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Artículo
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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
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On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data
(MDPI, 2021)
Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance ...
Artículo
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Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance
(Elsevier, 2021)
Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of ...
Artículo
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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 ...
Artículo
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Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market
(MDPI, 2021)
The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, ...
Artículo
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Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance
(ScienceDirect, 2021-08-18)
Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of ...
Artículo
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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
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Generation of synthetic data with Conditional Generative Adversarial Networks
(Oxford University Press, 2022)
The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one ...
Artículo
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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
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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, ...