@conference{SOCO24_Troncoso,

title = {Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection},

author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros},

url = {https://link.springer.com/chapter/10.1007/978-3-031-75013-7_14},

doi = {https://doi.org/10.1007/978-3-031-75013-7_14},

year  = {2024},

date = {2024-10-10},

booktitle = {SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications},

pages = {139-149},

series = {Lecture Notes in Networks and Systems},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{TRONCOSO-GARCIA24c,

title = {Ground-Level Ozone Forecasting using Explainable Machine Learning},

author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},

url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_8},

doi = {https://doi.org/10.1007/978-3-031-62799-6_8},

year  = {2024},

date = {2024-06-06},

booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},

volume = {14640},

pages = {71-80},

series = {Lecture Notes in Artificial Intelligence},

abstract = {The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{IWANN2023,

title = {Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals},

author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},

year  = {2023},

date = {2023-01-01},

booktitle = {IWANN International Work-conference on Artificial Intelligence},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{SAC2023,

title = {Evolutionary computation to explain deep learning models for time series forecasting},

author = {A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},

url = {https://dl.acm.org/doi/abs/10.1145/3555776.3578994},

year  = {2023},

date = {2023-01-01},

booktitle = {SAC 38th Annual ACM Symposium on Applied Computing},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{TRONCOSO-GARCIA22,

title = {Explainable machine learning for sleep apnea prediction},

author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S1877050922012406},

doi = {https://doi.org/10.1016/j.procs.2022.09.351},

year  = {2022},

date = {2022-09-10},

booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems},

pages = {2930-2939},

abstract = {Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{TRONCOSO-GARCIA21,

title = {Non-invasive devices for respiratory sound monitoring},

author = {A. R. Troncoso-García and J. A. Ortega and R. Seepold and N. Martínez-Madrid},

url = {https://www.sciencedirect.com/science/article/pii/S1877050921018135},

doi = {https://doi.org/10.1016/j.procs.2021.09.076},

year  = {2021},

date = {2021-01-01},

booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems},

pages = {3040-3048},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}