Compliance with the basic conditions of quality in higher education implies the design of strategies to reduce student dropout, and Information and Communication Technologies (ICT) in the educational field have allowed directing, reinforcing, and consolidating the process of professional academic training. We propose an academic and emotional tracking model that uses data mining and machine learning to group university students according to their level of dropout risk. We worked with 670 students from a Peruvian public university, applied 5 valid and reliable psychological assessment questionnaires to them using a chatbot-based system, and then classified them using 3 density-based unsupervised learning algorithms, DBSCAN, K-Means, and HDBSCAN. The results showed that HDBSCAN was the most robust option, obtaining better validity levels in two of the three internal indices evaluated, where the performance of the Silhouette index was 0.6823, the performance of the Davies–Bouldin index was 0.6563, and the performance of the Calinski–Harabasz index was 369.6459. The best number of clusters produced by the internal indices was five. For the validation of external indices, with answers from mental health professionals, we obtained a high level of precision in the F-measure: 90.9%, purity: 94.5%, V-measure: 86.9%, and ARI: 86.5%, and this indicates the robustness of the proposed model that allows us to categorize university students into five levels according to the risk of dropping out.

La accesibilidad web es una responsabilidad social y un derecho civil que se espera que todos los sitios web cumplan. Esta investigación propuso analizar las herramientas, niveles y pautas de evaluación de accesibilidad de los sitios web administrados por instituciones latinoamericanas. La metodología fue una revisión sistemática exploratoria que permitió seleccionar 43 trabajos académicos a partir de la búsqueda en las bases de datos de Scopus, IEEE Xplore y SciELO. Se reconoce que la mayor cantidad de sitios web evaluados son educativos y gubernamentales, también se identifica que la herramienta más utilizada para evaluar la accesibilidad es el Web Accessibility Evaluation Tool, y solo un sitio web cumplió el máximo nivel de accesibilidad web (nivel AAA) según las Directrices de Accesibilidad para el Contenido Web. Los resultados sugieren que se necesita mayor esfuerzo para promover y mejorar la accesibilidad de los sitios web en la región.

In this study, we propose the use of a convolutional neural network (CNN) (2+1) D-based model for the non-invasive detection of iron deficiency anemia using smartphones, addressing the need for accessible and efficient diagnostic methods. We have collected fingertip images from 909 young people, creating a robust dataset to train and validate the model through machine learning (ML), providing us with both spatial and temporal information. Our approach stands out for its simplicity and potential to be implemented in diverse contexts, which facilitates early and accurate diagnosis of anemia. The results show remarkable performance indices in the validation set: an accuracy of 0.9840, a precision of 0.9830, a sensitivity (recall) of 0.9840, an F1-Score of 0.9835, and an AUC ROC of 0.9878, demonstrating the model’s high ability to effectively classify anemic states. These findings validate the efficacy of the model to significantly improve the detection of iron deficiency anemia, promoting the use of mobile technologies and machine learning for more accessible and efficient diagnosis globally. This breakthrough marks a significant step towards improving access and efficiency in health diagnostics through smartphone technology and machine learning, opening new avenues for the effective management of anemia and other health conditions.

Objective. The scientific performance of Peruvian universities was examined and characterized by aggregating information from their institutional repositories. Design/Methodology/Approach. A protocol was followed that included the following stages: (1) analysis and optimization of the DSpace database model; (2) data recovery and validation with the OAI-PMH Protocol; (3) obtaining an index of resources available in the repositories; (4) metadata extraction and processing; and (5) extraction and inclusion of keywords in the database. Results/Discussion. A diverse use of keywords was identified. 69,1% of them appeared only once. A significant gap was evident in producing theses between public and private universities. Conclusions. The discrepancy between the research areas of Peruvian universities and regional needs highlights the importance of greater alignment between academic production and the demands of society in each region; this underlines the need to adapt research programs to address specific problems and contribute more effectively to regional development.

Coffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector.