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The difficulties in analyzing large and extensive systems necessitate the use of efficient machine-learning tools to identify unknown system anomalies in order to avoid critical problems and ensure high reliability. Given that data logged by a system include unknown anomalies, anomaly identification models aim to simultaneously identify the time of occurrence and the features that contributed to these anomalies. To maximize accuracy, it is important to utilize the data as well as the domaindoi:10.36001/ijphm.2020.v11i2.2924 fatcat:7mga3ji7wffrdebbslou3vewme