Zobrazeno 1 - 10
of 8 278
pro vyhledávání: '"ASLAM, MUHAMMAD"'
Autor:
Aslam, Muhammad Azeem, Jun, Wang, Ahmed, Nisar, Zaman, Muhammad Imran, Yanan, Li, Hongfei, Hu, Shiyu, Wang, Liu, Xin
In multi-label emotion classification, particularly for low-resource languages like Arabic, the challenges of class imbalance and label correlation hinder model performance, especially in accurately predicting minority emotions. To address these issu
Externí odkaz:
http://arxiv.org/abs/2410.03979
Human emotion is a complex phenomenon conveyed and perceived through facial expressions, vocal tones, body language, and physiological signals. Multimodal emotion recognition systems can perform well because they can learn complementary and redundant
Externí odkaz:
http://arxiv.org/abs/2408.09035
Based on the expectations that the lowest-lying doubly-bottom tetraquark $T_{bb\bar u \bar d}$ ($J^P = 1^+$) and the bottom-charm tetraquark $T_{bc\bar u \bar d}$ ($J^P = 0^+$) are stable against strong and electromagnetic decays, we work out a numbe
Externí odkaz:
http://arxiv.org/abs/2405.01173
Autor:
Aslam, Muhammad Haseeb, Zeeshan, Muhammad Osama, Belharbi, Soufiane, Pedersoli, Marco, Koerich, Alessandro, Bacon, Simon, Granger, Eric
Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because of their ability to learn complementary and redundant semantic information. However, these models struggle in
Externí odkaz:
http://arxiv.org/abs/2401.15489
Publikováno v:
Published in 'Progress of Theoretical and Experimental Physics' 2024
The experimental studies of the observables associated with the $b \rightarrow c$ transitions in the semileptonic $B-$ meson decays at BaBar, Belle and LHCb have shown some deviations from the Standard Model (SM) predictions, consequently, providing
Externí odkaz:
http://arxiv.org/abs/2401.02334
Autor:
Lateef, Abdul, Raza, Zulfiqar Ali, Aslam, Muhammad, Rehman, Muhammad Shoaib Ur, Iftikhar, Asma, Zahir, Abdul
Publikováno v:
Pigment & Resin Technology, 2023, Vol. 53, Issue 6, pp. 955-965.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/PRT-04-2023-0035
Autor:
Ali, Rizwan, Xu, Jin, Baig, Mushahid Hussain, Rehman, Hafiz Saif Ur, Waqas Aslam, Muhammad, Qasim, Kaleem Ullah
Publikováno v:
Journal of Economic Studies, 2024, Vol. 51, Issue 8, pp. 1677-1693.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JES-01-2024-0022
Publikováno v:
Kybernetes, 2023, Vol. 53, Issue 11, pp. 4840-4862.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/K-03-2023-0390
Autor:
Zeeshan, Muhammad Osama, Aslam, Muhammad Haseeb, Belharbi, Soufiane, Koerich, Alessandro Lameiras, Pedersoli, Marco, Bacon, Simon, Granger, Eric
Adapting a deep learning model to a specific target individual is a challenging facial expression recognition (FER) task that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt
Externí odkaz:
http://arxiv.org/abs/2312.05632