José Luis Aznarte

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Jose M. BenitezFull Professor, Universidad de GranadaDirección de correo verificada de decsai.ugr.es
Antonio Arauzo-AzofraUniversidad de CórdobaDirección de correo verificada de uco.es
Rodrigo de Medrano LópezPhDDirección de correo verificada de dia.uned.es
Francisco Martínez-ÁlvarezFull Professor, Data Science & Big Data Lab, Pablo de Olavide University of Seville, SpainDirección de correo verificada de upo.es
Cristina Rubio-EscuderoSenior Lecturer, University of SevillaDirección de correo verificada de us.es
Julio DíazResearch Professor at ISCIII.Director of the Reference Unit on Climate Change, Health andDirección de correo verificada de isciii.es
Diego Nieto Lugilde (ORCID: 0000-00...Dpto. de Botánica, Ecología y Fisiología Vegetal. Universidad de Córdoba (https://ror.orgDirección de correo verificada de uco.es
Francisca Alba SánchezProfessor of Botany, University of GranadaDirección de correo verificada de ugr.es
C. De Linares FernandezProfesor Contratado Doctor (UGR)Dirección de correo verificada de ugr.es
Rafael Navajas-PérezAssociate Professor, Universidad de Granada, SpainDirección de correo verificada de ugr.es
Marcelo C. MedeirosProfessor, Department of Economics, The University of Illinois at Urbana-ChampaignDirección de correo verificada de illinois.edu
George KariniotakisProfessor MINES ParisTech. Head of Renewable Energies & SmartGrids GroupDirección de correo verificada de mines-paristech.fr
Espinar BellaMINES ParisTechDirección de correo verificada de mines-paristech.fr
Robin GirardResearch Associate at MINES Paris PSLDirección de correo verificada de mines-paristech.fr
Christoph BergmeirUniversity of GranadaDirección de correo verificada de monash.edu
Juan Luis CastroProfessor of Computer Science and Artificial Intelligence. University of GranadaDirección de correo verificada de decsai.ugr.es

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José Luis Aznarte

Associate Professor. Artificial Intelligence Department, UNED.

Dirección de correo verificada de dia.uned.es - Página principal

Artificial Intelligence Time series forecasting Deep learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Shapley additive explanations for NO2 forecasting MV García, JL Aznarte Ecological Informatics 56, 101039, 2020	165	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	130	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	127	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	123	2016
Predicting air quality with deep learning LSTM: Towards comprehensive models R Navares, JL Aznarte Ecological Informatics 55, 101019, 2020	97	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	82	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	71	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	70	2020
A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction R de Medrano, JL Aznarte Applied Soft Computing 96, 106615, 2020	47	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	43	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	40	2012
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	35	2018
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
Probabilistic forecasting for extreme NO2 pollution episodes JL Aznarte Environmental Pollution 229, 321-328, 2017	31	2017
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations K Sherratt, H Gruson, H Johnson, R Niehus, B Prasse, F Sandmann, ... Elife 12, e81916, 2023	26	2023
Time series modeling and forecasting using memetic algorithms for regime-switching models C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benitez IEEE transactions on neural networks and learning systems 23 (11), 1841-1847, 2012	24	2012
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
Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars B Jiménez-García, J Aznarte, N Abellán, E Baquedano, ... Journal of the Royal Society Interface 17 (168), 20200446, 2020	20	2020
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	19	2021

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