Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Phi Le"'
Publikováno v:
IEEE Access, Vol 12, Pp 55889-55904 (2024)
Patients’ safety is paramount in the healthcare industry, and reducing medication errors is essential for improvement. A promising solution to this problem involves the development of automated systems capable of assisting patients in verifying the
Externí odkaz:
https://doaj.org/article/4f61dc8b4b0147f69fc440115c0d35e3
Publikováno v:
Frontiers in Systems Biology, Vol 4 (2024)
Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constru
Externí odkaz:
https://doaj.org/article/a845c2977dcf43798780ba07759f8b5f
Autor:
Li Zhang, Lawrence Fong, Andrew H Ko, Brandon Chen, Alexander Cheung, Alec Starzinski, Hai Yang, Tony Li, Bridget P Keenan, Maira Soto, Erin L Filbert, Stephanie Starzinski, Marissa Gin, Phi Le, Brandon Bol, Frank J Hsu
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 11, Iss Suppl 1 (2023)
Externí odkaz:
https://doaj.org/article/d6f0d9b6024c4f07aadb89a3523af07b
Autor:
Anh Duy Nguyen, Phi Le Nguyen, Viet Hung Vu, Quoc Viet Pham, Viet Huy Nguyen, Minh Hieu Nguyen, Thanh Hung Nguyen, Kien Nguyen
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-25 (2022)
Abstract Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data
Externí odkaz:
https://doaj.org/article/0991ebb42e74494ea776e3c05dff9e04
Publikováno v:
IEEE Access, Vol 10, Pp 76537-76546 (2022)
Multipath communication is a well-developed technology that enhances communication effectiveness and resilience. Moreover, it can flexibly utilize network resources through load balancing among available paths. However, traditionally, deploying such
Externí odkaz:
https://doaj.org/article/5b448e2195014258adfe2aac9cd2e49f
Autor:
Anh Duy Nguyen, Huy Hieu Pham, Huynh Thanh Trung, Quoc Viet Hung Nguyen, Thao Nguyen Truong, Phi Le Nguyen
Publikováno v:
PLoS ONE, Vol 18, Iss 9, p e0291865 (2023)
Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern that needs to be investigated thorou
Externí odkaz:
https://doaj.org/article/fcd17d7eeac14c83a21f4b4fb59b3b14
Autor:
Nguyen, Thanh Tam, Ren, Zhao, Pham, Trinh, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruc
Externí odkaz:
http://arxiv.org/abs/2411.09955
Autor:
Nguyen, Dac Thai, Nguyen, Trung Thanh, Nguyen, Huu Tien, Nguyen, Thanh Trung, Pham, Huy Hieu, Nguyen, Thanh Hung, Truong, Thao Nguyen, Nguyen, Phi Le
Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioacti
Externí odkaz:
http://arxiv.org/abs/2410.21932
Autor:
Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia, Nguyen, Phi Le, Huynh, Thanh Trung
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant ch
Externí odkaz:
http://arxiv.org/abs/2410.03070
Autor:
Nguyen, Minh Hieu, Nguyen, Huu Tien, Nguyen, Trung Thanh, Nguyen, Manh Duong, Hoang, Trong Nghia, Nguyen, Truong Thao, Nguyen, Phi Le
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data pertu
Externí odkaz:
http://arxiv.org/abs/2410.03067