Flight Data

Flight data analysis is crucial for improving aviation safety, efficiency, and operational insights. Current research focuses on leveraging machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as LSTMs), and hybrid models combining these with traditional methods, to predict flight delays, detect anomalies, and even forecast potential accidents. These advancements are enabling more accurate predictive maintenance, improved situational awareness (e.g., violence detection at airports), and enhanced understanding of complex flight dynamics, ultimately contributing to safer and more efficient air travel.