Distributed Systems Framework for Packaging Design Innovation using Visual Perception and Algorithm Optimization

Abstract

Food consumed by humans is becoming more and more customized to fit each person’s unique demands, with a vast array of product labels readily available. As a result, many companies are beginning to concentrate on enhancing the practicality of contemporary packaging. Throughout the lifetime of a user-product engagement, sensory paradigms and emotional responses may shift. Traditional product packaging layout is largely based on the designer’s emotional imagination and prior events; however, it is limited by uncontrollable content and a lack of expert advice; most earlier studies involving mental analysis of images focused on predicting the most prevalent viewers feelings. There are situations when an image’s general impression is insufficient for practical purposes since the emotions it arouses are very subjective and differ from viewer to viewer. The proposed methodology uses Genetic Algorithm based Multi-Layer Ant Colony Optimization for analysing visual perception and emotion perception to identify the senses of human being. A significant set of images called Image-Emotion-Social-Net is utilized to assess categorized and multivariate attitude representations. The collection, which comes from Flickr, has more than a million photos uploaded by more than 9,000 members. The results of this dataset’s research indicate that the suggested approach performs better in personalized emotional identification than several contemporary methods. In comparison to other current methods, the experimental findings demonstrate that the suggested approach obtains a high packaging layout excellence rate of 95.1%, a performance success rate of 98.5%, and a mean square error rate of 1.5%.