From ac8f7a89a245f68cde58ec3603f61ad24272f79d Mon Sep 17 00:00:00 2001 From: Ricardo Emanuel Vaz Vargas Date: Mon, 5 Sep 2022 13:43:14 -0300 Subject: [PATCH] Add a citation --- LIST_OF_CITATIONS.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/LIST_OF_CITATIONS.md b/LIST_OF_CITATIONS.md index 01172009..9d4854b3 100644 --- a/LIST_OF_CITATIONS.md +++ b/LIST_OF_CITATIONS.md @@ -56,4 +56,6 @@ Journal of Computational and Applied Mathematics. 2020. https://doi.org/10.1016/ 1. A.L. D’Almeida, N.C.R. Bergiante, G.S. Ferreira, F.R. Leta, C.B.C. Lima, G.B.A. Lima. Digital transformation: a review on artificial intelligence techniques in drilling and production applications. The International Journal of Advanced Manufacturing Technology. 2022. https://doi.org/10.1007/s00170-021-08631-w. -1. A.P.F. Machado, R.E.V. Vargas, P.M. Ciarelli, C.J. Munaro. Improving performance of one-class classifiers applied to anomaly detection in oil wells. Journal of Petroleum Science and Engineering. 2022. https://doi.org/10.1016/j.petrol.2022.110983. \ No newline at end of file +1. A.P.F. Machado, R.E.V. Vargas, P.M. Ciarelli, C.J. Munaro. Improving performance of one-class classifiers applied to anomaly detection in oil wells. Journal of Petroleum Science and Engineering. 2022. https://doi.org/10.1016/j.petrol.2022.110983. + +1. N. Aslam, I.U. Khan, A. Alansari, M. Alrammah, A. Alghwairy, R. Alqahtani, R. Alqahtani, M. Almushikes, M.A. Hashim. Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells. Applied Computational Intelligence and Soft Computing. 2022. https://doi.org/10.1155/2022/1558381. \ No newline at end of file