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Ponencia
Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals
(Springer Link, 2023-09)
This paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder characterized by abnormal breathing pauses or infrequent breathing during sleep. The current standard ...
Artículo
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
(World Scientific, 2021)
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a ...
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
On the performance of deep learning models for time series classification in streaming
(Springer, 2020)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...
Ponencia
Evaluation of the transformer architecture for univariate time series forecasting
(Springer, 2021)
The attention-based Transformer architecture is earning in- creasing popularity for many machine learning tasks. In this study, we aim to explore the suitability of Transformers for time series forecasting, which is a ...
Artículo
Data streams classification using deep learning under different speeds and drifts
(Oxford University Press, 2022)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...
Artículo
Short-term solar irradiance forecasting in streaming with deep learning
(Elsevier, 2023)
Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for efficient load balancing and grid stability. ...