• A classification and review of tools for developing and interacting with machine learning systems 

      Mosqueira-Rey, E.; Hernández-Pereira, Elena; Alonso Ríos, David; Bobes-Bascarán, José (Association for Computing Machinery, 2022)
      [Abstract] In this paper we aim to bring some order to the myriad of tools that have emerged in the field of Artificial Intelligence (AI), focusing on the field of Machine Learning (ML). For this purpose, we suggest a ...
    • A comparison of performance of K-complex classification methods using feature selection 

      Hernández-Pereira, Elena; Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia; Álvarez-Estévez, Diego; Moret-Bonillo, Vicente; Alonso-Betanzos, Amparo (2016-01-20)
      [Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the ...
    • 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 Sleep Stages Classification 

      Fernández-Varela, Isaac; Hernández-Pereira, Elena; Alvarez-Estevez, Diego; Moret-Bonillo, Vicente (2019-02)
      [Abstract]: Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the ...
    • 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 knowledge model for the development of a framework for hypnogram construction 

      Fernández-Leal, Ángel; Cabrero-Canosa, Mariano; Mosqueira-Rey, E.; Moret-Bonillo, Vicente (Elsevier BV, 2017-02-15)
      [Abstract] We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth ...
    • 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 ...
    • A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning 

      Garralda-Barrio, Mariano; Eiras-Franco, Carlos; Bolón-Canedo, Verónica (Elsevier, 2024-07)
      [Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ...
    • A One-Class Classification method based on Expanded Non-Convex Hulls 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (Elsevier, 2023)
      [Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation ...
    • A scalable decision-tree-based method to explain interactions in dyadic data 

      Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019-12)
      [Abstract]: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, ...
    • A scalable saliency-based feature selection method with instance-level information 

      Cancela, Brais; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Gama, João (Elsevier, 2019-11)
      [Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most ...
    • A Systematic and Generalizable Approach to the Heuristic Evaluation of User Interfaces 

      Alonso Ríos, David; Mosqueira-Rey, E.; Moret-Bonillo, Vicente (Taylor & Francis, 2018-01-24)
      [Abstract]: Heuristic evaluation is one of the most actively used techniques for analyzing usability, as it is quick and inexpensive. This technique is based on following a given set of heuristics, which are typically ...
    • A systematic approach to API usability: Taxonomy-derived criteria and a case study 

      Mosqueira-Rey, E.; Alonso Ríos, David; Moret-Bonillo, Vicente; Fernández-Varela, Isaac; Álvarez-Estévez, Diego (Elsevier B.V., 2018-05)
      [Abstract]: CONTEXT. The currently existing literature about Application Program Interface (API) usability is heterogeneous in terms of goals, scope, and audience; and its connection to accepted definitions of usability ...
    • A Taxonomy-Based Usability Study of an Intelligent Speed Adaptation Device 

      Alonso Ríos, David; Mosqueira-Rey, E.; Moret-Bonillo, Vicente (Taylor & Francis Inc., 2014-04-04)
      [Abstract] Usability studies are often based on ad hoc definitions of usability. These studies can be difficult to generalize, they might have a steep learning curve, and there is always the danger of being inconsistent ...
    • 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 ...
    • Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach 

      Mosqueira-Rey, E.; Hernández-Pereira, Elena; Bobes-Bascarán, José; Alonso Ríos, David; Pérez-Sánchez, Alberto; Fernández-Leal, Ángel; Moret-Bonillo, Vicente; Vidal-Ínsua, Yolanda; Vázquez-Rivera, Francisca (Springer Nature, 2023-11)
      [Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty ...
    • 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; Dumitru, 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 ...
    • Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences 

      Meira, Jorge; Carneiro, João; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Novais, Paulo; Marreiros, Goreti (MDPI, 2022)
      [Abstract] Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they ...