Quantitative System Modeling of Financial Scenario Uncertainty based on Deep Learning in the Context of Algorithm Optimization
Ying He · 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · 2022
Quantitative system modeling of financial scenario uncertainty based on deep learning in the context of algorithm optimization is implemented in the paper. For example, big data technology and artificial intelligence technology can be used to supervise its financial activities by setting risk indicators, collect data in its financial activities in the real time, conduct comprehensive analysis of Internet financial transaction risks, and analyze and determine whether the risk is not. Beyond indicators make the core point of our algorithm, and with this idea, the deep learning in the context of algorithm optimization is designed. The decoder of the core existing point cloud completion network directly uses the fully connected layer to perform feature decoding on the entire latent feature vector to output the completed point cloud, and the system model is used and applied to the financial scenario uncertainty analysis. Through the experimental result, the performance is tested.