Listar Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por título
Mostrando ítems 11-30 de 75
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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. ... -
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
(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. ... -
Commentaries on Viewpoint: The ongoing need for good physiological investigation: Obstructive sleep apnea in HIV patients as a paradigm
(American Physiological Society, 2015-01-15)[Abstract] The intriguing paradigm put forth by Darquenne et al. (3) highlighted that improved therapy against human immunodeficiency virus (HIV) has come at the cost of elevated rates of chronic diseases, such as obstructive ... -
Community detection and social network analysis based on the Italian wars of the 15th century
(Elsevier, 2020)[Abstract]: In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions ... -
Computer-assisted analysis of polysomnographic recordings improves interscorer associated agreement and scoring times
(Public Library of Science, 2022-09)[Abstract]: Study objectives To investigate inter-scorer agreement and scoring time differences associated with visual and computer-assisted analysis of polysomnographic (PSG) recordings. Methods A group of 12 expert scorers ... -
Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review
(Hindawi Publishing Corporation, 2015)Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The ... -
CUDA acceleration of MI-based feature selection methods
(Elsevier, 2024-08)[Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ...
