Junhyeok Lee
Biographic Data
| ID | 7960271 |
|---|---|
| NAME | Junhyeok Lee |
| GIVEN NAMES | Junhyeok |
| FAMILY NAME | Lee |
| SIGNATURE | LEE J |
| AFFILIATIONS | Kyung Hee University |
| ORCID | 0009-0008-8089-1881 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
E-commerce supply chain planning with Space-as-a-Service
A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms
Our findings demonstrate that integrating sMRI and FA features improves brain age prediction accuracy and generalization. Furthermore, the correlation between brainPAD and clinical symptoms highlights its potential as a biomarker for disease progression and treatment monitoring. These results underscore the value of multimodal neuroimaging and machine learning in advancing psychiatric neuroimaging research and paving the way for clinical applicat…
Helmet wearing and related factors among electric personal mobility device users in Korea
Using data from the 2022 Korea Community Health Survey (n = 13 320), this study investigated helmet use and related factors among Korean adults using personal mobility devices, without distinguishing between private and hired users. Among mobility device users, 32.1% responded that they always wore a helmet. The proportion of helmet use was 35.2% among men, 25.8% among women, 29.2% among those aged 19–44 years, 42.3% among those aged 45–64 years …
No prominent works on this page.
Helmet wearing and related factors among electric personal mobility device users in Korea
Using data from the 2022 Korea Community Health Survey (n = 13 320), this study investigated helmet use and related factors among Korean adults using personal mobility devices, without distinguishing between private and hired users. Among mobility device users, 32.1% responded that they always wore a helmet. The proportion of helmet use was 35.2% among men, 25.8% among women, 29.2% among those aged 19–44 years, 42.3% among those aged 45–64 years …
A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms
Our findings demonstrate that integrating sMRI and FA features improves brain age prediction accuracy and generalization. Furthermore, the correlation between brainPAD and clinical symptoms highlights its potential as a biomarker for disease progression and treatment monitoring. These results underscore the value of multimodal neuroimaging and machine learning in advancing psychiatric neuroimaging research and paving the way for clinical applicat…
E-commerce supply chain planning with Space-as-a-Service
Medicine (2 works) · Psychology (2 works) · Automotive and Human Injury Biomechanics (1 works) · Context (archaeology) (1 works) · Engineering (1 works) · Facility Location and Emergency Management (1 works) · Functional Brain Connectivity Studies (1 works) · Human Factors and Ergonomics (1 works) · Injury Epidemiology and Prevention (1 works) · Injury prevention (1 works)