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- W4298151649 abstract "An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Specific air traffic controllers are assigned to solve such conflicts. A controller needs to consider various types of information in order to solve a conflict. The most common and preliminary information is the coordinate position of the involved aircraft. Additionally, a controller has to take into account more information such as flight planning, weather, restricted territory, etc. The most important challenges a controller has to face are: to think about the issues involved and make a decision in a very short time. Due to the increased number of aircraft, it is crucial to reduce the workload of the controllers and help them make quick decisions. A conflict can be solved in many ways, therefore, we consider this problem as a multi-label classification problem. In doing so, we are proposing a multi-label classification model which provides multiple heading advisories for a given conflict. This model we named CRMLnet is based on a novel application of a multi-layer neural network and helps the controllers in their decisions. When compared to other machine learning models, our CRMLnet has achieved the best results with an accuracy of 98.72% and ROC of 0.999. The simulated data set that we have developed and used in our experiments will be delivered to the research community." @default.
- W4298151649 created "2022-10-01" @default.
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- W4298151649 date "2021-09-10" @default.
- W4298151649 modified "2023-09-29" @default.
- W4298151649 title "Multi-label Classification of Aircraft Heading Changes Using Neural Network to Resolve Conflicts" @default.
- W4298151649 doi "https://doi.org/10.48550/arxiv.2109.04767" @default.
- W4298151649 hasPublicationYear "2021" @default.
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