Kendini tekrarlayan derin sinir ağlarının öznitelik seçim yöntemleri ile iyileştirilmesi ve zaman serisi olarak ele alınan otomatik tanımlama sistemi verilerinde kullanımı.

Automatic Identification System (AIS) is an observation and analysis system that has become compulsory nowadays due to the risks of maritime transportation such as collision, fire, and spillage of hazardous or polluting substances. In the literature, we can see the applications of basic mathematical models, statistical models and machine learning algorithms using AIS data in order to detect these dangers in advance and to make controlled and safe travel of ships. In this study, AIS data have been evaluated as time series, and accuracy comparisons have been made by being developed different models with Autoregressive Integrated Moving Average, Multilayer Perceptron (MLP) and Deep Recurrent Neural Networks (DRNN) beside traditional route estimation model. In addition, feature selection techniques have been weighted in MLP and RDNN models, and new algorithms have been proposed with these improving. Relief, Pearson's Correlation, Gain Ratio and Information Gain (IG) methods were used to compare the accuracy of the route and collision estimations. In order to be used in these accuracy tests, AIS data related into certain times of Çanakkale Strait and Marmara Sea were used. The results showed that all the approaches were close and high accuracy due to the linear movement of the ships in Çanakkale Strait. On the other hand, it has been observed that the best approach in the Marmara Sea was the improved DRNN with IG due to its irregular structure. [ABSTRACT FROM AUTHOR]