A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain

Introduction

The traditional use of business applications always presents a challenging task to deal with its complicated structure and expensive technology. There is a big need of experts to handle such daunting structures or the task scheduled to maintain the system’s installation, configuration, testing, running, securing, and updating procedures. With the help of cloud computing, the problems related to data storage gets easily resolved as the owner of the data just have to pay for what is required on the basis of demand in regard of the software and hardware technology used and it becomes the major responsibility of the experienced vendor to provide the system’s scaling and automatic upgradation facility. In the technological field, the cloud computing is made up of two of its important terms: cloud and computing (Weiwei, Liang & Buyya, 2014). In recent years, Cloud based trading achieves a great success and motivation by using electronic commerce. The cloud provides many services like online shopping, Business-to-Business and electronic data change. The different types of services in cloud provide a lot of opportunities in the field of e-commerce. Nevertheless, cutting-edge e-commerce is facing threats due to counterfeit items. For example, Alibaba Group has a chief counterfeit trouble that fakes still flourish on its virtual shopping center platform (Tsai, Wei, & Rodrigues, 2014). The similar situations also occur at other e-commerce platforms, which includes e-Bay, Amazon, Taobao, Tmall, and AliExpress. It is a venture problem distinguishing the genuine from the fakes, for the reason that maximum digital buying shops, together with Alibaba, do not own personal the products offered on its systems. Current famous answers to the counterfeit hassle is the use of coverage-based tactics, inclusive of signing contract suffering certifications for licensed dealers, and random research mechanisms (Gopinath, Geethu, & Vasudevan, 2015), (Gandhi, Rohan, Harry Liu, Charlie Hu, Lu, Padhye, Yuan, & Zhang, 2015). However, those techniques need a large quantity of exertions workloads and the reaction velocity is a way behind the predicted stage because of the complicated criminal methods and the lengthy execution time (Singh, Aarti, Juneja, & Malhotra, 2015). Limited controls of the products have ended in a severe impediment to the enterprise boom. Neither cutting off nor investigating all suspicious merchandises is a feasible alternative for contemporary e-trade service providers. Addressing this problem, this paper proposes a novel method with building up a deterministic model for suspicious counterfeits predictions.