首页|Research from University of Transport Technology Broadens Understanding of Machi ne Learning (Machine learning-based model for predicting arrival time of contain er ships)
Research from University of Transport Technology Broadens Understanding of Machi ne Learning (Machine learning-based model for predicting arrival time of contain er ships)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting from Hanoi, Vietnam, by NewsRx journalists, research stated, "Forecasting container ship arrival times i s challenging, requiring a thorough analysis for accuracy."The news editors obtained a quote from the research from University of Transport Technology: "This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 in put variables and 1 output variable (arrival time), ML models are constructed. T he Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge locati on, dispatch day of the week, and dispatch week. The ranking of ML performance f or predicting the arrival time of container ships can be arranged in descending order as GB-PSO > XGB > RF > RF-PSO > GB > KNN > SVR."
University of Transport TechnologyHano iVietnamAsiaCyborgsEmerging TechnologiesMachine Learning
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