José Luis Aznarte
José Luis Aznarte
Associate Professor. Artificial Intelligence Department, UNED.
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Titel
Zitiert von
Zitiert von
Jahr
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
1052007
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
1032011
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
562016
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, 250-255, 2010
412010
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
412007
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
322012
SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis
R Navajas-Pérez, C Rubio-Escudero, JL Aznarte, MR Rejón, ...
Bioinformatics 23 (6), 767-768, 2007
222007
Shapley additive explanations for NO2 forecasting
MV García, JL Aznarte
Ecological Informatics 56, 101039, 2020
202020
Probabilistic forecasting for extreme NO2 pollution episodes
JL Aznarte
Environmental Pollution 229, 321-328, 2017
202017
Equivalences between neural-autoregressive time series models and fuzzy systems
JL Aznarte, JM Benítez
IEEE transactions on neural networks 21 (9), 1434-1444, 2010
202010
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
192018
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
192016
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
172012
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 (10), 2849-2859, 2018
142018
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
142017
Predicting air quality with deep learning LSTM: Towards comprehensive models
R Navares, JL Aznarte
Ecological Informatics 55, 101019, 2020
112020
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
102018
Linearity testing for fuzzy rule-based models
JL Aznarte, MC Medeiros, JM Benítez
Fuzzy Sets and Systems 161 (13), 1836-1851, 2010
102010
What are the most important variables for Poaceae airborne pollen forecasting?
R Navares, JL Aznarte
Science of The Total Environment 579, 1161-1169, 2017
82017
A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters
JL Aznarte, D Molina, AM Sánchez, JM Benítez
Applied Intelligence 34 (3), 386-393, 2011
82011
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