Hybridized Multi-Special Decision Finding with Anti-Theft Probabilistic Method in the Improvement of Cloud-Based E-Commerce

In addition to dealing with the dispute between the e-commerce activities of companies and the lack of supplies, the companies had settled, by applying a highly developed cloud technology framework, the difficulties of lack of resources, workforce and necessary technology in e-commerce activities. E-commerce utilizing cloud-based financial instruments is becoming a common strategy for the rise of international growth over the years. Nevertheless, the presence of fake goods on the site endangered the advantages of all investors. Therefore, this paper suggests a Hybridized Multi-special Decision finding with the Anti-Theft Probabilistic (HMDAP) method for making the improvement of the cloud-based model, and it is trained to find fake goods. A multi-special decision finding is used to address the issues and the lack of e-commerce facilities by creating a programming environment for e-commerce provided by the cloud computing system. The Anti-Theft Probabilistic method is used to track fake goods and use the Carlo method to predict possible stolen data in e-commerce. HMDAP enables businesses to reduce expenses through the successful delivery of e-commerce activities and provides assumptions of unsafe data in e-commerce.