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Swarm and evolutionary computation, v.54, 2020년, pp.100650 -
Ortego, Patxi (TECNALIA) , Diez-Olivan, Alberto (TECNALIA) , Del Ser, Javier (TECNALIA) , Veiga, Fernando (TECNALIA) , Penalva, Mariluz (TECNALIA) , Sierra, Basilio (Department of Computer Sciences and Artificial Intelligence, University of the Basque Country (UPV)
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Abstract The Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are ga...
Inf. Fusion Diez-Olivan 50 92 2019 10.1016/j.inffus.2018.10.005 Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0
Int. J. Adv. Manuf. Technol. Diez-Olivan 95 327 2018 10.1007/s00170-017-1204-2 Kernel-based support vector machines for automated health status assessment in monitoring sensor data
Neural Network. Schmidhuber 61 85 2015 10.1016/j.neunet.2014.09.003 Deep learning in neural networks: an overview
APSIPA Transactions on Signal and Information Processing Deng 3 2014 A tutorial survey of architectures, algorithms, and applications for deep learning
J. Manuf. Syst. Wang 48 144 2018 10.1016/j.jmsy.2018.01.003 Deep learning for smart manufacturing: methods and applications
B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le, Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697-8710.
10.1007/978-3-030-01246-5_2 C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, K. Murphy, Progressive neural architecture search, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 19-34.
Swarm and evolutionary computation Camci 41 1 2018 10.1016/j.swevo.2017.10.003 An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm
Swarm and Evolutionary Computation Hoang 38 120 2018 10.1016/j.swevo.2017.07.006 A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions
10.1007/978-3-319-59650-1_17 A. Diez-Olivan, M. Penalva, F. Veiga, L. Deitert, R. Sanz, B. Sierra, Kernel density-based pattern classification in blind fasteners installation, in: International Conference on Hybrid Artificial Intelligence Systems, Springer, pp. 195-206.
Swarm and Evolutionary Computation Wang 49 114 2019 10.1016/j.swevo.2019.06.002 cpso-cnn: an efficient pso-based algorithm for fine-tuning hyper-parameters of convolutional neural networks
Swarm and Evolutionary Computation Junior 49 62 2019 10.1016/j.swevo.2019.05.010 Particle swarm optimization of deep neural networks architectures for image classification
Swarm and Evolutionary Computation Zhou 50 100561 2019 10.1016/j.swevo.2019.100561 Shallow and deep neural network training by water wave optimization
Swarm and Evolutionary Computation Bigdeli 34 75 2017 10.1016/j.swevo.2016.12.004 Time series analysis and short-term forecasting of solar irradiation, a new hybrid approach
Swarm and Evolutionary Computation Malviya 1 223 2011 10.1016/j.swevo.2011.07.001 Tuning of neural networks using particle swarm optimization to model mig welding process
IEEE Access Karim 6 1662 2018 10.1109/ACCESS.2017.2779939 Lstm fully convolutional networks for time series classification
J. Syst. Eng. Electron. Zhao 28 162 2017 10.21629/JSEE.2017.01.18 Convolutional neural networks for time series classification
Wang 1578 2017 Proceedings of the International Joint Conference on Neural Networks Time series classification from scratch with deep neural networks: a strong baseline
Fu 2011 A Review on Time Series Data Mining
ACM SIGKDD Explorations Newsletter Xing 12 40 2010 10.1145/1882471.1882478 A brief survey on sequence classification
Zheng 298 2014 Web-Age Information Management Time series classification using multi-channels deep convolutional neural networks
Chen 2015 The Ucr Time Series Classification Archive
He 770 2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Deep residual learning for image recognition
Holland 1975 Adaptation in Natural and Artificial Systems
Whitley 1994 A Genetic Algorithm Tutorial
Swarm and evolutionary computation Wang 39 86 2018 10.1016/j.swevo.2017.09.004 Parameter optimization and speed control of switched reluctance motor based on evolutionary computation methods
Floreano 2008 Neuroevolution: from Architectures to Learning
Evol. Comput. Stanley 10 99 2002 10.1162/106365602320169811 Evolving neural networks through augmenting topologies
Miikkulainen 293 2019 Evolving Deep Neural Networks, Artificial Intelligence in the Age of Neural Networks and Brain Computing
Soft Computing Diez-Olivan 1 2018 Deep evolutionary modeling of condition monitoring data in marine propulsion systems
Lin 2013 Network in Network
Chollet 2015 Keras
Bickel 2009 Springer Series in Statistics
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