Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

Abstract

With the rapid development of information technology, decision support systems that can assist business managers in making scientific decisions have become the focus of research. At present, there are not many related studies, but from the brand marketing level, there are not many studies combining smart technology. Based on computer vision technology and parallel computing algorithms, this paper launches an in-depth study of brand marketing decision support systems. First, use computer vision technology and Viola-Jones face detection framework to detect consumers’ faces, and use the classic convolutional neural network model AlexNet for gender judgment and age prediction to analyze consumer groups. Then, use parallel computing to optimize the genetic algorithm to improve the running speed of the algorithm. Design the brand marketing decision support system based on the above technology and algorithm, analyze the relevant data of the L brand, and divide the functional structure of the system into three parts: customer market analysis, performance evaluation, and demand forecasting. The ROC curve of the Viola-Jones face detection framework shows its superior performance. After 500 iterations of the AlexNet model, the verification set loss of the network is stable at 1.8, and the accuracy of the verification set is stable at 38%. Parallel genetic algorithms run 1.8 times faster than serial genetic algorithms at the lowest and 9 times faster at the highest. The minimum prediction error is 0.17%, and the maximum is 2%, which shows that the system can make accurate predictions based on previous years’ data. Computer vision is a technique that converts still image or video data into a decision or a new representation. All such transformations are done to accomplish a specific purpose. Therefore, a brand marketing decision support system based on computer vision and parallel computing can help managers make scientific decisions, save production costs, reduce inventory pressure, and enhance the brand’s competitive advantage.

Research on Enterprise Digital Precision Marketing Strategy Based on Big Data

“…Being "user-centered" has always been the focus of major e-commerce brands [1][2][3]. It is becoming more and more important to mine the ow value of brand crowd assets and conduct targeted and accurate operations [4][5][6][7][8]. erefore, for major brand owners, in addition to paying attention to the rise of transaction volume, how to precipitate and consume brand group assets and make e cient use of user stock value, so as to drive the breakthrough of the boundary of the incremental market, has become the core of common concern of major e-commerce platforms and many brands [9][10][11][12][13][14].…”

Retracted: Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”