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José Luis Aznarte
José Luis Aznarte
Full Professor. Artificial Intelligence Department, UNED.
Verified email at dia.uned.es - Homepage
Title
Cited by
Cited by
Year
Shapley additive explanations for NO2 forecasting
MV García, JL Aznarte
Ecological Informatics 56, 101039, 2020
3632020
Predicting air quality with deep learning LSTM: Towards comprehensive models
R Navares, JL Aznarte
Ecological Informatics 55, 101019, 2020
1922020
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
1592016
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
1422011
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
1322007
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
1232018
Precise automatic classification of 46 different pollen types with convolutional neural networks
V Sevillano, K Holt, JL Aznarte
Plos one 15 (6), e0229751, 2020
1172020
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
1132023
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
832010
A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction
R de Medrano, JL Aznarte
Applied Soft Computing 96, 106615, 2020
702020
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
492016
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
442018
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
442007
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
432012
Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels
SP Vasseur, JL Aznarte
Scientific Reports 11 (1), 11592, 2021
412021
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 (2), 31, 2021
392021
Probabilistic forecasting for extreme NO2 pollution episodes
JL Aznarte
Environmental Pollution 229, 321-328, 2017
352017
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
342020
Consideraciones éticas en torno al uso de tecnologías basadas en datos masivos en la UNED
JL Aznarte
RIED-Revista Iberoamericana de Educación a Distancia 23 (2), 237-252, 2020
312020
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
312018
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Articles 1–20