Zobrazeno 1 - 10
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pro vyhledávání: '"Mustafa, Ahmed A."'
The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traffic management systems, particu
Externí odkaz:
http://arxiv.org/abs/2408.15751
Autor:
Mustafa, Ahmed, Rafique, Muhammad Tahir, Baig, Muhammad Ijlal, Sajid, Hasan, Khan, Muhammad Jawad, Kallu, Karam Dad
This research paper introduces a novel word-level Optical Character Recognition (OCR) model specifically designed for digital Urdu text, leveraging transformer-based architectures and attention mechanisms to address the distinct challenges of Urdu sc
Externí odkaz:
http://arxiv.org/abs/2408.15119
Neural vocoders are now being used in a wide range of speech processing applications. In many of those applications, the vocoder can be the most complex component, so finding lower complexity algorithms can lead to significant practical benefits. In
Externí odkaz:
http://arxiv.org/abs/2405.21069
Publikováno v:
Journal of Applied Research in Higher Education, 2023, Vol. 16, Issue 5, pp. 1734-1748.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JARHE-05-2023-0190
Publikováno v:
Arabiyatuna: Jurnal Bahasa Arab, Vol 8, Iss 2, Pp 897-912 (2024)
This research aims to expose the meanings, and the difference in the structure of the present tense in the frequent Qur’anic readings with an explanation of the impact of this difference in the noble verse, and it aims to provide correct linguistic
Externí odkaz:
https://doaj.org/article/4533cefd080448fc85ab96bb9cd137db
Autor:
Mahgoub Samar M., Alwaili Maha A., Rudayni Hassan A., Almalki Manal A., Allam Ahmed A., Abdel-Reheim Mustafa Ahmed, Mohammed Osama A., Mohamed Mahmoud A.
Publikováno v:
Green Processing and Synthesis, Vol 13, Iss 1, Pp 1374-88 (2024)
Externí odkaz:
https://doaj.org/article/07503b6a1ed04c69998965261c28ff6d
Autor:
Mustafa Ahmed Mahmutoglu, Aditya Rastogi, Marianne Schell, Martha Foltyn-Dumitru, Michael Baumgartner, Klaus Hermann Maier-Hein, Katerina Deike-Hofmann, Alexander Radbruch, Martin Bendszus, Gianluca Brugnara, Philipp Vollmuth
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-7 (2024)
Abstract The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA da
Externí odkaz:
https://doaj.org/article/0eb7272d53b34633a6b9fc8219c448c2
Autor:
Hashim Ali Alghamdi, Meshari Saad M Alqahtani, Hatem Mostafa Mohammed Asiri, Abdulaziz Mohammed M Abudasir, Khalid Talab Salem Alshahrani, Rahaf Ahmed Alamer, Ali Abdullah S Alshahrani, Yasir Abduallah M Alyahya, Anas Mohammed abudasir, Saeed Jarallah S AlQahtani, Ghassan E. Mustafa Ahmed
Publikováno v:
BMC Pediatrics, Vol 24, Iss 1, Pp 1-7 (2024)
Abstract Background Overall, stoma-related morbidity affects a reported 20–38% of pediatric patients. However, determining the true incidence of major stoma-related morbidity is challenging due to limited cohort sizes in existing studies. Thus, the
Externí odkaz:
https://doaj.org/article/1a9190f33a504f96a224b4d372e91e92
Speech codec enhancement methods are designed to remove distortions added by speech codecs. While classical methods are very low in complexity and add zero delay, their effectiveness is rather limited. Compared to that, DNN-based methods deliver high
Externí odkaz:
http://arxiv.org/abs/2309.14521
Classical speech coding uses low-complexity postfilters with zero lookahead to enhance the quality of coded speech, but their effectiveness is limited by their simplicity. Deep Neural Networks (DNNs) can be much more effective, but require high compl
Externí odkaz:
http://arxiv.org/abs/2307.06610