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Semi-supervised and unsupervised machine learning methods for sea traffic anomaly detection

Autor: Venskus, Julius

The increasing intensity in maritime traffic pushes the requirement for a better prevention-oriented incident management system. The methods developed and studied in the dissertation improve Maritime situation awareness (MSA) by improving detection o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a8614d00ae2227e5dd1bec6c550ca55f
https://repository.vu.lt/VU:ELABAETD99652782&prefLang=en_US

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic.

Autor: VENSKUS, Julius¹ julius.venskus@mii.stud.vu.lt, TREIGYS, Povilas^1,2 povilas.treigys@mii.vu.lt, BERNATAVIČIENĖ, Jolita¹ jolita.bernataviciene@mii.vu.lt, MEDVEDEV, Viktor¹ viktor.medvedev@mii.vu.lt, VOZNAK, Miroslav³ miroslav.voznak@vsb.cz, KURMIS, Mindaugas⁴ mindaugask01@gmail.com, BULBENKIENĖ, Violeta⁴ bulbenkiene@gmail.com

Publikováno v: Informatica. 2017, Vol. 28 Issue 2, p359-374. 16p.

Detecting Maritime traffic anomalies with long-short term memory recurrent neural network

Autor: Venskus, Julius, Treigys, Povilas, Bernatavičienė, Jolita, Markevičiūtė, Jurgita

Publikováno v: 11th international workshop on data analysis methods for software systems (DAMSS 2019), Druskininkai, Lithuania, November 28-30, 2019 / Lithuanian Computer Society, Vilnius University Institute of Data Science and Digital Technologies, Lithuanian Academy of Sciences, Vilnius : Vilnius University Press, 2019, p. 89

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::08d3e6e572ea4881e14afda03f46dc55
https://repository.vu.lt/VU:ELABAPDB55161168&prefLang=en_US

Confidence and Prediction Intervals Usage in Maritime Traffic Awareness Evaluation Using LSTM Deep Neural Networks.

Autor: Venskus, Julius, Jurkus, Robertas, Treigys, Povilas

Publikováno v: Vilnius University Proceedings; 2023, Vol. 39, p95-96, 2p