Classification of Operational Records in Aviation Using Deep Learning Approaches

• URL: http://arxiv.org/abs/2501.01222v2
• Date: Mon, 17 Feb 2025 07:49:58 GMT
• Title: Classification of Operational Records in Aviation Using Deep Learning Approaches
• Authors: Aziida Nanyonga, Graham Wild,
• Abstract summary: This study evaluates the performance of four different models for DP (deep learning) in a classification task involving Commercial, Military, and Private categories. Among the models, BLSTM achieved the highest overall accuracy of 72%, demonstrating superior performance in stability and balanced classification. CNN and sRNN exhibited lower accuracies of 67% and 69%, with significant misclassifications in the Private class.
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• Abstract: Ensuring safety in the aviation industry is critical, even minor anomalies can lead to severe consequences. This study evaluates the performance of four different models for DP (deep learning), including: Bidirectional Long Short-Term Memory (BLSTM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Simple Recurrent Neural Networks (sRNN), on a multi-class classification task involving Commercial, Military, and Private categories using the Socrata aviation dataset of 4,864 records. The models were assessed using a classification report, confusion matrix analysis, accuracy metrics, validation loss and accuracy curves. Among the models, BLSTM achieved the highest overall accuracy of 72%, demonstrating superior performance in stability and balanced classification, while LSTM followed closely with 71%, excelling in recall for the Commercial class. CNN and sRNN exhibited lower accuracies of 67% and 69%, with significant misclassifications in the Private class. While the results highlight the strengths of BLSTM and LSTM in handling sequential dependencies and complex classification tasks, all models faced challenges with class imbalance, particularly in predicting the Military and Private categories. Addressing these limitations through data augmentation, advanced feature engineering, and ensemble learning techniques could enhance classification accuracy and robustness. This study underscores the importance of selecting appropriate architectures for domain specific tasks

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