Javier González-Enrique

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 17

Top Topics

Empirical Mode Decomposition

Demand Forecasting

Machine Learning

Top Venues

Neural Comput. Appl.

Publications

José Antonio Moscoso López, Javier González-Enrique, Daniel Urda, Juan Jesús Ruiz-Aguilar, Ignacio J. Turias
Hourly pollutants forecasting using a deep learning approach to obtain the AQI. Log. J. IGPL 31 (4) (2023)
María Gema Carrasco-García, María Inmaculada Rodríguez-García, Javier González-Enrique, Paloma Rocío Cubillas-Fernández, Juan Jesús Ruiz-Aguilar, Ignacio José Turias Domínguez
Hyperspectral Technology for Oil Spills Detection by Using Artificial Neural Network Classifier. SOCO (1) (2023)
Javier González-Enrique, Juan Jesús Ruiz-Aguilar, Eduardo Madrid Navarro, Rosa Martínez Álvarez-Castellanos, Ivan Felis Enguix, José M. Jerez, Ignacio J. Turias
Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain). SOCO (2022)
M. I. Rodríguez-García, Javier González-Enrique, Juan Jesús Ruiz-Aguilar, Ignacio J. Turias
Pollution Concentrations Prediction Approach Using Autoencoders. SOCO (2022)
María Inmaculada Rodríguez-García, Nawel Kouadria, Arantxa M. Ortega León, Javier González-Enrique, Ignacio J. Turias
Virtual Sensor to Estimate Air Pollution Heavy Metals Using Bioindicators. SOCO (2022)
Damián Nimo, Javier González-Enrique, David Perez, Juan Almagro, Daniel Urda, Ignacio J. Turias
A Virtual Sensor Approach to Estimate the Stainless Steel Final Chemical Characterisation. SOCO (2022)
Daniel Urda, Francisco J. Veredas, Javier González-Enrique, Juan J. Ruiz-Aguilar, José M. Jerez, Ignacio J. Turias
Deep neural networks architecture driven by problem-specific information. Neural Comput. Appl. 33 (15) (2021)
Juan Jesús Ruiz-Aguilar, Ignacio Turias, Javier González-Enrique, Daniel Urda, David A. Elizondo
A permutation entropy-based EMD-ANN forecasting ensemble approach for wind speed prediction. Neural Comput. Appl. 33 (7) (2021)
Javier González-Enrique, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso López, Daniel Urda, Lipika Deka, Ignacio J. Turias
Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain). Sensors 21 (5) (2021)
José Antonio Moscoso López, Daniel Urda, Juan Jesús Ruiz-Aguilar, Javier González-Enrique, Ignacio J. Turias
A machine learning-based forecasting system of perishable cargo flow in maritime transport. Neurocomputing 452 (2021)
José Antonio Moscoso López, Daniel Urda, Javier González-Enrique, Juan Jesús Ruiz-Aguilar, Ignacio J. Turias
Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods. SOCO (2020)
Juan Jesús Ruiz-Aguilar, Daniel Urda, José Antonio Moscoso López, Javier González-Enrique, Ignacio J. Turias
Container Demand Forecasting at Border Posts of Ports: A Hybrid SARIMA-SOM-SVR Approach. OLA (2020)
Juan Jesús Ruiz-Aguilar, Daniel Urda, José Antonio Moscoso López, Javier González-Enrique, Ignacio J. Turias
A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models. Neurocomputing 391 (2020)
José Antonio Moscoso López, Juan Jesús Ruiz-Aguilar, Daniel Urda, Javier González-Enrique, Ignacio José Turias
Ro-Ro Freight Forecasting Based on an ANN-SVR Hybrid Approach. Case of the Strait of Gibraltar. IWANN (1) (2019)
Javier González-Enrique, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso López, Steffanie Van Roode, Daniel Urda, Ignacio J. Turias
A Genetic Algorithm and Neural Network Stacking Ensemble Approach to Improve NO2 Level Estimations. IWANN (1) (2019)
Daniel Urda Muñoz, Juan Jesús Ruiz-Aguilar, Javier González-Enrique, Ignacio Turias Domínguez
A Deep Ensemble Neural Network Approach to Improve Predictions of Container Inspection Volume. IWANN (1) (2019)
José Antonio Moscoso López, Juan Jesús Ruiz-Aguilar, Javier González-Enrique, Daniel Urda, Héctor Mesa, Ignacio J. Turias
Ro-Ro Freight Prediction Using a Hybrid Approach Based on Empirical Mode Decomposition, Permutation Entropy and Artificial Neural Networks. HAIS (2019)