Multi-Scale Deep Learning-Based University Financial System: Hardware Design and Database Integration

被引：0
作者：
Feng, Ruiji [1 ]
机构：
[1] School of Economics and Management, Inner Mongolia University of Technology, Hohhot
来源：
Journal of Combinatorial Mathematics and Combinatorial Computing | 2024年 / 120卷
关键词：
College finance; Deep learning; Financial management; Quality improvement; Risk management;
D O I：
10.61091/jcmcc120-30
中图分类号：
学科分类号：
摘要：
With the increasing scale of college enrollment and the increasing complexity of college teaching management, college finance department should innovate the traditional financial management mode while adapting to the reform of teaching management, and make use of the openness and real-time characteristics of Internet to improve the quality of college financial management and reduce the risk of college financial management. To this end, this paper designs a university financial system based on multi-scale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed in this time has the most categories of financial information parameters extracted. © 2024 the Author(s), licensee Combinatorial Press.
引用
收藏
页码：337 / 344
页数：7
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