Listar Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por título
Mostrando ítems 5-24 de 75
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A convolutional network for the classification of sleep stages
(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
(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
(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
(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 novel intelligent approach for man-in-the-middle attacks detection over internet of things environments based on message queuing telemetry transport
(Wiley, 2024)[Abstract]: One of the most common attacks is man-in-the-middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber-attack detection systems. MitM attacks on internet of things systems ... -
A One-Class Classification method based on Expanded Non-Convex Hulls
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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 ... -
Artificial Intelligence in Pre-University Education: What and How to Teach
(MDPI, 2020-08-26)[Abstract] The present paper is part of the European Erasmus+ project on educational innovation led by the UDC and entitled “AI+: Developing an Artificial Intelligence Curriculum adapted to European High School”. In this ... -
Automatic classification of respiratory patterns involving missing data imputation techniques
(Academic Press, 2015-10)[Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to ... -
Automatic detection of EEG arousals
(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. ...