Publicaciones (48) Publicaciones de JOSE LUIS AZNARTE MELLADO

2023

  1. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

    eLife, Vol. 12

2022

  1. Avoiding e-protoring, ensuring trust: UNED’s AvEx

    Designing online assessment. Solutions that are rigorous, trusted, flexible and scalable (European Association of Distance Teaching Universities (EADTU)), pp. 75-77

  2. Predicciones de tres modelos matemáticos en relación a la estrategia de vacunación frente a la COVID-19 en España. Junio de 2021

    Revista espanola de salud publica, Vol. 96

  3. Sobre el uso de tecnologías de reconocimiento facial en la universidad: el caso de la UNED

    RIED: revista iberoamericana de educación a distancia, Vol. 25, Núm. 1, pp. 261-270

2021

  1. A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting

    Applied Artificial Intelligence, Vol. 35, Núm. 10, pp. 782-801

  2. Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels

    Scientific Reports, Vol. 11, Núm. 1

  3. 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, Vol. 13, Núm. 2

  4. On the inclusion of spatial information for spatio-temporal neural networks

    Neural Computing and Applications, Vol. 33, Núm. 21, pp. 14723-14740

  5. SOCAIRE: Forecasting and monitoring urban air quality in Madrid

    Environmental Modelling and Software, Vol. 143

2020

  1. A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction

    Applied Soft Computing Journal, Vol. 96

  2. Consideraciones éticas en torno al uso de tecnologías basadas en datos masivos en la UNED

    RIED: revista iberoamericana de educación a distancia, Vol. 23, Núm. 2, pp. 237-252

  3. Deep learning architecture to predict daily hospital admissions

    Neural Computing and Applications, Vol. 32, Núm. 20, pp. 16235-16244

  4. Deep learning improves taphonomic resolution: High accuracy in differentiating tooth marks made by lions and jaguars: Deep learning improves taphonomic resolution: High accuracy in differentiating tooth marks made by lions and jaguars

    Journal of the Royal Society Interface, Vol. 17, Núm. 168

  5. Direct assessment of health impacts on hospital admission from traffic intensity in Madrid

    Environmental Research, Vol. 184

  6. Forecasting Plantago pollen: improving feature selection through random forests, clustering, and Friedman tests

    Theoretical and Applied Climatology, Vol. 139, Núm. 1-2, pp. 163-174

  7. Forecasting hourly NO 2 concentrations by ensembling neural networks and mesoscale models

    Neural Computing and Applications, Vol. 32, Núm. 13, pp. 9331-9342

  8. Precise automatic classification of 46 different pollen types with convolutional neural networks

    PLoS ONE, Vol. 15, Núm. 6

  9. Predicting air quality with deep learning LSTM: Towards comprehensive models

    Ecological Informatics, Vol. 55

  10. Shapley additive explanations for NO2 forecasting

    Ecological Informatics, Vol. 56

2019

  1. Geographical imputation of missing poaceae pollen data via convolutional neural networks

    Atmosphere, Vol. 10, Núm. 11