The Best Decision for E-Commerce Funds Transfer Based on Cloud Computing Technique

Dongmei Wang, Guangqin Li and Weilin Xiao

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Cloud computing offers an efficient, scalable, and flexible platform; through techniques and methods proven by it, many complex projects were successfully carried out. It provides consumers the opportunity to interact with previously unknown ease to measure sources and connections to IT-managed services. Online shopping has been considered as one of the commonly used channels for easy shopping in the Internet world and becomes a convenient way to search for products from various places and to make the operation quicker. The digital economy is powered by high-speed Internet connectivity launched by IT as a practical platform for interaction between online shopping and organizations. The platforms for e-commerce have led to extensive international access by local consumers to goods and services. E-commerce has improved productivity in enterprises and has added to the economies in the countries of the business industry. However, e-commerce security is the primary concern to protect details from electronic theft such as credit card or debit card fraud, retailer, or bank leakage of details while a card is not used for purchases online. In this paper, Cloud Computing-Based Multilevel Decision Support System (CCMDSS) has been proposed for secure fund transfer in the e-commerce platform. Furthermore, this study utilizes cryptography techniques and multiple networks to safeguard the security of information and decrease the systematic risks. The experimental results show that the proposed method enhances the efficiency and feasibility and secures the information while transferring the money. Compared with other models, Prediction Ratio validation has a significant improvement, and the error rate is greatly reduced, only 5.99%. Multiple data prove that the comprehensive performance of CCMDSS model ranks among the top among existing models.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9432413

DOI: 10.1155/2022/9432413

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