Oliveira, David Fernandes Neves; Vismari, Lúcio Flávio 
; Almeida Junior, Jorge Rady de ; Cugnasca, Paulo Sérgio ; Camargo Júnior, João Batista ; Marreto, Eduardo; Doimo, Debora R; Almeida, Leandro P F; Gripp, Rafael; Neves, Marcelo M
Source: ICMLA. Conference titles: International Conference on Machine Learning and Applications (ICMLA). Unidade: EP
Subjects: APRENDIZADO COMPUTACIONAL, FERROVIAS, SENSOR, FALHA
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ABNT
OLIVEIRA, David Fernandes Neves et al. Evaluating unsupervised anomaly detection models to detect faults in heavy haul railway operations. 2019, Anais.. Piscataway: IEEE, 2019. Disponível em: https://doi.org/10.1109/ICMLA.2019.00172. Acesso em: 29 jun. 2025.APA
Oliveira, D. F. N., Vismari, L. F., Almeida Junior, J. R. de, Cugnasca, P. S., Camargo Júnior, J. B., Marreto, E., et al. (2019). Evaluating unsupervised anomaly detection models to detect faults in heavy haul railway operations. In ICMLA. Piscataway: IEEE. doi:10.1109/ICMLA.2019.00172NLM
Oliveira DFN, Vismari LF, Almeida Junior JR de, Cugnasca PS, Camargo Júnior JB, Marreto E, Doimo DR, Almeida LPF, Gripp R, Neves MM. Evaluating unsupervised anomaly detection models to detect faults in heavy haul railway operations [Internet]. ICMLA. 2019 ;[citado 2025 jun. 29 ] Available from: https://doi.org/10.1109/ICMLA.2019.00172Vancouver
Oliveira DFN, Vismari LF, Almeida Junior JR de, Cugnasca PS, Camargo Júnior JB, Marreto E, Doimo DR, Almeida LPF, Gripp R, Neves MM. Evaluating unsupervised anomaly detection models to detect faults in heavy haul railway operations [Internet]. ICMLA. 2019 ;[citado 2025 jun. 29 ] Available from: https://doi.org/10.1109/ICMLA.2019.00172
