José Luis Aznarte
José Luis Aznarte
Associate Professor. Artificial Intelligence Department, UNED.
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Empirical study of feature selection methods based on individual feature evaluation for classification problems
A Arauzo-Azofra, JL Aznarte, JM Benítez
Expert systems with applications 38 (7), 8170-8177, 2011
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
JL Aznarte, D Nieto-Lugilde, C de Linares Fernández, CD de la Guardia, ...
Expert Systems with Applications 32 (4), 1218-1225, 2007
Shapley additive explanations for NO2 forecasting
MV García, JL Aznarte
Ecological Informatics 56, 101039, 2020
Dynamic line rating using numerical weather predictions and machine learning: A case study
JL Aznarte, N Siebert
IEEE Transactions on Power Delivery 32 (1), 335-343, 2016
Predicting air quality with deep learning LSTM: Towards comprehensive models
R Navares, JL Aznarte
Ecological Informatics 55, 101019, 2020
Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks
V Sevillano, JL Aznarte
PloS one 13 (9), e0201807, 2018
Photovoltaic Forecasting: A state of the art
B Espinar, JL Aznarte, R Girard, AM Moussa, G Kariniotakis
5th European PV-Hybrid and Mini-Grid Conference, Pages 250-255-ISBN 978-3 …, 2010
Precise automatic classification of 46 different pollen types with convolutional neural networks
V Sevillano, K Holt, JL Aznarte
PLoS One 15 (6), e0229751, 2020
Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences
JL Aznarte, JM Benítez, JL Castro
Fuzzy sets and systems 158 (24), 2734-2745, 2007
Financial time series forecasting with a bio-inspired fuzzy model
JL Aznarte, J Alcalá-Fdez, A Arauzo-Azofra, JM Benítez
Expert Systems with Applications 39 (16), 12302-12309, 2012
A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction
R de Medrano, JL Aznarte
Applied Soft Computing 96, 106615, 2020
Earthquake magnitude prediction based on artificial neural networks: A survey
E Florido, JL Aznarte, A Morales-Esteban, F Martínez-Álvarez
Croatian Operational Research Review, 159-169, 2016
Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid
R Navares, J Díaz, C Linares, JL Aznarte
Stochastic environmental research and risk assessment 32, 2849-2859, 2018
Probabilistic forecasting for extreme NO2 pollution episodes
JL Aznarte
Environmental Pollution 229, 321-328, 2017
Equivalences between neural-autoregressive time series models and fuzzy systems
JL Aznarte, JM Benítez
IEEE transactions on neural networks 21 (9), 1434-1444, 2010
SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis
R Navajas-Perez, C Rubio-Escudero, JL Aznarte, MR Rejon, ...
Bioinformatics 23 (6), 767-768, 2007
Time series modeling and forecasting using memetic algorithms for regime-switching models
C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benítez
IEEE transactions on neural networks and learning systems 23 (11), 1841-1847, 2012
Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features
R Navares, JL Aznarte
International journal of biometeorology 61 (4), 647-656, 2017
Deep learning classification of tooth scores made by different carnivores: achieving high accuracy when comparing African carnivore taxa and testing the hominin shift in the …
N Abellán, B Jiménez-García, J Aznarte, E Baquedano, ...
Archaeological and Anthropological Sciences 13, 1-14, 2021
Forecasting hourly concentrations by ensembling neural networks and mesoscale models
D Valput, R Navares, JL Aznarte
Neural Computing and Applications 32 (13), 9331-9342, 2020
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Artículos 1–20