Prediction of Warning Level in Aircraft Accidents
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This work focuses on the evaluation of risk and safety in the civil aviation industry. The aim of the work is to study the performance of different classification algorithms on accident reports of the Federal Aviation Administration (FAA) accident/incident data system database. The work analyses the number of accident data records for all categories of aviation between the years of 1950 to 2012. The classification algorithms such as DT, KNN, SVM, NN and NB are used to predict the warning level of the component as the class attribute. The rules are proven in terms of their accuracy, and these results are seen to be very meaningful. This study also proves that the NB classifiers will perform better than other classifiers.
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(1)
This work focuses on the evaluation of risk and safety in the civil aviation industry. The aim of the work is to study the performance of different classification algorithms on accident reports of the Federal Aviation Administration (FAA) accident/incident data system database. The work analyses the number of accident data records for all categories of aviation between the years of 1950 to 2012. The classification algorithms such as DT, KNN, SVM, NN and NB are used to predict the warning level of the component as the class attribute. The rules are proven in terms of their accuracy, and these results are seen to be very meaningful. This study also proves that the NB classifiers will perform better than other classifiers.
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