@article{article,

title = {Object detection using depth completion and camera-LiDAR fusion for autonomous driving},

author = {Manuel Carranza-García and F. Galán-Sales and José María Luna-Romera and José Riquelme},

doi = {10.3233/ICA-220681},

year  = {2022},

date = {2022-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {29},

pages = {1-18},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/jigpal/jzac033,

title = {Data streams classification using deep learning under different speeds and drifts},

author = {Pedro Lara-Benítez and Manuel Carranza-García and David Gutiérrez-Avilés and José C Riquelme},

url = {https://doi.org/10.1093/jigpal/jzac033},

doi = {10.1093/jigpal/jzac033},

issn = {1367-0751},

year  = {2022},

date = {2022-01-01},

journal = {Logic Journal of the IGPL},

abstract = {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, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts.},

note = {jzac033},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pedro2020,

title = {Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting},

author = {P Lara-Benítez and M Carranza-García and J M Luna-Romera and J C Riquelme},

doi = {10.3390/app10072322},

year  = {2020},

date = {2020-03-28},

journal = {Applied Sciences},

volume = {10},

number = {7},

pages = {2322},

note = {JCR (2018): 2.217},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lara-Benítez2020,

title = {Asynchronous dual-pipeline deep learning framework for online data stream classification},

author = {P Lara-Benítez and M Carranza-García and J García-Gutiérrez and J C Riquelme },

doi = {10.3233/ICA-200617},

year  = {2020},

date = {2020-02-27},

journal = {Integrated Computer-Aided Engineering},

volume = {27},

number = {2},

pages = {101-119},

note = {JCR (2018): 4.904},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{M2019,

title = {A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks},

author = {M Carranza-García and J García-Gutiérrez and J C Riquelme},

doi = {10.3390/rs11030274},

year  = {2019},

date = {2019-01-31},

journal = {Remote Sensing},

volume = {11 (3)},

number = {274},

note = {JCR(2018): 4.118},

keywords = {},

pubstate = {published},

tppubtype = {article}

}