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Semi-Supervised Active Learning for Anomaly Detection in Aviation., , , , and . J. Aerosp. Inf. Syst., 20 (4): 181-194 (2023)Multiclass Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model., , and . J. Aerosp. Inf. Syst., 19 (2): 83-97 (February 2022)EEG-Based Estimation of Mental Fatigue: Convergent Evidence for a Three-State Model., , , , , , , and . HCI (16), volume 4565 of Lecture Notes in Computer Science, page 201-211. Springer, (2007)Multiclass Multiple-Instance Learning for Predicting Precursors to Aviation Safety Events., , , and . J. Aerosp. Inf. Syst., 19 (1): 22-36 (January 2022)Kernel PLS-SVC for Linear and Nonlinear Classification., , and . ICML, page 640-647. AAAI Press, (2003)Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events., , , and . CoRR, (2021)Discovery of Precursors to Adverse Events using Time Series Data., , and . SDM, page 639-647. SIAM, (2016)Aircraft anomaly detection using performance models trained on fleet data., , and . CIDU, page 17-23. IEEE, (2012)Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms., , , , , and . J. Aerosp. Inf. Syst., 11 (7): 482 (2014)Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring., , and . J. Aerosp. Comput. Inf. Commun., 6 (7): 464-482 (2009)