AI RESEARCH
A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
arXiv CS.LG
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This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, time gaps, and turn angles. To address this limitation, we propose a novel quality metric call