Zhiyuan Luo
Biographic Data
| ID | 9903339 |
|---|---|
| NAME | Zhiyuan Luo |
| GIVEN NAMES | Zhiyuan |
| FAMILY NAME | Luo |
| SIGNATURE | LUO Z |
| AFFILIATIONS | National Science and Technology Center for Disaster Reduction |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Bibliometric study on clinical research of osteoporosis in adolescents
Objective: Focusing on the theme of "osteoporosis-related research in adolescents," a systematic visualization of the developmental lineage, current research status, hot spots, and trends of adolescent osteoporosis was conducted to provide a reference for subsequent related research, clinical diagnosis, and treatment. Method: The Web of Science core database was used as the data source to retrieve the relevant literature and the bibliometrics met…
No prominent works on this page.
Bibliometric study on clinical research of osteoporosis in adolescents
Objective: Focusing on the theme of "osteoporosis-related research in adolescents," a systematic visualization of the developmental lineage, current research status, hot spots, and trends of adolescent osteoporosis was conducted to provide a reference for subsequent related research, clinical diagnosis, and treatment. Method: The Web of Science core database was used as the data source to retrieve the relevant literature and the bibliometrics met…
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Anomaly Detection Techniques and Applications (1 work) · Bibliometrics (1 work) · Biomarkers in Disease Mechanisms (1 work) · Bone health and osteoporosis research (1 work) · Bone Metabolism and Diseases (1 work) · Computer Science (1 work) · COVID-19 diagnosis using AI (1 work) · Deep learning (1 work) · Family medicine (1 work) · Interpretability (1 work)