Zobrazeno 1 - 10
of 11 026
pro vyhledávání: '"Phi A"'
A systematic review of machine learning models for management, prediction and classification of ARDS
Publikováno v:
Respiratory Research, Vol 25, Iss 1, Pp 1-14 (2024)
Abstract Aim Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promisi
Externí odkaz:
https://doaj.org/article/62e4547f0ba54fdf98707f8fab2eb426
Autor:
Minh C. Tran, Douglas C. Crockett, Tu K. Tran, Phi A. Phan, Formenti Federico, Richard Bruce, Gaetano Perchiazzi, Stephen J. Payne, Andrew D. Farmery
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluat
Externí odkaz:
https://doaj.org/article/5aee7b5e5b6e41128767d3bae2b981a7
Autor:
Chen, Hailin, Jiao, Fangkai, Ravaut, Mathieu, Farruque, Nawshad, Nguyen, Xuan Phi, Qin, Chengwei, Dey, Manan, Ding, Bosheng, Xiong, Caiming, Joty, Shafiq, Zhou, Yingbo
The rapid development of large language models (LLMs) necessitates robust, unbiased, and scalable methods for evaluating their capabilities. However, human annotations are expensive to scale, model-based evaluations are prone to biases in answer styl
Externí odkaz:
http://arxiv.org/abs/2412.18011
Autor:
Barboule, Camille, Huynh, Viet-Phi, Bufort, Adrien, Chabot, Yoan, Damnati, Géraldine, Lecorvé, Gwénolé
Despite outstanding processes in many tasks, Large Language Models (LLMs) still lack accuracy when dealing with highly technical domains. Especially, telecommunications (telco) is a particularly challenging domain due the large amount of lexical, sem
Externí odkaz:
http://arxiv.org/abs/2412.15891
Autor:
Ding, Tong, Wagner, Sophia J., Song, Andrew H., Chen, Richard J., Lu, Ming Y., Zhang, Andrew, Vaidya, Anurag J., Jaume, Guillaume, Shaban, Muhammad, Kim, Ahrong, Williamson, Drew F. K., Chen, Bowen, Almagro-Perez, Cristina, Doucet, Paul, Sahai, Sharifa, Chen, Chengkuan, Komura, Daisuke, Kawabe, Akihiro, Ishikawa, Shumpei, Gerber, Georg, Peng, Tingying, Le, Long Phi, Mahmood, Faisal
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). Howe
Externí odkaz:
http://arxiv.org/abs/2411.19666
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
In the context of modern life, particularly in Industry 4.0 within the online space, emotions and moods are frequently conveyed through social media posts. The trend of sharing stories, thoughts, and feelings on these platforms generates a vast and p
Externí odkaz:
http://arxiv.org/abs/2411.04532
Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First
Externí odkaz:
http://arxiv.org/abs/2411.02773
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
We explore a robust version of the barycenter problem among $n$ centered Gaussian probability measures, termed Semi-Unbalanced Optimal Transport (SUOT)-based Barycenter, wherein the barycenter remains fixed while the others are relaxed using Kullback
Externí odkaz:
http://arxiv.org/abs/2410.08117