Predictive modeling of aircraft tyre demand using a hybrid CNN-LSTM architecture

The proposed approach in this paper utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) cell network based deep learning algorithm to forecast the optimal time period for replacing aircraft tyres. The solution compares various optimizers such as Nadam and Adam, and activation functions such as ReLU and Swish, in order to select the best suited model. The primary concern is accuracy, which is evaluated using the Root Mean Squared Error (RMSE) metric. The proposed CNN-LSTM algorithm can predict the required features with a RMSE of 0.3. The model that incorporates the NADAM optimizer and the Swish activation function exhibits a comparatively lower RMSE curve in the graph.