With the rapid development of numerous airlines, various trend designs have been implemented to develop cockpit interiors for pilots. When communicating with most pilots, they express their emotional feelings about the cockpit. The performance-enhanced cockpit provides the pilot with a better emotional experience, further enhancing the comfort and pleasure of driving. Therefore, it is necessary to provide a cockpit with good performance. Therefore, this paper proposes a novel Neural Network-based Balanced Optimization Algorithm (NN-EOA) for cockpit emotion recognition. The proposed NN-EOA technique simulates quantitative computation with high accuracy and minimizes the error rate during evaluation. Here, images of the interior cockpit design were assessed on four emotional terms, namely, user-friendly (interactive), precise (precise), traditional (traditional), and neat (tidy). Finally, experimental results are performed on various parameters, namely, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) for various techniques. From the experimental evaluations, it can be seen that the proposed NN-EOA provides high agreement rates in the experimental group with the smallest MAE, MAPE, and RMSE.

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