Listar Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por título
Mostrando ítems 47-66 de 75
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GenEs: una plataforma para la generación, realización y evaluación de exámenes
(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 ... -
How Important Is Data Quality? Best Classifiers vs Best Features
(Elsevier, 2021)[Abstract] The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve question. First, there is an increasingly high number of algorithms available belonging to different families. And ... -
Improving detection of apneic events by learning from examples and treatment of missing data
(I O S Press, 2014)[Abstract] This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to ... -
Improving Medical Data Annotation Including Humans in the Machine Learning Loop
(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 ... -
Insights into distributed feature ranking
(Elsevier, 2019)[Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing ... -
Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop
(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 ... -
Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome
(Bentham Open, 2014-06-13)This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in ... -
Inter-database validation of a deep learning approach for automatic sleep scoring
(PLOS, 2021)[Abstract] Study objectives Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers ... -
Interpretable market segmentation on high dimension data
(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, ... -
Large scale anomaly detection in mixed numerical and categorical input spaces
(Elsevier, 2019)[Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ... -
Low-Precision Feature Selection on Microarray Data: An Information Theoretic Approach
(Springer, 2022)[Abstract] The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the ... -
Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19
(Springer, 2022)[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ... -
On developing an automatic threshold applied to feature selection ensembles
(Elsevier, 2019-01)[Abstract]: Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble ... -
On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems
(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 ... -
On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility
(MDPI, 2023-10-02)[Abstract]: In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard ... -
On the scalability of feature selection methods on high-dimensional data
(Springer, 2018)[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending ... -
Quality of sleep data validation from the Xiaomi Mi Band 5 against polysomnography: comparison study
(JMIR Publications, 2023-05)[Abstract] Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, ... -
Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model
(MDPI, 2022)[Abstract] In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We ... -
Quantum Factory Method: A Software Engineering Approach to Deal with Incompatibilities in Quantum Libraries
(Springer Nature, 2023-06)[Abstract]: The current context of Quantum Computing and its available technologies present an extensive variety of tools and lack of methodologies, leading to incompatibilities across platforms, which end up as inconsistencies ... -
Reduced precision discretization based on information theory
(Elsevier, 2022-01)[Abstract] In recent years, new technological areas have emerged and proliferated, such as the Internet of Things or embedded systems in drones, which are usually characterized by making use of devices with strict requirements ...
