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INTRODUCTION. This article analyses the research results on the relationship between digital teaching competence (CDD) and the digital technologies (DT) use, to support higher education at the University of Nariño (Colombia); this includes the evaluation of digital educational resources use, technological devices and different types of digital tools. The project is based on the need to determine the results obtained after the implementation of the institutional strategy to promote the use and appropriation of these technologies in the educational process, so that there is evidence to support decision-making for its improvement. METHOD. The methodology used for the research was quantitative, non-experimental, with a descriptive and correlational scope. For this, two duly validated questionnaires were used, made up of closed questions, which were applied to a sample of 162 teachers from different faculties. RESULTS. The results indicate that about a half of teachers make very frequent use of different tools, digital content and technological devices; likewise, it is shown that there is a close relationship between technologies frequency use and teaching staff self-perceived CDD level. DISCUSSION. The discussion addresses the importance and usefulness of the results obtained, with a view to developing concrete actions to improve the indicators evaluated. In addition, it is analysed how research in this field provides important information, so that universities can design improvement plans and use of available TD, as occurred in the evaluated university. The study model that has been developed can be transferable to other Higher Education institutions., INTRODUCCIÓN. En el presente artículo se analizan los resultados de una investigación en torno a la relación entre la competencia digital docente (CDD) y el uso de tecnologías digitales (TD) como apoyo a la educación superior en la Universidad de Nariño (Colombia); esto incluye la evaluación de uso de recursos educativos digitales, dispositivos tecnológicos y diferentes tipos de herramientas digitales. El objetivo es ofrecer a otras universidades un método de evaluación del uso y apropiación de estas tecnologías en el proceso educativo, de manera que puedan obtener evidencias que sustenten una toma de decisiones informada a la hora de diseñar los proyectos institucionales en el ámbito de las TD. MÉTODO: La metodología usada para la investigación fue cuantitativa, no experimental, con alcance descriptivo y correlacional. Para ello se utilizaron dos cuestionarios debidamente validados, conformados por preguntas cerradas, los cuales fueron aplicados a una muestra de 162 docentes de diferentes facultades. RESULTADOS: Los resultados indican que cerca de la mitad del profesorado hace un uso muy frecuente de diferentes herramientas, contenidos digitales y dispositivos tecnológicos; así mismo, se demuestra que existe una estrecha relación entre la frecuencia de uso de estas tecnologías y el nivel de desarrollo de la CDD autopercibida por el profesorado. DISCUSIÓN: Para la discusión se aborda la importancia y utilidad de los resultados obtenidos, con miras al desarrollo de acciones concretas para el mejoramiento de los indicadores analizados. Así mismo, se analiza la forma en que las investigaciones en este campo brindan información muy importante para que las universidades puedan diseñar planes de mejoramiento y de aprovechamiento de las TD disponibles, tal como ocurrió en la institución objeto de estudio. El modelo de estudio que se ha desarrollado puede ser transferible a otras instituciones de Educación Superior.

La provincia de Santa Elena, Ecuador, se encuentra en un proceso de transformación educativa impulsado por el interés de los docentes en incorporar los últimos avances en investigación educativa a su práctica docente. Sin embargo, este camino hacia la mejora de la calidad educativa se enfrenta a diversos desafíos. Para explorar este panorama, se realizó un estudio con metodología mixta, cuantitativa y cualitativa, que recopiló las percepciones de 63 docentes de la provincia de Santa Elena, Ecuador. Los resultados revelaron un panorama esperanzador: existe un creciente interés entre los docentes por integrar estos avances a su práctica. Se valen de diversas fuentes de información para mantenerse actualizados, demostrando un compromiso con el aprendizaje continuo y el reconocimiento del valor de la investigación para enriquecer la enseñanza.

Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature.

En este trabajo de fin de grado se va a explicar el desarrollo de un sistema de reconocimiento del lenguaje de signos utilizando técnicas de Machine Learning (ML), Deep Learning(DL) y visión por computadora. El objetivo principal de este proyecto es diseñar e implementar un programa que permita capturar y clasificar imágenes, en este caso, que contengan los gestos de las manos que corresponden a el abecedario del lenguaje de signos inglés. Para ello, se emplea una red neuronal convolucional para el entrenamiento y la clasificación de las imágenes capturadas con una cámara. Además, se hará uso de una Raspberry Pi junto con una cámara y un display para ejecutar el programa y mostrar los resultados en un entorno visual. Los resultados obtenidos demuestran una alta precisión en el reconocimiento de las letras del lenguaje de signos, lo que valida la eficacia del sistema propuesto.

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed.

The emotional or affective state has a direct impact not only on personal life, but also in the field of work, sports, rehabilitation processes, among other fields. In the evolving understanding of emotional theory, it has been theorized that an emotion can be classified according to a two-dimensional model composed of an Arousal value and a Valence value, as well as empirically demonstrating the impact of emotions on physiological variables. This work presents the development of a wearable device for capturing physiological signals, the collection of a dataset (after approval by the ethics committee) in which participants’ emotional states are induced, and the development of an automatic classifier of the emotional state based on neural networks. According to this last point, a 4-phase optimization process is presented in which the physiological sensors are evaluated independently and with multiple variations of the hyperparameters of the neural networks, keeping those that provide the most information, combinations are made between them and the robustness of the final system obtained is evaluated. The results exceed 92% accuracy in all cases, which, compared with previous work, significantly improves the classifiers developed in recent years. The key contributions of this study are detailed as follows: (a) a wearable device designed to collect physiological signals from the user in a non-invasive way is presented, proving that it works properly in a controlled environment; (b) a data-collection protocol is designed to induce emotional states in test subjects using small video clips, demonstrating that the user evokes the feelings that are induced; and (c) a machine learning-based system is developed and optimized to classify the emotional state based on the two-dimensional model of emotion, demonstrating its efficiency and accuracy.