Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006

2024/08/26 by Messaoud Djeddou, Aouatef Hellal, Djeddou, Messaoud +7
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Customer churn and segmentation #FOS: Computer and information sciences #Impact of AI and Big Data on Business and Society #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2408.14626

openalex publication_date 2024/08/26 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28

Abstract

This study explores the development of a hybrid deep convolutional neural network (DCNN) model enhanced by autoencoders and data augmentation techniques to predict critical heat flux (CHF) with high accuracy. By augmenting the original input features using three different autoencoder configurations, the model's predictive capabilities were significantly improved. The hybrid models were trained and tested on a dataset of 7225 samples, with performance metrics including the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and normalized root-mean-squared error (NRMSE) used for evaluation. Among the tested models, the DCNN₃F-A2 configuration demonstrated the highest accuracy, achieving an R2 of 0.9908 during training and 0.9826 during testing, outperforming the base model and other augmented versions. These results suggest that the proposed hybrid approach, combining deep learning with feature augmentation, offers a robust solution for CHF prediction, with the potential to generalize across a wider range of conditions.

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