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Since there is no annotated dataset for this purpose, in this study, we apply an unsupervised approach. Our method benefits from the unsupervised learning feature of autoencoders. We utilize the reconstruction error as a signal for anomaly detection. For this purpose, we estimate the probability density function of the reconstruction error and find different levels of abnormality based on statistical attributes of the density of error. Our results demonstrate the effectiveness of this approach for localizing irregular patterns in the trajectory of vessel movements.<\/jats:p>","DOI":"10.1515\/jisys-2022-0270","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T07:11:01Z","timestamp":1700032261000},"source":"Crossref","is-referenced-by-count":3,"title":["Anomaly detection for maritime navigation based on probability density function of error of reconstruction"],"prefix":"10.1515","volume":"32","author":[{"given":"Zahra","family":"Sadeghi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Institute for Big Data Analytics, Dalhousie University , Halifax , B3H 1W5 , Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Institute for Big Data Analytics, Dalhousie University , Halifax , B3H 1W5 , Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"2025120517213996148_j_jisys-2022-0270_ref_001","doi-asserted-by":"crossref","unstructured":"Santhosh KK, Dogra DP, Roy PP. 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