Examinando por Autor "Charte, Francisco"
Mostrando 1 - 7 de 7
- Resultados por página
- Opciones de ordenación
Ítem Avances en la evaluación de la piel periestomal en personas con ostomía digestiva de eliminación. Utilización de modelos de Inteligencia Artificial (EPPIA).(2025-07-15) López-Medina, Isabel María; Pérez-Godoy, María Dolores; Álvarez-Nieto, Carmen; Hueso, César; Capilla, Concepción; Montesinos, Ana Carmen; Moya-Muñoz, Noelia; García-Fernández, Francisco Pedro; Charte, Francisco; Pérez-Jiménez, Claudia; Mora-Artacho, María José; Morales-Ventura, Encarnación; Marí-Vidal, María Carmen; Belinchón, Gema; Marzal-Rodríguez, Julia; Rodríguez-Castellano, Paz; Alba-Fernández, Carmen María; López-Nogues, María; Jiménez-López, Isabel; de-la-Villa, María; Rodríguez-Izquierdo, María Rosario; Olivero-Corral, María Silvia; Bienvenido, María Paz; Hoyo, María Araceli; Rivas-Molina, María Carmen; de-la-Luz, Mayra; Molina-Navarrete, Encarnación; Jurado-Millán, Lucas; Lacasa, Encarnación; López-Megías, Patricia; Coca, Mercedes; Fernández-Gálvez, Antonio José; Espejo-Lunar, Esperanza; Ordoñez, Trinidad; Cabrera-López, MontserratEl avance en la evaluación de la piel periestomal en individuos con ostomía digestiva de eliminación es esencial para mejorar la calidad de vida de los pacientes. El propósito de este proyecto es avanzar en la evaluación de la piel periestomal mediante la aplicación de modelos de Inteligencia Artificial. Se busca desarrollar un sistema innovador y fiable que utilice la Inteligencia Artifical para analizar datos visuales, permitiendo una detección temprana de complicaciones en la piel de estos pacientes. Adicionalmente se pretende analizar los principales factores de riesgo asociados a las lesiones y complicaciones de esta piel. Este proyecto consta de dos fases: recolección y etiquetado de datos para modelos de Inteligencia Artifical, y análisis epidemiológico simultáneo para identificación de factores de riesgo asociados a complicaciones de la piel periestomal.Ítem DESReg: Dynamic Ensemble Selection library for Regression tasks(Elsevier, 2024-05-01) Pérez-Godoy, María Dolores; Molina-Pérez, Marta; Martínez-del-Río, Francisco; Elizondo, David; Charte, Francisco; Rivera-Rivas, Antonio JesúsNowadays, regression is a very demanded predictive task to solve a wide range of problems belonging to different research and society areas. Examples of applications include industry, economic, medical and energy fields. Ensemble methodology works by merging the output obtained from a set of base methods (learners), achieving successful results in both classification and regression tasks. Traditional ensembles use the output of the whole set of base methods, in a static way, to obtain the result of the ensemble. However, latest studies show that dynamic selection of learners or even dynamic aggregation of their outputs produce better results. Methodologies that integrate these techniques are called dynamic ensembles or dynamic ensemble selection. Although the literature and tools to work with dynamic ensembles for classification tasks is abundant, for regression tasks these resources are scarcer. This paper aims to mitigate these shortcomings by presenting a library for the design, development and execution of dynamic ensembles for regression problems. Specifically, the Python software package DESReg is presented. This library allows us to access to the latest dynamic ensemble techniques in the field, standing out for its high configurability, its support for extending it with user-defined functions or its parallel computation capabilities.Ítem E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments(Elsevier, 2020) García-Vico, Angel; Charte, Francisco; González, Pedro; Elizondo, David; Carmona, Cristóbal J.In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process. The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.Ítem EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search(IOS Press, 2020-08-01) Charte, Francisco; Rivera-Rivas, Antonio Jesús; Martínez-de-Río, Francisco; del-Jesús, María JoséMachine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time.Ítem Low-cost IoT gas concentrator system prototype (ponencia)(IEEE Access, 2022-07-01) Montoro Lendínez, Alicia; López Ruíz, José Luis; Charte, Francisco; Espinilla Estévez, Macarena; Medina Quero, JavierÍtem Plan de Gestión de Datos-EPPIA(2024) López-Medina, Isabel María; Pérez-Godoy, María Dolores; Hueso, César; Álvarez-Nieto, Carmen; Charte, Francisco; García-Fernández, Francisco Pedro; Capilla, Concepción; Moya-Muñoz, Noelia; Montesinos, Ana CarmenEste plan describe la gestión de los datos que se crearán en el proyecto EPPIA , su tratamiento conservación. Los datos del proyecto serán FAIR (Findable, Accessible, Interoperable and Reusa-ble). La protección de los datos se garantizará de acuerdo con las normas institucionales.Ítem Strategies for time series forecasting with generalized regression neural networks(Elsevier, 2022-06-28) Martínez-del-Río, Francisco; Charte, Francisco; Frías, María Pilar; Martínez-Rodríguez, Ana MaríaThis paper discusses how to forecast time series using generalized regression neural networks. The main goal is to take advantage of their inherent properties to generate fast, highly accurate forecasts. To this end, the key modeling decisions involved in forecasting with generalized regression neural networks are described. To deal with every modeling decision, several strategies are proposed. Each strategy is analyzed in terms of forecast accuracy and computational time. Apart from the modeling decisions, any successful time series forecasting methodology has to be able to capture the seasonal and trend patterns found in a time series. In this regard, some clever techniques to cope with these patterns are also suggested. The proposed methodology is able to forecast time series in an automatic way. Additionally, the paper introduces a publicly available R package that incorporates the best presented modeling approaches and transformations to forecast time series with generalized regression neural networks.