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ORCIDM. J. Jiménez-Navarro
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- affiliation: University of Seville, Spain
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2020 – today
- 2024
[j4]M. J. Jiménez-Navarro
, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
Explaining deep learning models for ozone pollution prediction via embedded feature selection. Appl. Soft Comput. 157: 111504 (2024)- [c13]Angela del Robledo Troncoso-García, Manuel Jesús Jiménez-Navarro, Francisco Martínez-Álvarez, Alicia Troncoso:
Ground-Level Ozone Forecasting Using Explainable Machine Learning. CAEPIA 2024: 71-80 - [c12]Maria Lourdes Linares-Barrera, Manuel Jesús Jiménez-Navarro, José C. Riquelme, María Martínez-Ballesteros:
Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting. CAEPIA 2024: 81-90 - [c11]Francisco Javier Galán-Sales, Maria Lourdes Linares-Barrera, Pablo Reina-Jiménez, Ana Rodríguez-López, Manuel Jesús Jiménez-Navarro:
Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis. CAEPIA 2024: 184-193 - [c10]Maria Lourdes Linares-Barrera, Manuel Jesús Jiménez-Navarro, Isabel Sofia Brito, José C. Riquelme, María Martínez-Ballesteros:
Evolutionary Feature Selection for Time-Series Forecasting. SAC 2024: 395-399 - 2023
- [j3]M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
PHILNet: A novel efficient approach for time series forecasting using deep learning. Inf. Sci. 632: 815-832 (2023) - [j2]Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. J. Big Data 10(1): 80 (2023) - [c9]Manuel Jesús Jiménez-Navarro, Belén Vega-Márquez, José María Luna-Romera, Manuel Carranza-García, María Martínez-Ballesteros:
Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation. CISIS-ICEUTE 2023: 319-328 - [c8]M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning. IWANN (2) 2023: 15-26 - [c7]Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Isabel Sofia Brito, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. SAC 2023: 441-448 - [c6]M. J. Jiménez-Navarro, María Martínez-Ballesteros, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
Explaining Learned Patterns in Deep Learning by Association Rules Mining. SOCO (2) 2023: 132-141 - [c5]Manuel Jesús Jiménez-Navarro, Camilo Restrepo Estrada, Laura Melgar-García, David Gutiérrez-Avilés:
Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. SOCO (1) 2023: 209-218 - 2022
- [j1]Miguel Angel Molina-Cabanillas, M. J. Jiménez-Navarro, Ricardo Arjona, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting. Knowl. Based Syst. 254: 109644 (2022) - [c4]Manuel Jesús Jiménez-Navarro, María Martínez-Ballesteros, Isabel Sofia Sousa Brito, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. SOCO 2022: 557-566 - 2021
- [c3]José F. Torres, M. J. Jiménez-Navarro, Francisco Martínez-Álvarez, Alicia Troncoso:
Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks. CAEPIA 2021: 216-225 - [c2]M. Á. Molina, M. J. Jiménez-Navarro, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés:
A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery. HAIS 2021: 511-523 - [c1]M. J. Jiménez-Navarro, Francisco Martínez-Álvarez, Alicia Troncoso, Gualberto Asencio-Cortés:
HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting. SOCO 2021: 717-727
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