Publications
2023 |
A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting (Journal Article) Computers and Electronics in Agriculture, 215 , pp. 108387, 2023. (Abstract | Links | BibTeX | Tags: deep learning, forecasting, precision agriculture, XAI) @article{TRONCOSO-GARCIA23b, title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting}, author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez}, url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755}, doi = {https://doi.org/10.1016/j.compag.2023.108387}, year = {2023}, date = {2023-11-08}, journal = {Computers and Electronics in Agriculture}, volume = {215}, pages = {108387}, abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time}, keywords = {deep learning, forecasting, precision agriculture, XAI}, pubstate = {published}, tppubtype = {article} } Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso A new approach based on association rules to add explainability to time series forecasting models (Journal Article) Information Fusion, 94 , pp. 169-180, 2023. (Abstract | Links | BibTeX | Tags: association rules, time series, XAI) @article{TRONCOSO-GARCIA23, title = {A new approach based on association rules to add explainability to time series forecasting models}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295}, doi = {10.1016/j.inffus.2023.01.021}, year = {2023}, date = {2023-01-22}, journal = {Information Fusion}, volume = {94}, pages = {169-180}, abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.}, keywords = {association rules, time series, XAI}, pubstate = {published}, tppubtype = {article} } Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal. |
A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Evolutionary computation to explain deep learning models for time series forecasting (Conference) SAC 38th Annual ACM Symposium on Applied Computing, 2023. (Links | BibTeX | Tags: deep learning, time series, XAI) @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 = {deep learning, time series, XAI}, pubstate = {published}, tppubtype = {conference} } |
L. Melgar-García, Á. Troncoso-García, D. Gutiérrez-Avilés, J. F. Torres and A. Troncoso Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python (Conference) ICEUTE 14th International Conference on European Transnational Educational, Lecture Notes in Networks and Systems 2023. (BibTeX | Tags: education, XAI) @conference{ICEUTE23_Laura, title = {Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python}, author = {L. Melgar-García, Á. Troncoso-García, D. Gutiérrez-Avilés, J. F. Torres and A. Troncoso}, year = {2023}, date = {2023-01-01}, booktitle = {ICEUTE 14th International Conference on European Transnational Educational}, series = {Lecture Notes in Networks and Systems}, keywords = {education, XAI}, pubstate = {published}, tppubtype = {conference} } |
A. M. Chacón-Maldonado and A.R. Troncoso-García and F. Martínez-Álvarez, G. Asencio-Cortés and A. Troncoso Olive oil fly population pest forecasting using explainable deep learning (Conference) SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications , Lecture Notes in Networks and Systems 2023. (BibTeX | Tags: precision agriculture, XAI) @conference{SOCO23_Andres, title = {Olive oil fly population pest forecasting using explainable deep learning}, author = {A. M. Chacón-Maldonado and A.R. Troncoso-García and F. Martínez-Álvarez, G. Asencio-Cortés and A. Troncoso}, year = {2023}, date = {2023-01-01}, booktitle = {SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications }, series = {Lecture Notes in Networks and Systems}, keywords = {precision agriculture, XAI}, pubstate = {published}, tppubtype = {conference} } |
2022 |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Explainable machine learning for sleep apnea prediction (Conference) KES International Conference on Knowledge Based and Intelligent information and Engineering Systems, 2022. (Abstract | Links | BibTeX | Tags: association rules, deep learning, time series, XAI) @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 = {association rules, deep learning, time series, XAI}, pubstate = {published}, tppubtype = {conference} } 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. |