Výsledky vyhledávání

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Dynamic Early Exiting Predictive Coding Neural Networks

Autor: Zniber, Alaa, Karrakchou, Ouassim, Ghogho, Mounir

Internet of Things (IoT) sensors are nowadays heavily utilized in various real-world applications ranging from wearables to smart buildings passing by agrotechnology and health monitoring. With the huge amounts of data generated by these tiny devices

Externí odkaz: http://arxiv.org/abs/2309.02022

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Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting

Autor: Miccini, Riccardo, Zniber, Alaa, Laroche, Clément, Piechowiak, Tobias, Schoeberl, Martin, Pezzarossa, Luca, Karrakchou, Ouassim, Sparsø, Jens, Ghogho, Mounir

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several

Externí odkaz: http://arxiv.org/abs/2308.16678

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Akademický článek

1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia

Autor: Mohammed Zniber, Tarja Lamminen, Pekka Taimen, Peter J. Boström, Tan-Phat Huynh

Publikováno v: Heliyon, Vol 10, Iss 7, Pp e28949- (2024)

Background: Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are prevalent conditions affecting a significant portion of the male population, particularly with advancing age. Traditional diagnostic methods, such as digital rectal examinat

Externí odkaz: https://doaj.org/article/1f9cc206ae66449498d4229cda81e816