Rafael Rivera-López

Los asaltos a casa habitación representan una problemática constante en diversas regiones de México, especialmente en comunidades vecinales que carecen de sistemas de vigilancia o canales efectivos de comunicación entre residentes. En respuesta a esta situación, se diseña una aplicación móvil orientada a la prevención del delito y al fortalecimiento del tejido comunitario. Esta solución empodera a los usuarios mediante la creación de una red de apoyo vecinal que permite una comunicación ágil y efectiva. El prototipo integra funcionalidades como un sistema de alarma en tiempo real, la generación de alertas geolocalizadas y la posibilidad de reportar actividades sospechosas, fomentando la colaboración entre los habitantes. Asimismo, contempla el uso de tecnologías móviles y servicios en la nube, con el propósito de contribuir a la seguridad ciudadana y a la construcción de comunidades más resilientes. El prototipo integra un diseño centrado en el usuario, basado en la metodología UX propuesta por Google, la cual se enfoca en comprender las necesidades reales del usuario mediante procesos de investigación, ideación, prototipado y pruebas interactivas, permitiendo que la experiencia del usuario sea positiva.

The literature has identified that the length of the article is related to the number of citations. This paper presents a correlation analysis of the moderator effect of the number of accesses to an article on the relationship between the article length and its citations using three regression techniques. This analysis concerns the manuscripts published in the Journal of International Business Studies between 1970 and 2021. For this analysis, we use a classic linear regression model and two machine learning regression techniques: model trees and artificial neural networks. The main results show moderation of the article’s accessibility in the relationship between article length and citations. Also, it affects the citation rate, but article length does not have a significant relationship with this rate. The conclusions show that analyzed hypotheses have a reduced correlation, but the regression method influences how the models are built in the statistical procedures. Specifically, artificial neural network models show better hypothesis-correlation than linear regression-based models.

The growing demand for complex machine learning models has increased the use of black-box models, such as random forests and artificial neural networks, posing significant challenges regarding explainability and interpretability. This manuscript addresses the critical problem of understanding and interpreting decisions from these opaque models, as a lack of interpretability can hinder their adoption in sensitive applications. To tackle this issue, we propose an evolutionary approach to induce univariate decision trees that accurately mimic the behavior of black-box models using tabular data. Our method employs two differential evolution algorithm variants, focusing on building univariate decision trees to enhance model explainability. Key contributions of this work include the development of a fitness function that balances accuracy with tree compactness to reduce overfitting and improve explanability. Additionally, we introduce a novel selection scheme that evaluates candidate solutions using synthetic instances, further enhancing the robustness against variance of the decision trees. Experimental results demonstrate that the proposed approach yields more precise and compact decision trees than traditional methods, significantly improving the explainability of complex machine learning models.

This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in the literature. In particular, we evaluate the use of self-adaptive differential evolution variants to evolve a population of oblique decision trees encoded as real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes and (2) a fitness function that penalizes excessive leaf nodes, promoting smaller and more accurate decision trees. We perform a comparative performance analysis of these differential evolution variants, showing that while they exhibit similar statistical behavior, the Single-Objective real-parameter optimization (jSO) method produces the most accurate oblique decision trees and is second best in compactness. The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications.