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de la Cruz, R., Padilla, O., Valle, M. A., & Ruz, G. A. (2021). Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks. Mathematics, 9(6), 639.
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Henriquez, P. A., & Ruz, G. A. (2017). Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers. Neurocomputing, 226, 109–116.
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Henriquez, P. A., & Ruz, G. A. (2018). A non-iterative method for pruning hidden neurons in neural networks with random weights. Appl. Soft. Comput., 70, 1109–1121.
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Henriquez, P. A., & Ruz, G. A. (2019). Noise reduction for near-infrared spectroscopy data using extreme learning machines. Eng. Appl. Artif. Intell., 79, 13–22.
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Hughes, S., Moreno, S., Yushimito, W. F., & Huerta-Canepa, G. (2019). Evaluation of machine learning methodologies to predict stop delivery times from GPS data. Transp. Res. Pt. C-Emerg. Technol., 109, 289–304.
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Simon, F., Ordonez, J., Reddy, T. A., Girard, A., & Muneer, T. (2016). Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data. Renew. Energy, 95, 413–421.
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Song, J. W., Wei, P. F., Valdebenito, M. A., Faes, M., & Beer, M. (2021). Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration. Mech. Syst. Sig. Process., 163, 108106.
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