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Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

Abdullah Ali Salamai1,*, Ather Abdulrahman Ageeli1, El-Sayed M. El-kenawy2

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

* Corresponding Author: Abdullah Ali Salamai. Email: email

(This article belongs to the Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 70(3), 5091-5106.


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 platform sales. 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).


Cite This Article

APA Style
Salamai, A.A., Ageeli, A.A., El-kenawy, E.M. (2022). Forecasting e-commerce adoption based on bidirectional recurrent neural networks. Computers, Materials & Continua, 70(3), 5091-5106.
Vancouver Style
Salamai AA, Ageeli AA, El-kenawy EM. Forecasting e-commerce adoption based on bidirectional recurrent neural networks. Comput Mater Contin. 2022;70(3):5091-5106
IEEE Style
A.A. Salamai, A.A. Ageeli, and E.M. El-kenawy "Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks," Comput. Mater. Contin., vol. 70, no. 3, pp. 5091-5106. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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