Efficient Prediction of Sales during Festival Times in an Electronic Showroom Using Novel Deep Belief Network Compared Over Alexnet with Improved Accuracy

Abstract

Aim: The aim of this paper is the efficient prediction of sales during festival times in an electronic showroom using a Deep Belief Network compared to AlexNet with improved accuracy. Materials and Methods: Deep Belief Network (N=10) and AlexNet algorithm (N=10) is the iteration for different times in predicting the accuracy percentage for accidents that happened. Two sample groups are considered and tested, G-power is a calculation that contains two different groups, alpha (0.05), and power (80%). Results: It was observed that the Deep Belief Network algorithm obtains an accuracy of 83.63% and the Novel Deep Belief Network has 74.12%. This NDBN appears to have a better significance of p=0.035 than the ResNet, that is p<0.05 using independent T-test analysis. Conclusion: The result proves that the Novel Deep Belief Network approaches to predicting the best retail sales store prediction have higher accuracy than the AlexNet algorithm.