Martínez Velasco, Antonieta Teodora
Main Affiliation
Preferred name
Martínez Velasco, Antonieta Teodora
Official Name
Martínez Velasco, Antonieta Teodora
ORCID
0000-0001-6535-1440 
Researcher ID
DWK-4326-2022
Scopus Author ID
57192978286
31 results
Now showing 1 - 10 of 31
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Item type:Publication, An Explainable Tool to Support Age-related Macular Degeneration Diagnosis(2022); ; Artificial intelligence and deep learning, in particu-lar, have gained large attention in the ophthalmology community due to the possibility of processing large amounts of data and dig-itized ocular images. Intelligent systems are developed to support the diagnosis and treatment of a number of ophthalmic diseases such as age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity. Hence, explainability is necessary to gain trust and therefore the adoption of these critical decision support systems. Visual explanations have been proposed for AMD diagnosis only when optical coherence tomography (OCT) images are used, but interpretability using other inputs (i.e. data point-based features) for AMD diagnosis is rather limited. In this paper, we propose a practical tool to support AMD diagnosis based on Artificial Hydrocarbon Networks (AHN) with different kinds of input data such as demographic characteristics, features known as risk factors for AMD, and genetic variants obtained from DNA genotyping. The proposed explainer, namely eXplainable Artificial Hydrocarbon Networks (XAHN) is able to get global and local interpretations of the AHN model. An explainability assessment of the XAHN explainer was applied to clinicians for getting feedback from the tool. We consider the XAHN explainer tool will be beneficial to support expert clinicians in AMD diagnosis, especially where input data are not visual. © 2022 IEEE.1 21Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Entrepreneurship: Analysis by Country Through Machine Learning TechniquesThis research aims to analyze entrepreneurship worldwide through the dimensions and pillars of the entrepreneurship ecosystem of each country, identifying the contribution and patterns of behavior and correlation within the entrepreneurship ecosystem. This analysis intends to show the main actions that countries have carried out in support of entrepreneurship and entrepreneurs. The tool used to analyze is machine learning, where various algorithms are applied. The evidence shows that the most relevant pillars in the entrepreneurial ecosystem are I. Opportunity Startup, II. Technology Absorption, III. Risk Acceptance, IV. Risk Capital and V. Process Innovation. The pillars that best correlate are I. Competition and Opportunity Startup, II. Opportunity Startup, and Risk Acceptance, III. Opportunity Startup and Technology Absorption, IV. Cultural Support and Opportunity Startup, and V. Opportunity Startup and Risk Capital. The present work aims to provide knowledge to decision-makers in both the public and private sectors to channel public policies that support entrepreneurs in this time of crisis and promote the generation and strengthening of entrepreneurial activity. Although there are still no reliable GEI data for the years 2020 to 2022, the economic crisis generated by the stagnation in the development of the countries has reduced support for entrepreneurs, which in many cases can be a key factor for the rescue of the most disadvantaged countries.2 14Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transformación digital para la competitividad de las empresas(2024); <jats:p>La transformación digital es un proceso tanto tecnológico como sociocultural que involucra la adopción de tecnologías digitales y modificaciones en los modelos de negocio y la estrategia de las firmas. La literatura que estudia relaciones entre la transformación digital y la competitividad de las empresas se incrementó en el periodo de postpandemia, sin embargo, las investigaciones antes de la pandemia son escasas, por lo cual el objetivo de esta investigación es analizar el efecto de la transformación digital en la competitividad de las empresas antes de esta crisis. Los datos que se utilizaron para el análisis son de las Encuestas Nacionales sobre Productividad y Competitividad de las Pymes en Sectores Estratégicos en México. El análisis se realizó en dos partes: I. geoestadístico para buscar clústeres geográficos de corte natural y II. estadístico mediante la regresión Ridge. Los resultados evidenciaron que tanto la transformación digital como la competitividad se distribuyen de manera desigual en los territorios; sin embargo, se encontró un efecto positivo de la transformación digital en la competitividad de estas. De acuerdo con lo anterior, se concluyó que las empresas que quieran aumentar su competitividad deben incrementar su transformación digital, por lo que los gobiernos deben continuar estableciendo políticas y programas de transformación digital en todos los sectores y en forma más equitativa.