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Henderson, R. G., Verougstraete, V., Anderson, K., Arbildua, J. J., Brock, T. O., Brouwers, T., et al. (2014). Inter-laboratory validation of bioaccessibility testing for metals. Regul. Toxicol. Pharmacol., 70(1), 170–181.
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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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Lobos, F., Goles, E., Ruivo, E. L. P., de Oliveira, P. P. B., & Montealegre, P. (2018). Mining a Class of Decision Problems for One-dimensional Cellular Automata. J. Cell. Autom., 13(5-6), 393–405.
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Montalva-Medel, M., de Oliveira, P. P. B., & Goles, E. (2018). A portfolio of classification problems by one-dimensional cellular automata, over cyclic binary configurations and parallel update. Nat. Comput., 17(3), 663–671.
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Pham, D. T., & Ruz, G. A. (2009). Unsupervised training of Bayesian networks for data clustering. Proc. R. Soc. A-Math. Phys. Eng. Sci., 465(2109), 2927–2948.
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Rozas Andaur, J. M., Ruz, G. A., & Goycoolea, M. (2021). Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company. Electronics, 10(22), 2787.
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Ruz, G. A. (2016). Improving the performance of inductive learning classifiers through the presentation order of the training patterns. Expert Syst. Appl., 58, 1–9.
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Sanchez-Saez, P., Lira, H., Marti, L., Sanchez-Pi, N., Arredondo, J., Bauer, F. E., et al. (2021). Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors. Astron. J., 162(5), 206.
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