Deep Learning Approaches for Vessel Estimated Time of Arrival Prediction: A Case Study on the Saint Lawrence River-Reference-Cited by-同舟云学术

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Deep Learning Approaches for Vessel Estimated Time of Arrival Prediction: A Case Study on the Saint Lawrence River

Published:2023-11-22 Issue: Volume: Page:
ISSN:
Container-title:2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)
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Author:

Bourzak Ismail¹,El Mekkaoui Sara²,Berrado Abdelaziz²,Caron Stéphane³,Benabbou Loubna⁴

Affiliation:

1. Informatique et Génie Université du Québec à Rimouski,Département De Mathématiques,Rimouski,Canada

2. Équipe de Recherche AMIPS École Mohammadia d’Ingénieurs Mohammed V University in Rabat,Rabat,Morocco

3. Xpert Solutions Technologiques Inc.,Lévis,Canada

4. Université du Québec à Rimouski,Département Sciences de la Gestion,Lévis,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10373588/10373590/10373707.pdf?arnumber=10373707

Reference21 articles.

1. Consequences of port congestion on logistics and supply chain in african ports;Gidado;Developing Country Studies,2015

2. Towards an Approach for Long Term AIS-Based Prediction of Vessel Arrival Times

3. Information systems in seaports: a categorization and overview

4. From digitalization to data-driven decision making in container terminals;Leonard,2019

5. Prediction of arrival times and human resources allocation for container terminal

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