Now showing items 1-18 of 18

    • A computational system for the Heuristic Forecasting of Fire Risk 

      Moret-Bonillo, Vicente; Cabrero-Canosa, Mariano; Mosqueira-Rey, E.; Carballal Mato, Elena; Piñeiro, M.; Kolev, S.; Galiñares, A.V.; Paz Andrade, M.J.; Carballas, Tarsy (International Institute of Informatics and Systemics, 2002-07)
      This article describes a computational system which forecasts the potential risk of forest fires, by processing a set of meteorological variables so as to produce a fire weather risk index. The system also studies a set ...
    • A convolutional network for the classification of sleep stages 

      Fernández-Varela, Isaac; Hernández-Pereira, Elena; Moret-Bonillo, Vicente (M D P I AG, 2018-09-14)
      [Abstract] The classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole ...
    • A Machine Learning Solution for Distributed Environments and Edge Computing 

      Penas-Noce, Javier; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (MDPI AG, 2019-08-09)
      [Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive ...
    • Adaptive Real-Time Method for Anomaly Detection Using Machine Learning 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (MDPI AG, 2020-08-20)
      [Abstract] Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring ...
    • An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures 

      Seijas Carpente, Manuel; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Rodríguez-Arias, Alejandro; Dimitru, Adina (MDPI AG, 2020-08-20)
      [Abstract] COVID-19 has brought a new normality in society. However, to avoid the situation, the virus must be stopped. There are several ways in which the governments of the world have taken action, from small measures ...
    • An Intelligent and Collaborative Multiagent System in a 3D Environment 

      Rodríguez Arias, Alejandro; Guijarro-Berdiñas, Bertha; Sánchez-Maroño, Noelia (MDPI AG, 2020-08-21)
      [Abstract] Multiagent systems (MASs) allow facing complex, heterogeneous, distributed problems difficult to solve by only one software agent. The world of video games provides problems and suitable environments for the use ...
    • Anomaly Detection in IoT: Methods, Techniques and Tools 

      Vigoya, Laura; López-Vizcaíno, Manuel F.; Fernández, Diego; Carneiro, Víctor (MDPI AG, 2019-07-22)
      [Abstract] Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how ...
    • Automatic detection of EEG arousals 

      Fernández-Varela, Isaac; Hernández-Pereira, Elena; Álvarez-Estévez, Diego; Moret-Bonillo, Vicente (ESANN, 2016-04-27)
      [Abstract] Fragmented sleep is commonly caused by arousals that can be detected with the observation of electroencephalographic (EEG) signals. As this is a time consuming task, automatization processes are required. ...
    • Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings 

      Eiras-Franco, Carlos; Flores, Miguel; Bolón-Canedo, Verónica; Zaragoza, Sonia; Fernández-Casal, Rubén; Naya, Salvador; Tarrío-Saavedra, Javier (2019)
      [Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ...
    • Feature Selection in Big Image Datasets 

      Figueira-Domínguez, J. Guzmán; Bolón-Canedo, Verónica; Remeseiro, Beatriz (MDPI AG, 2020-08-24)
      [Abstract] In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level ...
    • Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems 

      Morán-Fernández, Laura; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (MDPI AG, 2018-09-17)
      [Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...
    • GenEs: una plataforma para la generación, realización y evaluación de exámenes 

      Cabrero-Canosa, Mariano; Acha Aller, Santiago X. (Thomson-Paraninfo, 2006)
      Este artículo describe una herramienta de entorno web que asiste al profesor en la tarea completa de la evaluación: desde la fase de composición del examen, seleccionando aleatoriamente un conjunto representativo de ...
    • Improving Medical Data Annotation Including Humans in the Machine Learning Loop 

      Bobes-Bascarán, José; Mosqueira-Rey, E.; Alonso-Ríos, David (MDPI, 2021)
      [Abstract] At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly ...
    • Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop 

      Mosqueira-Rey, E.; Alonso-Ríos, David; Baamonde-Lozano, Andrés (Elsevier, 2021)
      [Abstract] Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This ...
    • Interpretable market segmentation on high dimension data 

      Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (M D P I AG, 2018-09-17)
      [Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
    • On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems 

      Blanco, Eva; Remeseiro, Beatriz; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-19)
      [Abstract] Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users ...
    • Regression Tree Based Explanation for Anomaly Detection Algorithm 

      López-Riobóo Botana, Iñigo Luis; Eiras-Franco, Carlos; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-18)
      [Abstract] This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ...
    • Usability Heuristics for Domain-Specific Languages (DSLs) 

      Mosqueira-Rey, E.; Alonso-Ríos, David (ACM, 2020-03-30)
      [Abstract] The usability of Domain-Specific Languages (DSLs) has been attracting considerable interest from researchers lately. In particular, our literature review found many usability studies that make use of subjective ...