@article{(Open Science Index):https://publications.waset.org/pdf/10012385,
	  title     = {Identification of Vessel Class with LSTM using Kinematic Features in Maritime Traffic Control},
	  author    = {Davide Fuscà and  Kanan Rahimli and  Roberto Leuzzi},
	  country	= {},
	  institution	= {},
	  abstract     = {Prevent abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep Long Short-Term Memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviours far from the expected one, depending on the declared type.},
	    journal   = {International Journal of Marine and Environmental Sciences},
	  volume    = {16},
	  number    = {1},
	  year      = {2022},
	  pages     = {1 - 4},
	  ee        = {https://publications.waset.org/pdf/10012385},
	  url   	= {https://publications.waset.org/vol/181},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 181, 2022},
	}