Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM
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Cited by:
- Truong Ngoc Cuong & Le Ngoc Bao Long & Hwan-Seong Kim & Sam-Sang You, 2023. "Data analytics and throughput forecasting in port management systems against disruptions: a case study of Busan Port," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 61-89, March.
- Truong Ngoc Cuong & Sam-Sang You & Le Ngoc Bao Long & Hwan-Seong Kim, 2022. "Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
- Mehran Farzadmehr & Valentin Carlan & Thierry Vanelslander, 2023. "Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-44, December.
- Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
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Keywords
container throughput forecasting; convolutional neural network (CNN); deep learning; long short-term memory (LSTM); recurrent neural network (RNN);All these keywords.
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