Jinhong Du
Biographic Data
| ID | 8954372 |
|---|---|
| NAME | Jinhong Du |
| GIVEN NAMES | Jinhong |
| FAMILY NAME | Du |
| SIGNATURE | DU J |
| AFFILIATIONS | Department of Statistics University of Chicago Chicago Illinois USA |
| ORCID | 0009-0002-4409-2122 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends
The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…
How do rural households adapt to multiple natural hazards in disaster-prone areas? A case study of the Wenchuan earthquake-stricken area, China
Increasing the energy efficiency of a data center based on machine learning
Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy …
No prominent works on this page.
Increasing the energy efficiency of a data center based on machine learning
Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy …
Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends
The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…
How do rural households adapt to multiple natural hazards in disaster-prone areas? A case study of the Wenchuan earthquake-stricken area, China
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Air Quality Monitoring and Forecasting (1 work) · Artificial neural network (1 work) · Business (1 work) · Chemistry (1 work) · China (1 work) · Climate change impacts on agriculture (1 work) · Cloud Computing and Resource Management (1 work)