Research on Systematic Risk Early Warning System Based on Machine Learning Technology: A Case Study of Marine Economy

被引:0
|
作者
Zhao, Jinlou [1 ,2 ]
Yu, Jiannan [1 ,2 ]
Wang, Xuzhang [1 ,2 ]
机构
[1] Harbin Engn Univ, Sch Econ & Management, Harbin, Peoples R China
[2] Hunan Vocat Inst Technol, Xiangtan, Peoples R China
关键词
Machine learning technology; early warning of systemic risk; marine economy; sustainable development;
D O I
10.2112/JCR-S1108-045.1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the rapid development of information technology, the value contained in big data has attracted more and more attention. Conducting efficient analysis of big data has become an important topic. Machine learning is one of the commonly used methods for data analysis. The traditional machine learning algorithm is often designed as the way of offline batch training. However, this method is not suitable for data sets with massive scale and continuous growth in the big data environment. Transforming the traditional machine learning algorithm so that it can better apply to the big data environment has become a research hotspot. With the rapid development of economy and society and the continuous progress of science and technology, the public's understanding of ocean functions is gradually deepened, the demand for marine products and services is increasing every day, and the economic and social benefits of the ocean are continuously rising. In this context, great importance should be given to the examination of marine resources and environment, the development and utilization of work, the constant adjusting of the ocean economic development policy, and the variety of comprehensive marine management measures to ensure the sustainable development of implementation of marine programs. The current mainstream of machine learning technology based on the marine economy of systemic risk early warning system for research is proposed here.
引用
收藏
页码:230 / 233
页数:4
相关论文
共 50 条
  • [21] Research on Early Warning Technology and System of Pneumoconiosis Hazards
    Zhao, Zheng
    WIENER KLINISCHE WOCHENSCHRIFT, 2024, 136 : S465 - S465
  • [22] Research on Technology Early-Warning System Based on Dynamic Information Monitoring
    汪雪锋
    朱东华
    刘嵩
    刘佳
    Journal of Beijing Institute of Technology, 2009, 18 (01) : 121 - 126
  • [23] Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
    Qin, Xuebin
    Huo, Jingtao
    Zhang, Jing
    Liu, Lang
    Wang, Pai
    Dong, Lihong
    APPLIED SCIENCES-BASEL, 2023, 13 (01):
  • [24] Research on technology early-warning system based on dynamic information monitoring
    Wang, Xue-Feng
    Zhu, Dong-Hua
    Liu, Song
    Liu, Jia
    Journal of Beijing Institute of Technology (English Edition), 2009, 18 (01): : 121 - 126
  • [25] Research on early warning of agricultural credit and guarantee risk based on deep learning
    Chao Zhang
    Zhenyu Wang
    Jie Lv
    Neural Computing and Applications, 2022, 34 : 6673 - 6682
  • [26] Research on early warning of agricultural credit and guarantee risk based on deep learning
    Zhang, Chao
    Wang, Zhenyu
    Lv, Jie
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (09): : 6673 - 6682
  • [27] Risk Early Warning of Distribution Power System Based on Data Mining Technology
    Liu, Keyan
    Wu, Xinzhong
    Shi, Chen
    PROCEEDINGS OF 2017 CHINA INTERNATIONAL ELECTRICAL AND ENERGY CONFERENCE (CIEEC 2017), 2017, : 40 - 45
  • [28] EARLY WARNING SYSTEM OF EXTERNAL SUSTAINABILITY OF AN ECONOMY: CASE OF UKRAINE
    Bazhenova, O.
    Chornodid, I.
    Yarmolenko, Y.
    Golubev, O.
    FINANCIAL AND CREDIT ACTIVITY-PROBLEMS OF THEORY AND PRACTICE, 2021, 4 (39): : 503 - 511
  • [29] Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review
    Muralitharan, Sankavi
    Nelson, Walter
    Di, Shuang
    McGillion, Michael
    Devereaux, P. J.
    Barr, Neil Grant
    Petch, Jeremy
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (02)
  • [30] Machine learning implementation for a rapid earthquake early warning system
    Sihombing, F.
    Torbol, M.
    LIFE-CYCLE ANALYSIS AND ASSESSMENT IN CIVIL ENGINEERING: TOWARDS AN INTEGRATED VISION, 2019, : 2769 - 2774