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Ángel Miguel García-Vico
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2020 – today
- 2024
- [j14]Iago Xabier Vázquez
, Bahgat Ayasi
Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasites. Appl. Soft Comput. 160: 111681 (2024) - [c11]Bahgat Ayasi
Advancing Computational Frontiers: Spiking Neural Networks in High-Energy Efficiency Computing Across Diverse Domains. CAEPIA 2024: 9-18 - [c10]Iago Xabier Vázquez, Ángel Miguel García-Vico, Huseyin Seker, Javier Sedano:
Low Consumption Models for Disease Diagnosis in Isolated Farms. IDEAL (1) 2024: 233-243 - [c9]Manuel Luis González Hernandez, Jorge Ruiz, Lidia Andrés, Randy Lozada, Erik S. Skibinsky-Gitlin, Jorge Fernández, Javier Sedano, Ángel Miguel García-Vico:
Deep Learning Inference on Edge: A Preliminary Device Comparison. IDEAL (1) 2024: 265-276 - 2023
- [j13]Luis Alfonso Pérez Martos
A Multiclustering Evolutionary Hyperrectangle-Based Algorithm. Int. J. Comput. Intell. Syst. 16(1): 165 (2023) - [j12]Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero
TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning. Neurocomputing 517: 223-228 (2023) - [j11]Ángel Miguel García-Vico
A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams. Inf. Fusion 91: 412-423 (2023) - [j10]Luis Alfonso Pérez Martos
Clustering: an R library to facilitate the analysis and comparison of cluster algorithms. Prog. Artif. Intell. 12(1): 33-44 (2023) - [c8]Manuel Luis González Hernandez, Jorge Ruiz, Randy Lozada
FAS-CT: FPGA-Based Acceleration System with Continuous Training. FedCSIS (Communication Papers) 2023: 131-139 - 2022
- [c7]Luis Alfonso Pérez Martos
A Case of Study with the Clustering R Library to Measure the Quality of Cluster Algorithms. HAIS 2022: 88-97 - [c6]Randy Lozada, Jorge Ruiz
Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference. IDEAL 2022: 348-358 - [i1]Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera:
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study). CoRR abs/2206.03179 (2022) - 2021
- [j9]Ángel Miguel García-Vico, Cristóbal J. Carmona, Pedro González
A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams. Expert Syst. Appl. 183: 115419 (2021) - [c5]Ángel Miguel García-Vico
A Preliminary Analysis on Software Frameworks for the Development of Spiking Neural Networks. HAIS 2021: 564-575 - [c4]Manuel Luis González Hernandez, Javier Sedano, Ángel Miguel García-Vico, José Ramón Villar
A Comparison of Techniques for Virtual Concept Drift Detection. SOCO 2021: 3-13 - 2020
- [j8]Ángel Miguel García-Vico, Francisco Charte, Pedro González
E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments. Neurocomputing 415: 60-73 (2020) - [j7]Ángel Miguel García-Vico
FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams. IEEE Trans. Fuzzy Syst. 28(12): 3193-3203 (2020) - [c3]Ángel Miguel García-Vico
A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns. SOCO 2020: 100-110
2010 – 2019
- 2019
- [j6]Ángel Miguel García-Vico
A Big Data Approach for the Extraction of Fuzzy Emerging Patterns. Cogn. Comput. 11(3): 400-417 (2019) - [j5]José María Luna, Cristóbal J. Carmona
Subgroup Discovery on Multiple Instance Data. Int. J. Comput. Intell. Syst. 12(2): 1602-1612 (2019) - 2018
- [j4]Pedro González García
Improvement of subgroup descriptions in noisy data by detecting exceptions. Prog. Artif. Intell. 7(1): 55-64 (2018) - [j3]Ángel Miguel García-Vico
MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns. IEEE Trans. Fuzzy Syst. 26(5): 2861-2872 (2018) - [j2]Ángel Miguel García-Vico
An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects. WIREs Data Mining Knowl. Discov. 8(1) (2018) - 2017
- [c2]Ángel Miguel García-Vico, Pedro González
A first approach to handle fuzzy emerging patterns mining on big data problems: The EvAEFP-spark algorithm. FUZZ-IEEE 2017: 1-6 - 2016
- [j1]Julián Luengo
The influence of noise on the evolutionary fuzzy systems for subgroup discovery. Soft Comput. 20(11): 4313-4330 (2016) - [c1]Ángel Miguel García-Vico
Analysing Concentrating Photovoltaics Technology Through the Use of Emerging Pattern Mining. SOCO-CISIS-ICEUTE 2016: 334-344
Coauthor Index
aka: Cristóbal José Carmona
aka: Pedro González García
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