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Ponencia
Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection
(SpringerLink, 2022)
Neural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and artificial vision, among others, for tasks like regression or classification. Their potential has ...
Artículo
A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
(Elsevier, 2022)
Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, ...
Artículo
Explaining deep learning models for ozone pollution prediction via embedded feature selection
(ScienceDirect, 2024)
Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects ...
Ponencia
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning
(Springer Link, 2023-10)
Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy features, which can lead to overfitting and poor performance. Feature selection can help address this ...
Artículo
A new approach based on association rules to add explainability to time series forecasting models
(ScienceDirect, 2023)
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 ...
Ponencia
Evolutionary computation to explain deep learning models for time series forecasting
(Association for Computing Machinery, 2023-06)
Deep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, deep learning is known as a black box approach ...
Artículo
PHILNet: A novel efficient approach for time series forecasting using deep learning
(ScienceDirect, 2023)
Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. ...
Artículo
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia
(MDPI, 2023-02)
Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating ...
Ponencia
Explaining Learned Patterns in Deep Learning by Association Rules Mining
(SpringerLink, 2023-08)
This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability of prediction outcomes. The study aims to gain insights into the patterns that ...
Artículo
Explainable machine learning for sleep apnea prediction
(ScienceDirect, 2022)
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 ...