Haoran Song
Biographic Data
| ID | 7301040 |
|---|---|
| NAME | Haoran Song |
| GIVEN NAMES | Haoran |
| FAMILY NAME | Song |
| SIGNATURE | SONG H |
| AFFILIATIONS | Lanzhou Jiaotong University |
| ORCID | 0009-0006-4408-456X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Location Prediction of Urban Fire Station Based on GMM Clustering and Machine Learning
Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian …
Pattern Recognition in Urban Maps Based on Graph Structures
Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1)…
Cordyceps
This study demonstrates Cordyceps' prospective efficacy and safety in the prevention or treatment of CI/RI and MI/RI, providing novel insights for managing ischemic CCVDs
No prominent works on this page.
Cordyceps
This study demonstrates Cordyceps' prospective efficacy and safety in the prevention or treatment of CI/RI and MI/RI, providing novel insights for managing ischemic CCVDs
Pattern Recognition in Urban Maps Based on Graph Structures
Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1)…
Location Prediction of Urban Fire Station Based on GMM Clustering and Machine Learning
Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian …
Advances in Oncology and Radiotherapy (1 work) · Artificial Intelligence (1 work) · Cancer (1 work) · Cancer Genomics and Diagnostics (1 work) · Cluster analysis (1 work) · Computer Science (1 work) · Data Management and Algorithms (1 work) · Decision tree (1 work) · Evacuation and Crowd Dynamics (1 work) · Facility Location and Emergency Management (1 work)