported. Videos of experiments will be available at: https://sites.google.com/up.edu.mx/smart-robotic-legs/. © 2018, Springer Nature Switzerland AG.Scopus© Citations 1 8 2