Justice Awosonviri AkodiaClement K. DzidonuDavid King BoisonPhilip Kisembe...
109-124页
查看更多>>摘要：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.
查看更多>>摘要：Based on The Purpose of Logical Structure School, Logic Structure, Logical Engineering, Theory of Logical Equation Structure Diagrams, and Bionic Logic Theory, this paper proposes five foundational classical theories (The Purpose of the Logical Structure School, Subjective Initiative Structure, Subjective Initiative Structure Engineering, The Life-Giving Logical Equation Structure Diagram, and Bionic Logic), providing directions, methods, and criteria for foundational research on artificial intelligence, robotics and the age of intelligence.
查看更多>>摘要：Based on existing logic theories, this paper proposes nine classical application theories. These consist of Logical Spacetime (which includes Logical Spacetime and Logical Mathematics and Physics), Logical Fields, Logical Networks, Life Communications, Life Reasoning Activities, Life Cycle, Life Data, Life Programming and Life Learning Strategy. The application theories describe a panoramic view of the technological development of their three main subjects (artificial intelligence, robotics and intelligent society) with the strength of one school of thought.
查看更多>>摘要：Continuous groundwater quality monitoring poses significant challenges affecting the environment and public health. Groundwater in Abidjan, specifically from the Continental Terminal (CT), is the primary supply source. Therefore, ensuring safe drinking water and environmental protection requires a thorough evaluation and surveillance of this resource. Our present research evaluates the quality of the CT groundwater in Abidjan using the water quality index (WQI) based on the analytical hierarchy process (AHP). This study also explores the application of machine learning predictions as a time-efficient and cost-effective approach for groundwater resource management. Therefore, three Machine Learning regression algorithms (Ridge, Lasso, and Gradient Boosting (GB)) were executed and compared. The AHP-based WQI results classified 98.98% of samples as "good" water quality, while 0.68% and 0.34% of samples were respectively categorized as "excellent" and "poor" water. Afterward, the prediction performance evaluation highlighted that the GB outperformed the other models with the highest accuracy and consistency (MSE = 0.097, RMSE = 0.300, r= 0.766, r_s= 0.757, and τ = 0.804). In contrast, the Lasso model recorded the lowest prediction accuracy, with an MSE of 148.921, an RMSE of 6.828, and consistency parameters of r= 0.397, r_s = 0.079, and τ= 0.082. Gradient Boosting regression effectively learns nonlinear events and interactions by iteratively fitting new models to errors of previous models, enabling a more realistic groundwater quality prediction. This study provides a novel perspective for improving groundwater quality management in Abidjan, promoting real-time tracking and risk mitigations.
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