David Gutiérrez Avilés

@article{prisma287471,

title = {Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits},

author = {D Martin-Pérez and F Rodriguez-Diaz and D Gutiérrez-Avilés and A Troncoso and F Martinez-Alvarez},

url = {https://prisma.us.es/publicacion/287471},

doi = {10.32604/cmes.2026.082712},

year  = {2026},

date = {2026-01-01},

journal = {CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES},

volume = {148},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{prisma262384,

title = {MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning},

author = {David Gutiérrez-Avilés and Manuel Jesús Jiménez-Navarro and José Francisco Torres and Francisco Martínez-Álvarez},

url = {https://prisma.us.es/publicacion/262384},

doi = {10.1016/j.neucom.2025.130046},

year  = {2025},

date = {2025-01-01},

journal = {NEUROCOMPUTING},

volume = {637},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{prisma270372,

title = {A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications},

author = {Francesc Rodríguez-Díaz and David Gutiérrez‐Avilés and Alicia Troncoso and Francisco Martínez‐Álvarez},

url = {https://prisma.us.es/publicacion/270372},

doi = {10.1145/3764582},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {ACM COMPUTING SURVEYS},

journal = {ACM COMPUTING SURVEYS},

volume = {58},

number = {4},

pages = {1–35},

note = {Tipo en PRISMA: Revisión.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{prisma258560,

title = {Online forecasting using neighbor-based incremental learning for electricity markets},

author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso},

url = {https://prisma.us.es/publicacion/258560},

doi = {10.1007/s00521-024-10876-x},

year  = {2025},

date = {2025-01-01},

journal = {NEURAL COMPUTING & APPLICATIONS},

volume = {37},

number = {27},

pages = {22923–22940},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3605098.3636034,

title = {An evolutionary triclustering approach to discover electricity consumption patterns in France},

author = {David Gutierrez-Aviles and Jose F. Torres and Francisco Martinez-Alvarez and Jairo Cugliari},

url = {https://doi.org/10.1145/3605098.3636034},

doi = {10.1145/3605098.3636034},

year  = {2024},

date = {2024-01-01},

journal = {Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {386-394},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_13,

title = {An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines},

author = {Francesc Rodríguez-Díaz and José Francisco Torres and David Gutiérrez-Avilés and Alicia Troncoso and Francisco Martínez-Álvarez},

url = {https://doi.org/10.1007/978-3-031-62799-6_13},

doi = {10.1007/978-3-031-62799-6_13},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {121-130},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleh,

title = {A Novel Distributed Forecasting Method Based on Information Fusion and Incremental Learning for Streaming Time Series},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Cristina Rubio-Escudero and Alicia Troncoso},

doi = {10.2139/ssrn.4326600},

year  = {2023},

date = {2023-01-01},

journal = {SSRN Electronic Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42529-5_20,

title = {Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting},

author = {Manuel Jesús Jiménez-Navarro and Camilo Restrepo-Estrada and Laura Melgar-García and David Gutierrez-Aviles},

url = {https://doi.org/10.1007/978-3-031-42529-5_20},

doi = {10.1007/978-3-031-42529-5_20},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {209-218},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.engappai.2023.106326,

title = {Identifying novelties and anomalies for incremental learning in streaming time series forecasting},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Cristina Rubio-Escudero and Alicia Troncoso},

url = {https://doi.org/10.1016/j.engappai.2023.106326},

doi = {10.1016/j.engappai.2023.106326},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {123},

pages = {106326},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{prisma219563,

title = {A new Apache Spark-based framework for big data streaming forecasting in IoT networks},

author = {Antonio M. Fernández-Gómez and David Gutiérrez-Avilés and Alicia Troncoso and Francisco Martínez-Álvarez},

url = {https://prisma.us.es/publicacion/219563},

doi = {10.1007/s11227-023-05100-x},

year  = {2023},

date = {2023-01-01},

journal = {JOURNAL OF SUPERCOMPUTING},

volume = {79},

number = {10},

pages = {11078–11100},

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{articlej,

title = {A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Maria Teresa Godinho and Rita Espada and Isabel Brito and Francisco Martínez-Álvarez and Alicia Troncoso and Cristina Rubio-Escudero},

doi = {10.1016/j.neucom.2021.06.101},

year  = {2022},

date = {2022-01-01},

journal = {Neurocomputing},

volume = {500},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-87869-6_67,

title = {Study Case of Household Electricity Consumption Patterns in London by Clustering Methodology},

author = {José María Luna-Romera and Manuel Carranza-García and David Gutiérrez-Avilés and José C. Riquelme-Santos},

url = {https://doi.org/10.1007/978-3-030-87869-6_67},

doi = {10.1007/978-3-030-87869-6_67},

year  = {2022},

date = {2022-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {N/A},

pages = {706-716},

keywords = {},

pubstate = {published},

tppubtype = {article}

}