Listar Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por título
Mostrando ítems 31-50 de 75
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Data-driven predictive maintenance framework for railway systems
(IOS Press, 2023)[Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of ... -
Dealing with heterogeneity in the context of distributed feature selection for classification
(Springer, 2021)[Abstract]: Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be ... -
Distributed classification based on distances between probability distributions in feature space
(Elsevier, 2019-09)[Abstract]: We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning ... -
Distributed correlation-based feature selection in spark
(Elsevier, 2019-09)[Abstract]: Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We ... -
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
(IEEE, 2023-07)[Abstract]: Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms ... -
Emerging technologies in artificial intelligence: quantum rule-based systems
(Springer, 2018)[Abstract]: This article tries to establish synergies between two areas of research and development that are apparently disconnected: artificial intelligence (AI) and quantum computing (QC). The article begins with a brief ... -
Ensembles for feature selection: A review and future trends
(Elsevier, 2019)[Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good ... -
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?
(2024-07)In the current landscape, user opinions exert an unprecedented influence on the trajectory of companies. In the field of online review platforms, these opinions, transmitted through text reviews and numerical ratings, ... -
FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs
(Elsevier, 2022)[Abstract]: Facial biometrics play an essential role in the fields of law enforcement and forensic sciences. When comparing facial traits for human identification in photographs or videos, the analysis must account for ... -
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning
(Elsevier, 2022-08)[Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ... -
Fast deep autoencoder for federated learning
(Elsevier Ltd, 2023-11)[Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network ... -
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing
(Association for Computing Machinery, 2020)[Abstract]: The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. ... -
Feature Selection in Big Image Datasets
(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
(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 ... -
Feature selection with limited bit depth mutual information for portable embedded systems
(Elsevier, 2020-06)[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ... -
FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks
(Elsevier B.V., 2023)[Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, ... -
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 ...