@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{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}

}

@article{10.1111/mice.12810,

title = {Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm},

author = {Kien‐Trinh T. Bui and José F. Torres and David Gutiérrez‐Avilés and Viet‐Ha Nhu and Dieu Tien Bui and Francisco Martínez‐Álvarez},

url = {https://doi.org/10.1111/mice.12810},

doi = {10.1111/mice.12810},

year  = {2022},

date = {2022-01-01},

journal = {Computer-Aided Civil and Infrastructure Engineering},

volume = {37},

pages = {1368-1386},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleo,

title = {Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach},

author = {José-Lázaro Amaro-Mellado and Laura Melgar-García and Cristina Rubio-Escudero and David Gutiérrez-Avilés},

doi = {10.1016/j.cageo.2021.104736},

year  = {2021},

date = {2021-01-01},

journal = {Computers & Geosciences},

volume = {150},

pages = {104736},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-57802-2_22,

title = {Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering},

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

url = {https://doi.org/10.1007/978-3-030-57802-2_22},

doi = {10.1007/978-3-030-57802-2_22},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {N/A},

pages = {226-236},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3341105.3374071,

title = {High-content screening images streaming analysis using the STriGen methodology},

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

url = {https://doi.org/10.1145/3341105.3374071},

doi = {10.1145/3341105.3374071},

year  = {2020},

date = {2020-01-01},

journal = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},

volume = {N/A},

pages = {537-539},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-20055-8_9,

title = {Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks},

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

url = {https://doi.org/10.1007/978-3-030-20055-8_9},

doi = {10.1007/978-3-030-20055-8_9},

year  = {2020},

date = {2020-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {N/A},

pages = {91-100},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.bdr.2020.100135,

title = {Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration},

author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez–Álvarez},

url = {https://doi.org/10.1016/j.bdr.2020.100135},

doi = {10.1016/j.bdr.2020.100135},

year  = {2020},

date = {2020-01-01},

journal = {Big Data Research},

volume = {19-20},

pages = {100135},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1089/big.2020.0051,

title = {Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model},

author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and J. C. Riquelme and A. Troncoso},

url = {https://doi.org/10.1089/big.2020.0051},

doi = {10.1089/big.2020.0051},

year  = {2020},

date = {2020-01-01},

journal = {Big Data},

volume = {8},

pages = {308-322},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-20521-8_22,

title = {Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting},

author = {J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},

url = {https://doi.org/10.1007/978-3-030-20521-8_22},

doi = {10.1007/978-3-030-20521-8_22},

year  = {2019},

date = {2019-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {259-269},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gggr18,

title = {TRIQ: a new method to evaluate triclusters},

author = {D Gutiérrez-Avilés and R Giráldez and F J Gil-Cumbreras and C Rubio-Escudero},

doi = {https://doi.org/10.1186/s13040-018-0177-5},

year  = {2018},

date = {2018-01-01},

journal = {BioData Mining},

volume = {2018},

pages = {11-15},

note = {JCR(2016): 1,57},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mgmrar15,

title = {A novel method for seismogenic zoning based on triclustering. Application to the Iberian Peninsula},

author = {F Martínez-Álvarez and D Gutiérrez-Avilés and A Morales-Esteban and J Reyes and J L Amaro-Mellado and C Rubio-Escudero},

doi = {10.3390/e17075000},

year  = {2015},

date = {2015-01-01},

journal = {Entropy},

volume = {17},

number = {7},

pages = {5000-5021},

note = {IF: 1.743 Q2 25/79},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gr15,

title = {MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data},

author = {D Gutiérrez-Avilés and C Rubio-Escudero},

doi = {10.4137/EBO.S25822},

year  = {2015},

date = {2015-01-01},

journal = {Evol Bioinform},

volume = {11},

number = {404},

pages = {121-135},

note = {IF: , Q3 30/56},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gr14,

title = {Mining 3D patterns from gene expression temporal data: a new tricluster evaluation measure},

author = {D Gutiérrez-Avilés and C Rubio-Escudero},

doi = {10.1155/2014/624371},

year  = {2014},

date = {2014-01-01},

journal = {The Scientific World Journal},

volume = {1},

pages = {453},

note = {IF: Q1 13/59},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{grmr14,

title = {TriGen: A genetic algorithm to mine triclusters in temporal gene expression data},

author = {D Gutiérrez-Avilés and C Rubio-Escudero and Martínez- F Á and J C Riquelme},

doi = {10.1016/j.neucom.2013.03.061},

year  = {2014},

date = {2014-01-01},

journal = {Neurocomputing},

volume = {132},

pages = {42-53},

note = {IF: 2.083 Q/123},

keywords = {},

pubstate = {published},

tppubtype = {article}

}