idUS - Perfil del autor: Jiménez Navarro, Manuel Jesús

Autorice su foto

Nombre	Jiménez Navarro, Manuel Jesús
Departamento	Lenguajes y Sistemas Informáticos
Área de conocimiento	Lenguajes y Sistemas Informáticos
Categoría profesional	Profesor Sustituto
Correo electrónico	Solicitar

Nº publicaciones

10
Nº visitas

575
Nº descargas

566

Tipología

Orden

Publicaciones
Capítulo de Libro Uso de herramientas software colaborativas para el seguimiento, estudio y evaluación de clases de enseñanzas prácticas y desarrollo Martínez Ballesteros, María del Mar; Jiménez Navarro, Manuel Jesús; Carranza García, Manuel; Gutiérrez Avilés, David; Martínez Ballesteros, María del Mar (Dykinson, 2024) Para ayudar a la integración de todos los desarrollos individuales existen herramientas de control de versiones como ...
Artículo Explaining deep learning models for ozone pollution prediction via embedded feature selection Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (ScienceDirect, 2024) Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is ...
Ponencia Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (Springer Link, 2023) Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy ...
Ponencia Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation Jiménez Navarro, Manuel Jesús; Vega Márquez, Belén; Luna Romera, José María; Carranza García, Manuel; Martínez Ballesteros, María del Mar (Springer Link, 2023) The quality of university teaching is essential for the success of students and the academic excellence of an educational ...
Ponencia Explaining Learned Patterns in Deep Learning by Association Rules Mining Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (SpringerLink, 2023) This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the ...
Ponencia A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sofia Brito, Isabel (Association for Computing Machinery, 2023) The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially ...
Tesis Doctoral Novel efficient deep learning architectures for time series forecasting Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Asencio Cortés, Gualberto (2023) This thesis focuses on the study of time series prediction using the technique known as deep learning or neural networks. ...
Artículo PHILNet: A novel efficient approach for time series forecasting using deep learning Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (ScienceDirect, 2023) Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior ...
Artículo DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting Molina Cabanillas, Miguel Ángel; Jiménez Navarro, Manuel Jesús; Arjona, Ricardo; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (ScienceDirect, 2022) The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in ...
Ponencia Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sousa Brito, Isabel Sofía; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (SpringerLink, 2022) Neural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and ...