Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks  

作　　者：Abdullah Ali Salamai Ather Abdulrahman Ageeli El-Sayed M.El-kenawy 

机构地区：[1]Community college,Jazan University,Jazan,Kingdom of Saudi Arabia [2]Department of Communications and Electronics,Delta Higher Institute of Engineering and Technology,Mansoura,35111,Egypt

出　　处：《Computers, Materials & Continua》2022年第3期5091-5106,共16页计算机、材料和连续体（英文）

摘　　要：E-commerce refers to a system that allows individuals to purchase and sell things online.The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase.They will purchase the item online and have it delivered to their home within a few days.The goal of this research was to develop machine learning algorithms that might predict e-commerce platformsales.A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search(SFS)based on a Guided Whale Optimization Algorithm(WOA)to optimize the parameter weights of the Bidirectional Recurrent Neural Networks(BRNN).Furthermore,a time series dataset is tested in the experiments of e-commerce demand forecasting.Finally,the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),and Genetic Algorithm(GA).A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance(ANOVA)test applied to confirm the performance of the proposed ensemble model.The proposed Algorithm achieved a root mean square error of RMSE(0.0000359),Mean(0.00003593)and Standard Deviation(0.000002162).

关 键 词：Neural networks E-COMMERCE forecasting risk management machine learning 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]