Fengyi Liu
Biographic Data
| ID | 5464313 |
|---|---|
| NAME | Fengyi Liu |
| GIVEN NAMES | Fengyi |
| FAMILY NAME | Liu |
| SIGNATURE | LIU F |
| AFFILIATIONS | Rutgers, the State University of New Jersey |
| ORCID | 0009-0007-0597-5498 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Acoustic-based machine learning approaches for depression detection in Chinese university students
Background: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective and self-reported methods. There is an urgent necessity to develop objective means of identifying depression. Acoustic features, which convey emotional information, have the potential to enhance the objectivity of depression assessments. This study aimed to investigate t…
Racialized Gender Differences in Mental Health Service Use, Adverse Childhood Experiences, and Recidivism Among Justice-Involved African American Youth
Association between composite dietary antioxidant index and coronary heart disease among US adults: A cross-sectional analysis
There was a negative non-linear correlation between CDAI and CHD in US adults. However, further prospective studies are still needed to reveal their relationship
Mothers’ nonstandard work schedules and the use of multiple and center-based childcare
Mothers’ nonstandard work schedules and the use of multiple and center-based childcare
Racialized Gender Differences in Mental Health Service Use, Adverse Childhood Experiences, and Recidivism Among Justice-Involved African American Youth
Association between composite dietary antioxidant index and coronary heart disease among US adults: A cross-sectional analysis
There was a negative non-linear correlation between CDAI and CHD in US adults. However, further prospective studies are still needed to reveal their relationship
Acoustic-based machine learning approaches for depression detection in Chinese university students
Background: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective and self-reported methods. There is an urgent necessity to develop objective means of identifying depression. Acoustic features, which convey emotional information, have the potential to enhance the objectivity of depression assessments. This study aimed to investigate t…
Medicine (4 works) · Psychology (3 works) · Environmental health (2 works) · Logistic regression (2 works) · African american (1 works) · Antioxidant Activity and Oxidative Stress (1 works) · Artificial Intelligence (1 works) · Business (1 works) · Cardiovascular Disease and Adiposity (1 works) · Center (category theory (1 works)