Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Firas Al-Hindawi"'
Publikováno v:
Frontiers in Physiology, Vol 15 (2024)
Predictive modeling of clinical time series data is challenging due to various factors. One such difficulty is the existence of missing values, which leads to irregular data. Another challenge is capturing correlations across multiple dimensions in o
Externí odkaz:
https://doaj.org/article/469956f3adec4ea6964d30b8acbf16a3
Publikováno v:
Bioengineering, Vol 10, Iss 12, p 1372 (2023)
Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a nov
Externí odkaz:
https://doaj.org/article/0e5af824e5cf45eb8697affd266ec1e4
Autor:
Firas Al-Hindawi, Tejaswi Soori, Han Hu, Md. Mahfuzur Rahman Siddiquee, Hyunsoo Yoon, Teresa Wu, Ying Sun
The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces signifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0946cc4ff5011e411f9683a72acd0f54
Autor:
Firas Al-Hindawi, Ying Sun, Teresa Wu, Tejaswi Soori, Seyed Moein Rassoulinejad-Mousavi, Han Hu, Arif Rokoni, Hyunsoo Yoon
Publikováno v:
Applied Thermal Engineering. 190:116849
Image-based deep learning (DL) models are employed to enable the detection of critical heat flux (CHF) based on pool boiling experimental images. Most machine learning approaches for pool boiling to date focus on a single dataset under a certain heat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d06f5f2b65447871f6f0b36711a07bf
https://doi.org/10.1016/j.applthermaleng.2021.116849
https://doi.org/10.1016/j.applthermaleng.2021.116849