Predicting transportation modes of gps trajectories using feature engineering and noise removal M Etemad, A Soares Júnior, S Matwin Canadian conference on artificial intelligence, 259-264, 2018 | 76 | 2018 |
A Network Abstraction of Multi-vessel Trajectory Data for Detecting Anomalies. I Varlamis, K Tserpes, M Etemad, AS Júnior, S Matwin EDBT/ICDT Workshops 2019, 2019 | 43 | 2019 |
Robust image watermarking scheme using bit-plane of hadamard coefficients E Etemad, S Samavi, SM Reza Soroushmehr, N Karimi, M Etemad, ... Multimedia Tools and Applications 77, 2033-2055, 2018 | 42 | 2018 |
Building navigation networks from multi-vessel trajectory data I Varlamis, I Kontopoulos, K Tserpes, M Etemad, A Soares, S Matwin GeoInformatica 25, 69-97, 2021 | 37 | 2021 |
A Trajectory Segmentation Algorithm Based on Interpolation-based Change Detection Strategies. M Etemad, AS Júnior, A Hoseyni, J Rose, S Matwin EDBT/ICDT Workshops, 58, 2019 | 32 | 2019 |
SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels M Etemad, A Soares, E Etemad, J Rose, L Torgo, S Matwin GeoInformatica, 2020 | 29 | 2020 |
VISTA: A visual analytics platform for semantic annotation of trajectories A Soares, J Rose, M Etemad, C Renso, S Matwin Proceedings of the 22nd international conference on extending database …, 2019 | 27 | 2019 |
Wise sliding window segmentation: A classification-aided approach for trajectory segmentation M Etemad, Z Etemad, A Soares, V Bogorny, S Matwin, L Torgo Advances in Artificial Intelligence: 33rd Canadian Conference on Artificial …, 2020 | 21 | 2020 |
Uncovering vessel movement patterns from AIS data with graph evolution analysis. E Carlini, VM de Lira, AS Júnior, M Etemad, BB Machado, S Matwin EDBT/ICDT Workshops 1, 2020 | 20 | 2020 |
Understanding evolution of maritime networks from automatic identification system data E Carlini, VM de Lira, A Soares, M Etemad, B Brandoli, S Matwin GeoInformatica, 1-25, 2022 | 19 | 2022 |
Using deep reinforcement learning methods for autonomous vessels in 2d environments M Etemad, N Zare, M Sarvmaili, A Soares, B Brandoli Machado, S Matwin Advances in Artificial Intelligence: 33rd Canadian Conference on Artificial …, 2020 | 19 | 2020 |
Spatial clustering method of historical ais data for maritime traffic routes extraction L Eljabu, M Etemad, S Matwin 2022 IEEE International Conference on Big Data (Big Data), 893-902, 2022 | 12 | 2022 |
Anomaly detection in maritime domain based on spatio-temporal analysis of ais data using graph neural networks L Eljabu, M Etemad, S Matwin 2021 5th International Conference on Vision, Image and Signal Processing …, 2021 | 12 | 2021 |
Transportation modes classification using feature engineering M Etemad arXiv preprint arXiv:1807.10876, 2018 | 11 | 2018 |
Developing an advanced information system to support ballast water management M Etemad, A Soares, P Mudroch, S A. Bailey, S Matwin Management of Biological Invasions 13, 2021 | 9 | 2021 |
On feature selection and evaluation of transportation mode prediction strategies M Etemad, AS Junior, S Matwin arXiv preprint arXiv:1808.03096, 2018 | 8 | 2018 |
Spatial clustering model of vessel trajectory to extract sailing routes based on ais data L Eljabu, M Etemad, S Matwin International Journal of Computer and Systems Engineering 16 (10), 482-492, 2022 | 6 | 2022 |
Destination port detection for vessels: An analytic tool for optimizing port authorities resources L Eljabu, M Etemad, S Matwin Dalhousie University Halifax, 2021 | 5 | 2021 |
Novel algorithms for trajectory segmentation based on interpolation-based change detection strategies M Etemad | 5 | 2020 |
Discovering gateway ports in maritime using temporal graph neural network port classification D Altan, M Etemad, D Marijan, T Kholodna arXiv preprint arXiv:2204.11855, 2022 | 4 | 2022 |