</jats:p>40Scopus© Citations 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, University–Industry Collaboration: A Sustainable Technology Transfer ModelFaced with the pandemic caused by COVID-19, universities worldwide are giving a powerful response to support their communities. One way to provide support is via the collaboration between universities and industries, allowing the co-creation of knowledge that leads to innovation. Historically, universities, as knowledge-intensive organizations (KIOs), have produced knowledge through research. At present, its important contribution to countries’ economy is widely recognized through the development of new knowledge and technical know-how. Universities are a source of innovation for firms, which ultimately translates into social welfare improvements. The objective of this research is to analyze the university–firm linkage. The methodological strategy is carried out using Bayesian networks through a model where the main elements of university–industry linking, which impact competitiveness and innovation, are identified and quantified. The technology transfer model shows that the most crucial processes are Technology Strategy, Value Proposal, Knowledge Management, Control and Monitoring, Innovation Management, Needs Detection, Knowledge Creation, New Products and Services, and Absorption Capacity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1 18Scopus© Citations 29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Critical Factors in the Participation of Women in Science, Technology, Engineering, and Mathematics -STEM- Disciplines in Mexico(Springer, 2024-01-01); ; Currently, women participate in STEM areas, still with a very marked gender gap. Taking this as a reference, in this work, an investigation has been carried out based on questionnaires applied to students of STEM careers. The information obtained was analyzed using multi-criteria decision methods. In particular, the Order of Preference by Similarity to the Ideal Solution (TOPSIS) method was applied to determine the most favorable conditions for women to study a STEM career. Through this analysis, this research has found that women's choice of a STEM career is strongly influenced firstly by the father's profession, secondly by the mother's profession, and also has a positive impact on the discrimination to which the person has been subjected, self-motivation. And self-esteem. These results indicate that it is necessary to influence the early educational stages to provide support from the family and school environment to women so that they develop their skills around STEM careers. In future work, the data obtained could be analyzed in greater depth, considering that the richness of the open responses may be lost by coding the respondents’ opinions as categorical variables. ©Springer.22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Determining the Influence of Socioeconomic and Clinical Factors in Diabetes in the Mexican Population Using Machine Learning TechniquesDiabetes has a significant cost for health systems and the economy in general of countries, in addition to affecting the quality of life of people who suffer from it. Studying and analyzing the economic and clinical factors that trigger it allows us to identify the financial burden it represents and how policies and programs can be generated to support the prevention of this disease. This research aims to analyze the influence of socioeconomic and clinical factors on the Mexican population suffering from Diabetes. The analysis methods that are applied are the machine learning technique. The results in the Mexican population show that deaths from Diabetes occur more frequently in the population between 20 and 59 years of age. The factors related to housing-urbanization, specifically homes without piped water homes and houses with dirt floors, as well as people without the right to social security, are the critical factors that correlate with deaths caused by Diabetes: Hypertension, pneumonia, chronic respiratory diseases, coronary diseases, and influenza. ©The authors © Springer.24 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Explainable artificial hydrocarbon networks classifier applied to preeclampsiaExplainability is crucial in domains where system decisions have significant implications for human trust in black-box models. Lack of understanding regarding how these decisions are made hinders the adoption of so-called clinical decision support systems. While neural networks and deep learning methods exhibit impressive performance, they remain less explainable than white-box approaches. Artificial Hydrocarbon Networks (AHN) is an effective black-box model that can be used to support critical clinical decisions if accompanied by explainability mechanisms to instill confidence among clinicians. In this paper, we present a use case involving global and local explanations for AHN models, provided with an automatic procedure so-called eXplainable Artificial Hydrocarbon Networks (XAHN). We apply XAHN to preeclampsia prognosis, enabling interpretability within an accurate black-box model. Our approach involves training a suitable AHN model using the cross-validation with ten repetitions, followed by a comparative analysis against four well-known machine learning techniques. Notably, the AHN model outperformed the others, achieving an F1-score of 74.91%. Additionally, we assess the efficacy of our XAHN explainer through a survey applied to clinicians, evaluating the goodness and satisfaction of the provided explanations. To the best of our knowledge, this work represents one of the earliest attempts to address the explainability challenge in preeclampsia prediction.