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
38 results
Now showing 1 - 10 of 38
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Item type:Publication, Branding interno en una empresa de servicios en MéxicoEl objetivo de la presente investigación es analizar los procesos de branding interno de una empresa de servicios del conocimiento en México, de manera que estos permitan fortalecer sus ventajas competitivas a través del liderazgo de su capital humano. Lo anterior se logra a través de dilucidar si la capacitación y motivación hacia la marca influyen positivamente en el desempeño de esta. La estrategia metodológica es de tipo cuantitativo a través de herramientas estadísticas y de machine learning. La evidencia muestra que la alineación por parte de los empleados con los valores de la marca muestra un impacto positivo y significativo con su motivación a favor de la marca y el desempeño de esta. Los factores más significativos dentro de la dimensión de compromiso es el binomio capacitación y liderazgo de los jefes.1 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Knowledge Management and Innovation in the Furniture Industry in Mexico(Springer, 2023); Knowledge is an asset for all companies. Consequently, learning and accumulating new knowledge over time constitute the essence of the innovation process. Thus, knowledge management is of the utmost importance. This research aims to analyze and propose a knowledge management model for innovation through Bayesian networks with machine learning techniques in the furniture industry. We develop a model where we identify and quantify the impact of critical factors on the generation of innovative value. The results show that the most relevant factors for optimal knowledge management in the furniture industry are suppliers, distributors, the enterprises’ business model structure, quality and risk management, strategic planning, value system model, national and international markets, e-commerce, and human capital. ©Springer ©The authors.5 - 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, Metodología para determinar los factores de riesgo asociados con enfermedades complejas: Degeneración Macular Relacionada con la Edad y Preeclampsia(2021); 7 21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Conceptual Framework for Digital Transformation of Business Models: Advancing Towards Industry 5.0(Springer Nature Switzerland, 2026); ; Hernández-Lara, Ana BeatrizDigital transformation is progressing unevenly across industries, with varying levels of success influenced by organizational and sector-specific factors. Understanding where to focus investments and what type of transformation to adopt has become a crucial challenge for companies seeking competitiveness and market relevance in the digital era. This paper aims to analyze companies’ strategic decision making to foster digital transformation, conducting a literature review, and proposing a conceptual framework for digital transformation of business models. The study identifies key drivers of successful digital transformation, including digital strategy, human capital, scalability, customer focus, security and risk management. Integrating these factors, the proposed model emphasizes the strategic alignment of digital initiatives with organizational goals, fostering a culture of continuous innovation and adaptability. The findings contribute to a deeper understanding of the mechanisms and prerequisites for effective digital transformation, offering insights for organizations navigating the shift toward Industry 5.0. ©The authors ©Springer.58 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Most Relevant Factors in the Gender Gap in European Countries(Academic Conferences International Ltd, 2025); ; Gender equality is essential for the sustainable development of all countries. It brings economic growth, improved education and health for the entire population, poverty reduction, and social and political stability as democracy is strengthened and more peaceful communities are generated. However, its study is complex and includes various dimensions. This research aims to analyze the most relevant factors of the gender gap in European countries. The methodological strategy is based on machine learning techniques applied to the Gender Equality Index, which includes the EU27 countries and was developed by EIGE. These machine-learning techniques are methods computers use to learn from data and make predictions without being explicitly programmed. This index has 31 relevant indicators that are grouped into 14 subdimensions, which are, in turn, divided into six dimensions. The relevant dimensions in the study of gender equality are I. work (5 indicators), II. Money (4 indicators), III. Knowledge (3 indicators), IV. Time (4 indicators), V. power (8 indicators), and VI. Health (7 indicators). The results show a women's gap. Three of the most relevant dimensions from this research inhibit gender equity: I. Power in its three economic, political, and social dimensions; II. Knowledge in its two dimensions of attainment, participation, and segregation, and III. Time in its dimension of social activities. Women's most significant factors for the gender gap are power, knowledge, and time. ©The authors ©International Conference on Gender Research.32 100 - 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia(IEEE, 2024); ; Miralles-Pechuán, LuisThe use of machine learning in healthcare has transformed the way diseases are diagnosed and treatments are optimized. However, medical databases often lack balanced data due to challenges in data collection caused by privacy regulations. Certain health conditions are underrepresented, which hampers machine learning performance. To address this problem, a hybrid approach has been proposed that combines the Synthetic Minority Oversampling Technique (SMOTE) with undersampling and uses two specific techniques tailored for imbalanced datasets. Comparative evaluations were conducted using various thresholds to reduce one class and employing Balanced Accuracy to mitigate bias toward the majority class, with popular machine learning methods. The results showed that Balanced Bagging and Balanced Random Forest consistently outperformed other methods, performing the best with an average ranking of 1.42 and 3.58 out of 32 configurations in the two datasets, respectively. Tree-based approaches such as Random Forest and Gradient Boosting demonstrated similar effectiveness, emphasizing the power of aggregating predictions from multiple trees to reduce bias. Notably, undersampling and SMOTE proved advantageous for non-tree-based models like KNN, SVM, and Logistic Regression showcasing their usefulness across different algorithms. This study provides a robust solution for handling imbalanced datasets in healthcare, which could potentially optimize healthcare interventions and improve patient outcomes and care©IEEE Latin America Transactions, The authorsScopus© Citations 1 12 - 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, 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