United States Department of Transportation
i
Novel Big Data and Artificial Intelligence Analytics Methods for Tracking and Monitoring Maritime Traffics
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2023-12-01
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[PDF-2.28 MB]
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Edition:Final Research Report
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Abstract:The past decade has seen an explosion of machine learning research and applications; especially, deep learning methods have enabled key advances in many application domains, such as computer vision, speech processing, and in maritime vessel trajectories. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyper-parameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset. The purpose of the field of Automated Machine Learning (AutoML) is to make these decisions in a data-driven, objective, and automated way. That is, the user simply provides data, and the AutoML system automatically determines the approach that performs best for this particular application. Therefore, the purpose of this project is to develop an AutoML model for monitoring and tracking maritime traffic, a state-of-the-art machine learning approach that is accessible to users of maritime historical and real-time databases interested in applying machine learning but do not have the resources to learn about the methods and technologies behind it in detail. This can be seen as a democratization of machine learning where the state-of-the-art machine learning is at every maritime database user’s fingertip.
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