Segui
Mohammad Etemad
Titolo
Citata da
Citata da
Anno
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
732018
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
422019
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
422018
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
332021
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
312019
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
282020
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
272019
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
212020
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
192020
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
192020
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
162022
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
122021
Transportation modes classification using feature engineering
M Etemad
arXiv preprint arXiv:1807.10876, 2018
112018
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
102022
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
82021
On feature selection and evaluation of transportation mode prediction strategies
M Etemad, AS Junior, S Matwin
arXiv preprint arXiv:1808.03096, 2018
72018
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
52022
Novel algorithms for trajectory segmentation based on interpolation-based change detection strategies
M Etemad
52020
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
42022
Destination port detection for vessels: An analytic tool for optimizing port authorities resources
L Eljabu, M Etemad, S Matwin
Dalhousie University Halifax, 2021
42021
Il sistema al momento non puň eseguire l'operazione. Riprova piů tardi.
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