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BRIEF RESEARCH REPORT article

Front. Blockchain
Sec. Financial Blockchain
Volume 5 - 2022 | doi: 10.3389/fbloc.2022.1027543

Deep learning model-based payment terminal development and efficient recognition

  • 1Shanghai Lixin University of Accounting and Finance, China
Provisionally accepted:
The final, formatted version of the article will be published soon.

As the most widely used payment method at this stage, mobile payment is more and more closely related to the blockchain economy. Traditional methods lack a certain degree of accuracy. This research proposes a feature-based and sequential-based Bilateral AM (BAM) and Convolutional Neural Network (CNN)-gated recurrent unit for the development and application of mobile payment and blockchain economy (Gated Recurrent Unit, GRU) hybrid model (BAM-CNN-GRU), select 5 feature parameters with high correlation with the blockchain for multivariate prediction. The introduction of BAM can automatically quantify the correlation between the input variables and the blockchain, and strengthen the expression of historical key information on the predicted output; the introduction of CNN can extract high-dimensional features that reflect the non-stationary dynamic changes of the blockchain. The proposed hybrid model achieves good results in both single-step and multi-step long-term series and multivariate input blockchain prediction. Compared with the other six methods, MAE is reduced by 75.45%, 64.74%, 62.84%, respectively. 59.41%, 45.54%, 44.16%. Compared with the BAM-GRU model, the CNN-GRU model, the GRU model, the LSTM model, the support vector machine SVM model and the BP model, the prediction accuracy of the hybrid model has been greatly improved, and it has a broader application prospect. This study provides theoretical support for BAM-CNN-GRU hybrid model in the development and application of mobile payment terminal and blockchain economy.

Keywords: BAM-CNN-GRU, mobile payment, Blockchain, Model Comparison, Prediction model

Received:25 Aug 2022; Accepted: 24 Oct 2022.

Copyright: © 2022 Mo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Tianyu Mo, Shanghai Lixin University of Accounting and Finance, Shanghai, China