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Guevara, E., Babonneau, F., Homem-de-Mello, T., & Moret, S. (2020). A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty. Appl. Energy, 271, 18 pp.
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Arevalo-Ramirez, T., Villacres, J., Fuentes, A., Reszka, P., & Cheein, F. A. A. (2020). Moisture content estimation of Pinus radiata and Eucalyptus globulus from reconstructed leaf reflectance in the SWIR region. Biosyst. Eng., 193, 187–205.
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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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Alejo, L., Atkinson, J., Guzman-Fierro, V., & Roeckel, M. (2018). Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res., 25(21), 21149–21163.
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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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