José Luis Aznarte
José Luis Aznarte
Associate Professor. Artificial Intelligence Department, UNED.
Verified email at - Homepage
Cited by
Cited by
Shapley additive explanations for NO2 forecasting
MV García, JL Aznarte
Ecological Informatics 56, 101039, 2020
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
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
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
A novel tree-based algorithm to discover seismic patterns in earthquake catalogs
E Florido, G Asencio–Cortés, JL Aznarte, C Rubio-Escudero, ...
Computers & Geosciences 115, 96-104, 2018
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