DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION

12 citations as recorded by crossref.

1. Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN X. Han et al. 10.3390/su13158162
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4. Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics Y. Oh & S. Kim 10.1109/TASE.2023.3329041
5. AOMC: An Adaptive Point Cloud Clustering Approach for Feature Extraction Z. Zhang et al. 10.1155/2022/3744086
6. Maritime Anomaly Detection for Vessel Traffic Services: A Survey T. Stach et al. 10.3390/jmse11061174
7. Artificial Intelligence in Maritime Anomaly Detection: A Decadal Bibliometric Analysis (2014–2024) A. Thakur et al. 10.1007/s40032-025-01169-w
8. A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data M. Syed & I. Ahmed 10.3390/s23146400
9. Multivariate hierarchical DBSCAN model for enhanced maritime data analytics N. Newaliya & Y. Singh 10.1016/j.datak.2024.102282
10. Integrating GPU-Accelerated for Fast Large-Scale Vessel Trajectories Visualization in Maritime IoT Systems M. Liang et al. 10.1109/TITS.2024.3521050
11. Improved Vessel Trajectory Prediction Model Based on Stacked-BiGRUs Y. Xu et al. 10.1155/2022/8696558
12. Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review J. Restrepo-Arias et al. 10.3390/s22103708