Publicaciones

• PUBLICACIONES EN REVISTAS 42/ V. Sevillano, J.L. Aznarte, Scarce is enough: Image Augmentation to Improve Automatic Pollen Classification in Small Datasets, under review.

  41/ R. Gadea, J.L. Aznarte, Attention to traffic forecasting: improving predictions with temporal graph attention networks, under review. Preprint: https://doi.org/10.36227/techrxiv.19732483.v1.

  40/ L. Gutiérrez, R. de Medrano, J.L. Aznarte, COVID-19 forecasting with deep learning: a distressing survey. Under review. Preprint: https://doi.org/10.36227/techrxiv.16855189.v1.

  39/ K. Sherratt et al., Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. Under review. Preprint: https://www.medrxiv.org/content/10.1101/2022.06.16.22276024v1.

  38/ Olmedo Lucerón C, Díez Domingo J, Expósito Singh D, Moriña Soler D, Aznarte JL, Almagro Pedreño J, Limia Sánchez A. Predicciones de tres modelos matemáticos en relación a la estrategia de vacunación frente a la COVID-19 en España. Junio de 2021. Rev Esp Salud Pública. 2022; 96. https://pubmed.ncbi.nlm.nih.gov/35179148.

  37/ J.L. Aznarte, M. Melendo, J.M. Lacruz, Sobre el uso de tecnologías de reconocimiento facial en la universidad: el caso de la UNED, Revista Iberoamericana de Educación a Distancia (2021). DOI: https://doi.org/10.5944/ried.25.1.31533.

  36/ R. de Medrano, J.L. Aznarte, A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting, Applied Artificial Intelligence (2021). https://doi.org/10.1080/08839514.2021.1935588. 

  35/ S. Pérez-Vasseur, J.L. Aznarte, Forecasting the full distribution of NO2 concentrations for extreme pollution episodes, Scientific Reports, 11 (2021). https://doi.org/10.1038/s41598-021-90063-3.

  34/ R. de Medrano, V. de Buen Remiro, J.L. Aznarte, SOCAIRE: Forecasting and Monitoring Urban Air Quality in Madrid, Environmental Modelling & Software, 143 (2021). https://www.sciencedirect.com/science/article/pii/S1364815221001274.

  33/ R. de Medrano, J.L. Aznarte, On the inclusion of spatial information for spatio-temporal neural networks, Neural Computing and Applications, 2021. https://doi.org/10.1007/s00521-021-06111-6.

  32/ N. Abellán, B. Jiménez-García, J.L. Aznarte, E. Baquedano, M. Domínguez-Rodrigo, 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 balance of power, Archaeological and Anthropological Sciences, 13:31 (2021). https://doi.org/10.1007/s12520-021-01273-9 

  31/ B. Jiménez-García, J.L. Aznarte, N. Abellán, E. Baquedano and M. Domínguez-Rodrigo, Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars, J. R. Soc. Interface 17: 20200446 (2020). http://dx.doi.org/10.1098/rsif.2020.0446 

  30/ R. de Medrano, J.L. Aznarte, A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction, Applied Soft Computing, Volume 96, November 2020. https://doi.org/10.1016/j.asoc.2020.106615. Preprint: https://arxiv.org/abs/2003.13977.

  29/ J.L. Aznarte, Consideraciones éticas en torno al uso de tecnologías basadas en datos masivos en la UNED, Revista Iberoamericana de Educación a Distancia (2020). http://revistas.uned.es/index.php/ried/issue/view/1381 

  28/ V. Sevillano, K. Holt, J.L. Aznarte, Precise automatic classification of 46 different pollen types with convolutional neural networks, PLoS ONE (2020): https://doi.org/10.1371/journal.pone.0229751 

  27/ R. Navares, J.L Aznarte, Deep learning architecture to predict daily hospital admissions, Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04840-8

  26/ R. Navares, J.L. Aznarte, C. Linares, J. Díaz, Direct assessment of health impacts from traffic intensity on hospital admissions in Madrid, Environmental Research, vol. 184, 2020. https://doi.org/10.1016/j.envres.2020.109254 

  25/ M. Vega, J.L. Aznarte, Explaining pollution predictions: a study on NO2 concentrations, Ecological Informatics, Volume 56, March 2020. https://doi.org/10.1016/j.ecoinf.2019.101039

  24/ R. Navares, J.L. Aznarte, Predicting Air Quality with Deep Learning LSTM: Towards Comprehensive Models, Ecological Informatics, 2020, vol. 55: https://doi.org/10.1016/j.ecoinf.2019.101019.

  23/ R. Navares, J.L. Aznarte, Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks, Atmosphere,  2019, 10(11), 717. https://doi.org/10.3390/atmos10110717

  22/ D. Valput, R. Navares, J.L. Aznarte, Forecasting hourly NO2 concentrations in urban areas by ensembling machine learning and mesoscale models: a case study in Madrid, Neural Computing and Applications (2019): https://link.springer.com/article/10.1007/s00521-019-04442-z 

  21/ R. Navares, J.L. Aznarte, Forecasting Plantago airborne pollen: improving feature selection through clustering and Friedman tests, Theoretical and Applied Climatology (2019): https://link.springer.com/article/10.1007/s00704-019-02954-1 

  20/ V. Sevillano, J.L. Aznarte, Improving Classification of Pollen Grain Images of the POLEN23E Dataset through three different Applications of Deep Learning Convolutional Neural Networks, PLoS ONE (2018): https://doi.org/10.1371/journal.pone.0201807.

