Abstract: The data of unplanned flight attendant events has characteristics such as diversity and complexity, which pose great challenges to data preprocessing and analysis. This study proposes a preprocessing and analysis method for unplanned flight attendant event data based on Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Firstly, an efficient word vector tool is used to preprocess the raw data, improving its quality and consistency. Then, convolutional neural networks are taken to extract local features of the data, combined with gated loop units to capture dynamic changes in time series, thus achieving effective analysis and prediction of unplanned events in air crew. The results showed that in the 6-class task, the research model exhibited the highest accuracy at 99.24%, the lowest accuracy at 94.24%, and an average accuracy of 98.53%. The highest, lowest, and average accuracies in the 10-class task were 96.63%, 90.17%, and 93.21%, respectively. The performance of the research model was superior to support vector machine, K-nearest neighbor algorithm, and some advanced algorithms. This study provides a more accurate analysis tool for unplanned event data of flight attendants, to assist in the efficiency of aviation emergency event handling and improve aviation safety.

Dongyang Li. “Preprocessing and Analysis Method of Unplanned Event Data for Flight Attendants Based on CNN-GRU”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.11 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0151198

@article{Li2024,
title = {Preprocessing and Analysis Method of Unplanned Event Data for Flight Attendants Based on CNN-GRU},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151198},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151198},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Dongyang Li}
}