© 2024 The Author(s). Published by Elsevier Inc.14Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Purchase Intention for Biopreservative-Treated Chicken and Consumer Segmentation Using Unsupervised Machine Learning(MDPI AG, 2026); ; ; Biopreservatives are emerging as a promising alternative to conventional food preservation methods. However, there is limited empirical evidence integrating validated psychometric instruments with advanced unsupervised segmentation techniques to identify heterogeneous consumer profiles regarding biopreserved chicken consumption. To address these limitations, self-organizing maps (SOMs) have been widely used as unsupervised learning tools that reduce dimensionality, preserve topological relationships, and facilitate the visualization of latent structures in multivariate datasets. This study aims to analyze the factors influencing the acceptance of biopreservation in chicken consumption through a strategic segmentation based on unsupervised machine learning and behavioral dimensions. This methodological design enables the capture of behavioral heterogeneity, the identification of strategic consumption profiles, and the generation of empirical evidence to support future research and communication strategies related to biopreservation technologies in similar consumer contexts. In this study, 323 instances were topologically organized into 16 clusters, grouping individuals with similar behavioral patterns. The descriptive analysis of clusters revealed highly differentiated consumer profiles in terms of Consumer acceptance, cold chain practices, and Knowledge of food microorganisms. The high-acceptance exploratory segment achieved the maximum score (5.00) in knowledge of beneficial microorganisms (V14), acceptance of their use in chicken preservation (V15), and familiarity with lactic acid bacteria (V16). The three-dimensional structure identified through Exploratory Factor Analysis (EFA) not only provided statistical validation of the instrument but also established a conceptual framework for interpreting the SOM’s topology. From a practical perspective, the findings highlight the relevance of consumer heterogeneity and suggest that consumer knowledge may play an important role in the acceptance of biopreservation technologies. These findings provide useful insights for future studies aimed at understanding the factors that influence consumer acceptance of biopreservation technologies. © The authors © Applied Sciences © MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Balancing Work, Family, and Personal Life in the Mexican Context: The Future of Work for the “COVID-19 Generation”Intergenerational talent management—i.e., attracting and retaining employees across generations and with different motivations—is one of companies’ greatest challenges. The expectations that recent generations bring with them have pushed culture in the direction of work-family balance, which is now seen as a key tool for human resources departments in charge of creating support mechanisms to attract and retain the next generation of workers. This trend has been reinforced by the changes brought about in light of the COVID-19 pandemic. Responding to this shift, and inspired by the challenges that our “new normal” posits, this chapter presents research results from a survey conducted in Mexico with respondents from generations Y and Z. The survey results offer important insight into how these generations perceive work-life balance, as well as the expectations that young Mexicans between the ages of 18 and 30 hold in terms of family and work.1 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Physicochemical, Microbiological, Proximate, and Consumer Characterization of Traditional Tenate Cheese in Two Mexican Regions(MDPI AG, 2026); ; Tenate cheese is a traditional Mexican pressed semi-hard cheese made from raw cow’s milk and wrapped in palm fiber. The characterization of this cheese remains scarce. This study presents an exploratory characterization of a single production batch of traditional Tenate cheese obtained from one artisanal producer, providing preliminary information on its physicochemical, microbiological, and proximate analyses, combined with consumer evaluation. The latter was analyzed using hierarchical cluster analysis (HCA) as an exploratory segmentation tool. Tenate cheese was characterized as a semi-hard cheese with active lactic fermentation, a lactic aroma, acidic and umami flavors, and a firm, granular texture. Microbiological analyses showed the absence of coliforms, enterobacteria, and Staphylococcus aureus among the microorganisms evaluated, whereas yeast counts exceeded the regulatory limit. As major foodborne pathogens were not included in the microbiological assessment, the overall microbiological safety of the product could not be confirmed. A total of 318 consumers from Aguascalientes (AGS, n = 149) and the Guadalajara Metropolitan Area (GMA, n = 169) evaluated the product using hedonic and Just-About-Right scales. Consumers from AGS reported significantly higher liking scores than those from GMA. Penalty analysis identified insufficient softness as the main attribute associated with lower liking in AGS, whereas low flavor intensity and weak aftertaste reduced acceptance in GMA. Hierarchical cluster analysis (HCA) identified three consumer segments in each location, revealing distinct preference patterns linked to regional expectations. The main contributions of this study are threefold. First, it contributes to the limited scientific knowledge available on Tenate cheese by providing a comprehensive characterization of the analyzed sample. Second, it shows that consumer acceptance differed between two regional markets, comparing two university-affiliated consumer groups, highlighting the value of consumer segmentation for product positioning. Third, it proposes and applies an integrated framework combining physicochemical, microbiological, nutritional, sensory, and consumer segmentation analyses that can be applied to the study of other artisanal cheeses. © The authors © Applied Sciences © MDPI.