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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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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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Arevalo-Ramirez, T. A., Castillo, A. H. F., Cabello, P. S. R., & Cheein, F. A. A. (2021). Single bands leaf reflectance prediction based on fuel moisture content for forestry applications. Biosyst. Eng., 202, 79–95.
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Bertossi, L., & Geerts, F. (2020). Data Quality and Explainable AI. ACM J. Data Inf. Qual., 12(2), 11.
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Blanco, K., Salcidua, S., Orellana, P., Sauma, T., Leon, T., Lopez-Steinmetz, L. C., et al. (2023). Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimers disease. Alzheimer's Res. Ther., Early Access.
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Decker, L., Leite, D., Minarini, F., Tisbeni, S. R., & Bonacorsi, D. (2022). Unsupervised Learning and Online Anomaly Detection: An On-Condition Log-Based Maintenance System. Int. J. Embed. Real-Time Commun. Syst., 13(1).
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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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Gutierrez-Portela, F., Arteaga-Arteaga, B. H., Almenares-Mendoza, F., Calderon-Benavente, L., Acosta-Mesa, H. G., & Tabares-Soto. R. (2023). Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset (IDSAI). IEEE Access, 11, 70542–70559.
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Heredia, C., Moreno, S., & Yushimito, W. F. (2022). Characterization of Mobility Patterns with a Hierarchical Clustering of Origin-Destination GPS Taxi Data. IEEE Trans. Intell. Transp. Syst., 23(8), 12700–12710.
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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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Lagos, F., & Pereira, J. (2023). Multi-arme d bandit-base d hyper-heuristics for combinatorial optimization problems. Eur. J. Oper. Res., 312(1), 70–91.
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Leite, D., Skrjanc, I., Blazic, S., Zdesar, A., & Gomide, F. (2023). Interval incremental learning of interval data streams and application to vehicle tracking. Inf. Sci., 630, 1–22.
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Opazo, D., Moreno, S., Alvarez-Miranda, E., & Pereira, J. (2021). Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities. Mathematics, 20(9), 2599.
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Otsuki, A., & Jang, H. (2022). Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks. Chemengineering, 6(6), 92.
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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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Ramos, D., Moreno, S., Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). AC-PLT: An algorithm for computer-assisted coding of semantic property listing data. Behav. Res. Methods, Early Access.
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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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Wolff, P., Rios, S., Clavijo, D., Grana, M., & Carrasco, M. (2020). Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile. J. Biomed. Semant., 11(1), 10 pp.
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