Product Appearance Aesthetics in Industrial Design Based on Variational Onsager Neural Network Optimized with Osprey Optimization Algorithm

Abstract

Aesthetics are significant because they may elicit emotions, induce feelings of pleasure or desire, and convey an item's perceived worth and desirability. A well-designed and visually appealing product might stick apart in a competitive market and capture the interest of customers. In contrast, a badly constructed product may be disregarded or considered as inferior quality, regardless of its real functioning. In this manuscript, Product Appearance Aesthetics in Industrial Design Based on Variational Onsager Neural Network Optimized with Osprey Optimization Algorithm (PAAID-VONN-OOA) is proposed. The input image is collected from the Product Design Aesthetic Database. Then the images are pre-processing using Master-Slave Adaptive Notch Filter (MSANF) to remove the noise. The pre-processed image is given into the Variational Onsager Neural Networks which is used for Product Appearance Aesthetics in Industrial Design. In general, the Variational Onsager Neural Network does no express adapting optimization techniques to determine ideal parameters to assure precise prediction. Therefore, it is proposed to utilize the Osprey Optimization Algorithm enhancement Variational Onsager Neural Network for Product Appearance Aesthetics in Industrial Design. The proposed PAAID-VONN-OPA method is implemented on python. Then, the performance of proposed technique is compared with other existing techniques. The proposed technique attains 16.28%, 30.78% and 25.29% higher accuracy, 29.13%, 18.47%, and 31.69% higher precision, 28.96%, 33.21% and 23.89% higher specificity comparing with the existing methods such as An Aesthetic Measurement Approach for Evaluating Product Appearance Design (PAAID-ETT)Research on Modern Book Packaging Design under Aesthetic Evaluation Based on Deep Learning Model (PAAID-SVM), and Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning (PAAID-DCNN).