Explainable machine learning for sleep apnea prediction. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso (KES, 2022)

Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python. - Laura Melgar-García; Angela Troncoso-García; David Gutiérrez-Avilés; José Francisco Torres; Alicia Troncoso (CISIS-ICEUTE, 2023)

Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso Lora (IWANN (1), 2023)

Evolutionary computation to explain deep learning models for time series forecasting. - Angela Troncoso-García; Alicia Troncoso Lora; Francisco Martínez-Álvarez; María Martínez-Ballesteros (SAC, 2023)

Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning. - Andrés Manuel Chacón-Maldonado; A. R. Troncoso-García; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés; Alicia Troncoso (SOCO (2), 2023)

A new approach based on association rules to add explainability to time series forecasting models. - A. R. Troncoso-García; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso (Inf. Fusion, 2023)

Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting. - A. R. Troncoso-García; Isabel Brito 001; Alicia Troncoso; Francisco Martínez-Álvarez (Comput. Electron. Agric., 2023)

Ground-Level Ozone Forecasting Using Explainable Machine Learning. - Angela del Robledo Troncoso-García; Manuel Jesús Jiménez-Navarro; Francisco Martínez-Álvarez; Alicia Troncoso (CAEPIA, 2024)

Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection. - A. R. Troncoso-García; M. J. Jiménez-Navarro; Maria Lourdes Linares-Barrera; Isabel Sofia Brito; Francisco Martínez-Álvarez; María Martínez-Ballesteros (SOCO (1), 2024)