Application of big data technology platform based on deep learning in smart tax evaluation systemApplication of big data technology platform based on deep learning in smart tax evaluation…Y. Wang

被引:0
|
作者
Ying Wang [1 ]
机构
[1] North China Institute of Aerospace Engineering,
关键词
Deep learning; Big data technology platform; Smart taxation; Evaluation system;
D O I
10.1007/s00500-023-08457-6
中图分类号
学科分类号
摘要
In today’s modern transformation of tax affairs, as information technology and Internet technology are constantly updated and become more advanced, it is necessary to reasonably change the processing flow of tax affairs, such as breaking the time and space constraints to make it more convenient. It is an inevitable need to complete the reform and optimize the business environment through the “release, management and service” policy to achieve technological transformation. The big data technology platform can well accomplish the expected tasks, thus providing systematic support for tax work. In this context, this paper integrates the deep learning technology with the big data technology platform to complete the design of the mobile tax system for taxpayers. It is convenient and fast, and can realize the creation of an intelligent tax evaluation system, so as to carry out tax management, information retrieval, information release, user communication and system management, and can also safely repair the system itself. Through the design experiment, the user-defined query and retrieval function of the system is tested. The results show that the comprehensive query module of the system has good security, and the evaluation system can meet the user's requirements for ease of use and friendly system interface. This paper has completed the construction of smart tax evaluation system by integrating deep learning technology and big data technology platform.
引用
收藏
页码:13917 / 13927
页数:10
相关论文
共 50 条
  • [41] Evaluation of maxout activations in deep learning across several big data domains
    Castaneda, Gabriel
    Morris, Paul
    Khoshgoftaar, Taghi M.
    JOURNAL OF BIG DATA, 2019, 6 (01)
  • [42] Big data solar power forecasting based on deep learning and multiple data sources
    Torres, Jose F.
    Troncoso, Alicia
    Koprinska, Irena
    Wang, Zheng
    Martinez-Alvarez, Francisco
    EXPERT SYSTEMS, 2019, 36 (04)
  • [43] Ecological evolution path of smart education platform based on deep learning and image detection
    Han, Zhongchun
    Xu, Anfeng
    MICROPROCESSORS AND MICROSYSTEMS, 2021, 80
  • [44] Evaluation Model of Operation State Based on Deep Learning for Smart Meter
    Zhao, Qingsheng
    Mu, Juwen
    Han, Xiaoqing
    Liang, Dingkang
    Wang, Xuping
    ENERGIES, 2021, 14 (15)
  • [45] Survey of deep learning based EEG data analysis technology
    Zhong B.
    Wang P.
    Wang Y.
    Wang X.
    Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2024, 58 (05): : 879 - 890
  • [46] The Optimization of Digital Art Teaching Platform Based on Information Technology and Deep Learning
    Liu, Yiying
    Ko, Young Chun
    IEEE ACCESS, 2023, 11 : 107287 - 107296
  • [47] FACE RECOGNITION TECHNOLOGY BASED ON DEEP LEARNING ALGORITHM FOR SMART CLASSROOM USAGE
    Wang, Yonghong
    Choo, Wou Onn
    Wang, Xiaofeng
    JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2023, 18 (06): : 39 - 47
  • [48] Application of deep learning and cloud data platform in college teaching quality evaluation (Publication with Expression of Concern)
    Fan, Peng
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (04) : 5547 - 5558
  • [49] Application of big data adaptive semi-supervised clustering method based on deep learning
    Zheng, Lu
    Ko, Young Chun
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2022, 22 (04) : 1179 - 1193
  • [50] A Method of Vulnerability Assessment Based on Deep Learning and ass Fault Big Data
    Tamura, Yoshinobu
    Sone, Hironobu
    Anand, Adarsh
    Yamada, Shigeru
    2021 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM21), 2021, : 1546 - 1550