Block Chain Financial Transaction Using Artificial Neural Network Deep Learning

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Abstract

In order to adapt to the rapid development of the trading market in the financial industry under the condition of artificial intelligence and solve the unstable problem of capital loss in the process of financial transactions, in this study, firstly, the concept and principle of block chain are summarized, and the problems that block chain can solve are analyzed in an all-round way. Secondly, the concepts and computational principles of artificial neural network and deep learning are analyzed, and they are integrated into block chains for artificial intelligence payment. Finally, the block chain, which integrates artificial neural network and deep learning, is applied to the financial market. The results show that block chains can make use of decentralized networks to simulate virtual consumer payments, and can be used in many financial fields. They can track and check information and prevent goods from being tampered with in the logistics process. Artificial neural network (ANN) can imitate the human brain to process and calculate the captured data by using neurons in both forward and reverse transmission modes. Deep learning can process and recognize graphics and language information by means of computer network. After updating the artificial neural network and the block chain of deep learning, all kinds of transaction needs can be met through appropriate calculation methods, so as to accurately grasp the trading situation of financial market and provide the standard data of artificial intelligence to customers. This shows that the block chain which integrates artificial neural network and deep learning plays a decisive role in financial transactions, can bring huge potential benefits to the financial industry, and make the financial transaction market develop steadily under huge competitive pressure.

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APA

Zhao, B., Wang, R., Cai, Y., & Zhao, E. (2020). Block Chain Financial Transaction Using Artificial Neural Network Deep Learning. In IOP Conference Series: Materials Science and Engineering (Vol. 740). Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/740/1/012161

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