Design of intelligent financial data management system based on higher-order hybrid clustering algorithm.

ABSTRACT: Amid the ever-expanding landscape of financial data, the importance of predicting potential risks through artificial intelligence methodologies has steadily risen. To achieve prudent financial data management, this manuscript delves into the domain of intelligent financial risk forecasting within the scope of system design. It presents a data model based on the variational encoder (VAE) enhanced with an attention mechanism meticulously tailored for forecasting a company's financial peril. The framework called the ATT-VAE embarks on its journey by encoding and enhancing multidimensional data through VAE. It then employs the attention mechanism to enrich the outputs of the VAE network, thereby demonstrating the apex of the model's clustering capabilities. In the experimentation, we implement

SUBMITTER: Huang L 

PROVIDER: S-EPMC11636689 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature