Zobrazeno 1 - 10
of 4 788
pro vyhledávání: '"Jouvet, A"'
Autor:
Nanini, Santino, Abid, Mariem, Mamouni, Yassir, Wiedemann, Arnaud, Jouvet, Philippe, Bourassa, Stephane
This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-
Externí odkaz:
http://arxiv.org/abs/2410.23503
Autor:
Rahman, Aamer Abdul, Agarwal, Pranav, Noumeir, Rita, Jouvet, Philippe, Michalski, Vincent, Kahou, Samira Ebrahimi
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the lack of interpretability and interactivity for clinicians. To address
Externí odkaz:
http://arxiv.org/abs/2407.19380
Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to t
Externí odkaz:
http://arxiv.org/abs/2404.14533
Autor:
Macabiau, Clara, Le, Thanh-Dung, Albert, Kevin, Shahriari, Mana, Jouvet, Philippe, Noumeir, Rita
Publikováno v:
IEEE Access, 12 (2024), pp. 81221-81235
This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples ar
Externí odkaz:
http://arxiv.org/abs/2308.08480
The paper introduces Diff-Filter, a multichannel speech enhancement approach based on the diffusion probabilistic model, for improving speaker verification performance under noisy and reverberant conditions. It also presents a new two-step training p
Externí odkaz:
http://arxiv.org/abs/2307.02244
Publikováno v:
Proc. 2021 ISCA Symposium on Security and Privacy in Speech Communication (62-66)
Speech data carries a range of personal information, such as the speaker's identity and emotional state. These attributes can be used for malicious purposes. With the development of virtual assistants, a new generation of privacy threats has emerged.
Externí odkaz:
http://arxiv.org/abs/2305.01759
Transformer-based models have shown outstanding results in natural language processing but face challenges in applications like classifying small-scale clinical texts, especially with constrained computational resources. This study presents a customi
Externí odkaz:
http://arxiv.org/abs/2303.12892
Publikováno v:
4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2022), Oct 2022, Corfu, Greece
Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise andthe adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-fieldspeaker ve
Externí odkaz:
http://arxiv.org/abs/2210.08834
When dealing with clinical text classification on a small dataset recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural
Externí odkaz:
http://arxiv.org/abs/2209.12831
Autor:
E. Russo, J. Buzan, S. Lienert, G. Jouvet, P. Velasquez Alvarez, B. Davis, P. Ludwig, F. Joos, C. C. Raible
Publikováno v:
Climate of the Past, Vol 20, Pp 449-465 (2024)
In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we ar
Externí odkaz:
https://doaj.org/article/5b217e4a0019410aaeef4a1774c50f42
