Yadav G, Paul K. Architecture and security of SCADA systems: A review. International Journal of Critical Infrastructure Protection. 2021; 34: article ID 100433. https://doi.org/10.1016/j.ijcip.2021.100433.
Sircar A, Yadav K, Rayavarapu K, Bist N, Oza H. Application of machine learning and artificial intelligence in oil and gas industry. Pet. Res. 2021; 6(4): 379–391. https://doi.org/10.1016/j.ptlrs.2021.05.009.
Zhang D, Chen Y, Meng J. Synthetic well logs generation via Recurrent Neural Networks. Petroleum Exploration and Development. 2018; 45(4): 629–639. https://doi.org/10.1016/S1876-3804(18)30068-5.
Zhou D, Huang D, Hao J, Ren Y, Jiang P, Jia X. Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by Generative Adversarial Networks. Eng. Failure Anal. 2020; 116: article ID 104759. https://doi.org/10.1016/j.engfailanal.2020.104759.
Wong P, Wong WK, Juwono FH, Gopal L, Yusoff MA. A minimalist approach for detecting sensor abnormality in oil and gas platforms. Pet. Res. 2022; 7(2): 177–185. https://doi.org/10.1016/j.ptlrs.2021.09.007.
Alguliyev R, Imamverdiyev Y, Sukhostat L. Intelligent diagnosis of petroleum equipment faults using a deep hybrid model. SN Appl. Sci. 2020; 2: article ID 924. https://doi.org/10.1007/s42452-020-2741-0.
Li Y, Ge T. Imminence monitoring of critical events: A representation learning approach. In: ACM SIGMOD SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data, 20–25 June 2021, Virtual event China. New York, NY, USA: ACM; 2021. p. 1103–1115. https://doi.org/10.1145/3448016.3452804.
Vargas REV, Munaro CJ, Ciarelli PM, Medeiros AM, do Amaral BG, Barrionuevo DC, et al. A realistic and public dataset with rare undesirable real events in oil wells. J. Pet. Sci. Eng. 2019; 181: article ID 106223. https://doi.org/10.1016/j.petrol.2019.106223.
Marins MA, Barros BD, Santos IH, Barrionuevo DC, Vargas REV, Prego TDM, et al. Fault detection and classification in oil wells and production/service lines using random forest. J. Pet. Sci. Eng. 2021; 197: article ID 107879. https://doi.org/10.1016/j.petrol.2020.107879.
Carvalho BG. Evaluating machine learning techniques for detection of flow instability events in offshore oil wells. MSc thesis. Federal University of Espírito Santo; 2021.
Klinovenko SA, Lozhnikov PS, Sulavko AE, Pletnev LV. Recognition of pre-emergency situations during the operation of oil wells based on convolutional neural networks and a modified Bayes classifier. In: Saint Petersburg Electrotechnical University “LETI” Proceedings of the Conference of Russian Young Researchers in Electrical and Electronic Engineering, 23–27 January 2023, Saint Petersburg, Russia. Saint Petersburg: Saint Petersburg Electrotechnical University “LETI”; 2023. p. 1297–1302.
Sulavko AE, Samotuga AE, Kuprik IA. Personal identification based on acoustic characteristics of outer ear using cepstral analysis, Bayesian classifier, and artificial neural networks. IET Biometrics. 2021; 10(6): 692–705. https://doi.org/10.1049/bme2.12037.
Lukic Y, Vogt C, Dürr O, Stadelmann T. Speaker identification and clustering using convolutional neural networks. In: IEEE Proceedings of the 26th International Workshop on Machine Learning for Signal Processing (MLSP), 13–16 September 2016, Salerno, Italy. Salerno, Italy: IEEE; 2016. p. 1–6. https://doi.org/10.1109/MLSP.2016.7738816.
Nagrani A, Chung JS, Xie W, Zisserman A. Voxceleb: Large-scale speaker verification in the wild. Computer Speech & Language. 2020; 60: article ID 101027. https://doi.org/10.1016/j.csl.2019.101027.
Sulavko AE, Samotuga AE, Stadnikov DG, Pasenchuk VA, Zhumazhanova SS. Biometric authentication on the basis of electroencephalograms parameters. J. Phys.: Conf. Ser. 2019; 1260(2): article ID 022011 https://doi.org/10.1088/1742-6596/1260/2/022011.