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Takase, T., Oyama, S. and Kurihara, M. (2018) Effective Neural Network Training with Adaptive Learning Rate Based on Training Loss. Neural Networks, 101, 68-78.
https://doi.org/10.1016/j.neunet.2018.01.016

has been cited by the following article:

• TITLE: Application of Random Search Methods in the Determination of Learning Rate for Training Container Dwell Time Data Using Artificial Neural Networks

  AUTHORS: Justice Awosonviri Akodia, Clement K. Dzidonu, David King Boison, Philip Kisembe

  KEYWORDS: Container Dwell Time Prediction, Artificial Neural Networks (ANNs), Learning Rate Optimization, RandomizedSearchCV Algorithm and Port Operations Efficiency

  JOURNAL NAME: Intelligent Control and Automation, Vol.15 No.4, September 29, 2024

  ABSTRACT: Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for training Artificial Neural Networks (ANNs) has remained a challenging task due to the diverse sizes, complexity, and types of data involved. Design/Method/Approach: This research used a RandomizedSearchCV algorithm, a random search approach, to bridge this knowledge gap. The algorithm was applied to container dwell time data from the TOS system of the Port of Tema, which included 307,594 container records from 2014 to 2022. Findings: The RandomizedSearchCV method outperformed standard training methods both in terms of reducing training time and improving prediction accuracy, highlighting the significant role of the constant learning rate as a hyperparameter. Research Limitations and Implications: Although the study provides promising outcomes, the results are limited to the data extracted from the Port of Tema and may differ in other contexts. Further research is needed to generalize these findings across various port systems. Originality/Value: This research underscores the potential of RandomizedSearchCV as a valuable tool for optimizing ANN training in container dwell time prediction. It also accentuates the significance of automated learning rate selection, offering novel insights into the optimization of container dwell time prediction, with implications for improving port efficiency and supply chain operations.