Zobrazeno 1 - 10
of 384
pro vyhledávání: '"Ji-Yeoun Lee"'
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10376 (2024)
In this study, we examine the predictive factors influencing the outcomes of voice treatment in patients with voice-related disorders, using the voice handicap index (VHI) as a key assessment tool. By analyzing various personal habits and clinical va
Externí odkaz:
https://doaj.org/article/abc79165c43b4516b409be61acba9a0e
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7574 (2024)
Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scal
Externí odkaz:
https://doaj.org/article/b8fe6ef198bc41c2a20c2a65ec0ffaaa
Publikováno v:
Applied Sciences, Vol 13, Iss 20, p 11523 (2023)
Examining the relationship between the prognostic factors and the effectiveness of voice therapy is a crucial step in developing personalized treatment strategies for individuals with voice disorders. This study recommends using the multilayer percep
Externí odkaz:
https://doaj.org/article/f49e8d721bcb48919d948c2f312ca9a4
Autor:
Ji-Na Lee, Ji-Yeoun Lee
Publikováno v:
Applied Sciences, Vol 13, Iss 6, p 3571 (2023)
The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the cl
Externí odkaz:
https://doaj.org/article/627de2fb865b4ee480677c530e27a459
Autor:
Ji-Yeoun Lee
Publikováno v:
Applied Sciences, Vol 11, Iss 21, p 9836 (2021)
The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforw
Externí odkaz:
https://doaj.org/article/aab94a242b2f46feb6426015713ef431
Autor:
Ji-Yeoun Lee
Publikováno v:
Applied Sciences, Vol 11, Iss 15, p 7149 (2021)
This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficient
Externí odkaz:
https://doaj.org/article/1d63c7aaa161435e933ff6c650c08947
Autor:
Hee-Jin Choi, Ji-Yeoun Lee
Publikováno v:
Applied Sciences, Vol 11, Iss 15, p 6966 (2021)
The objective of this study was to test higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identify gender- and age-related differences through HOS analysis. This study was based on data from 116 s
Externí odkaz:
https://doaj.org/article/66509b75cdb24fe59daaa0251ba2c35f
Autor:
Jong Seok Lee1, Ji Yeoun Lee1,2, Kyung Hyun Kim1, Sung-Hye Park3, Eun Jung Koh1, Seung-Ki Kim1,4, Ji Hoon Phi1,4 phijh@snu.ac.kr
Publikováno v:
Cancer Research & Treatment. Jul2024, Vol. 56 Issue 3, p909-919. 11p.
Autor:
Sojin Kim, Tamrin Chowdhury, Hyeon Jong Yu, Jee Ye Kahng, Chae Eun Lee, Seung Ah. Choi, Kyung-Min Kim, Ho Kang, Joo Ho Lee, Soon-Tae Lee, Jae-Kyung Won, Kyung Hyun Kim, Min-Sung Kim, Ji Yeoun Lee, Jin Wook Kim, Yong-Hwy Kim, Tae Min Kim, Seung Hong Choi, Ji Hoon Phi, Young-Kyoung Shin, Ja-Lok Ku, Sungyoung Lee, Hongseok Yun, Hwajin Lee, Dokyoung Kim, Kyoungmi Kim, Junho K. Hur, Sung-Hye Park, Seung-Ki Kim, Chul-Kee Park
Publikováno v:
Genome Medicine, Vol 14, Iss 1, Pp 1-16 (2022)
Abstract Background The activation of the telomere maintenance mechanism (TMM) is one of the critical drivers of cancer cell immortality. In gliomas, TERT expression and TERT promoter mutation are considered to reliably indicate telomerase activation
Externí odkaz:
https://doaj.org/article/f8118b5a57eb4e34a8c2820b80aa8ed9
Autor:
Youngbo Shim, Kyung Hyun Kim, Seung-Ki Kim, Kwanjin Park, Seunghyun Lee, Ji Yeoun Lee, Kyu-Chang Wang
Publikováno v:
Neurosurgery; Nov2024, Vol. 95 Issue 5, p1117-1125, 9p