  19/ E. Florido-Navarro, G. Asencio-Cortés, J.L. Aznarte, F. Martinez-Alvarez, A novel tree-based algorithm to discover seismic patterns in earthquake catalogs. Computers & Geosciences, Volume 115, June 2018, Pages 96-104: https://doi.org/10.1016/j.cageo.2018.03.005.

  18/ R. Navares, J. Díaz, C. Linares, J.L. Aznarte, Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid, Stochastic Environmental Research and Risk Assessment (2018): https://doi.org/10.1007/s00477-018-1519-z.

  17/ J.L. Aznarte, Probabilistic forecasting for extreme NO2 pollution episodes, Environmental Pollution, Volume 229, October 2017, Pages 321-328: https://doi.org/10.1016/j.envpol.2017.05.079

  16/ R. Navares, J.L. Aznarte,  What are the most important variables for airborne pollen forecasting?, Science of the Total Environment (2017), Volume 579.

  15/ E. Florido, F. Martínez-Álvarez, A. Morales, J.L. Aznarte, Earthquake prediction based on artificial neural networks: A survey, Croatian Operational Research Review 7 (2), 159-169 (2016).

  14/ R. Navares, J.L. Aznarte, Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features. Int J Biometeorol (2016). doi:10.1007/s00484-016-1242-8

  13/ J.L. Aznarte, N. Siebert, Dynamic Line Rating Using Numerical Weather Predictions and Machine Learning: a Case Study. IEEE Trans. Power Delivery, Volume: PP, Issue: 99 (2016). doi:10.1109/TPWRD.2016.2543818

  12/ E. Florido, F. Martínez-Álvarez, A. Morales-Esteban, J. Reyes, J.L. Aznarte-Mellado, Detecting precursory patterns to enhance earthquake prediction in Chile. Computers & Geosciences, Volume 76, March 2015, Pages 112-120.

  11/ C. Bergmeir, I. Triguero Velázquez, D. Molina, J.L. Aznarte, J.M. Benítez, Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-switching Models. IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 11, pp. 1841–1847, 2012.

  10/ J.L. Aznarte, J. Alcalá-Fdez, A. Arauzo-Azofra, J.M. Benítez, Financial time series forecasting with a bio-inspired fuzzy model. Expert Systems with Applications, vol. 39 (2012) 12302-12309.

  9/ J.L. Aznarte, J.M. Benítez, Testing for heteroskedasticity of the residuals in fuzzy rule-based models. Applied Intelligence Vol. 34, Issue: 3 (2011), 386-394 (doi:10.1007/s10489-011-0288-x). Paper selected for publication in a special number of the journal dedicated to the conference IEA/AIE 2010.

  8/ A. Arauzo-Azofra, J. L. Aznarte M., J. M. Benítez, Empirical Study of Feature Selection Methods Based on Individual Feature Evaluation for Classification Problems. Expert Systems with Applications Vol. 38, Issue: 7 (2011), 8170-8177.

  7/ J.L. Aznarte, J. Alcalá-Fdez, A. Arauzo-Azofra, J.M. Benítez, Fuzzy autoregressive rules: towards linguistic time series forecasting. Econometric Reviews Vol. 30, Issue 6 (2011) 646-668.

  6/ J.L. Aznarte, J.M. Benítez, Neural-autoregressive time series models with fuzzy equivalences. IEEE Transactions on Neural Networks Vol. 21, No. 9 (2010), 1434-1445.

  5/ J.L. Aznarte, M.C. Medeiros, J.M. Benítez, Testing for linear independence of the residuals in fuzzy rule-based models. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 18, No. 4 (2010) 371–387. Paper selected for publication in a special number of the journal dedicated to the IX ISDA Conference (Pisa 2009).

  4/ J.L. Aznarte, M.C. Medeiros, J.M. Benítez, Linearity testing against a fuzzy rule-based model. Fuzzy Sets and Systems 161 (2010) 1836-1851, doi:10.1016/j.fss.2010.01.005.

  3/ J.L. Aznarte, J.M. Benítez, J.L. Castro, Smooth Transition Autoregressive Models and Fuzzy Rule-based Systems: Functional Equivalence and Consequences. Fuzzy Sets and Systems 158 (2007) 2734-2745, doi:10.1016/j.fss.2007.03.021.

  2/ J.L. Aznarte, R. Navajas, C. Rubio, M. Ruiz, M. A. Garrido, SatDNA Analyzer: a computing tool for satellite-DNA evolutionary Analysis. Bioinformatics 23:6 (2007) 767-768, doi:10.1093/bioinformatics/btm005.

  1/ J.L. Aznarte, D. Nieto Lugilde, J.M. Benítez, F. Alba Sánchez, C. de Linares Fernández, Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications 32 (2007) 1218-1225, doi:10.1016/j.eswa.2006.02.011.