| Shapley additive explanations for NO2 forecasting MV García, JL Aznarte Ecological Informatics 56, 101039, 2020 | 132 | 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 | 126 | 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 | 123 | 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 | 112 | 2016 |
| Predicting air quality with deep learning LSTM: Towards comprehensive models R Navares, JL Aznarte Ecological Informatics 55, 101019, 2020 | 81 | 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 | 68 | 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 | 66 | 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 | 58 | 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 | 42 | 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 | 39 | 2012 |
| A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction R de Medrano, JL Aznarte Applied Soft Computing 96, 106615, 2020 | 37 | 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 | 34 | 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 | 31 | 2018 |
| Probabilistic forecasting for extreme NO2 pollution episodes JL Aznarte Environmental Pollution 229, 321-328, 2017 | 27 | 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 | 21 | 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 | 21 | 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 | 20 | 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 | 17 | 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 | 16 | 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 | 16 | 2018 |
Jose M. BenitezFull Professor, Universidad de GranadaVerified email at decsai.ugr.es
