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pro vyhledávání: '"Hussain, Khaled"'
Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on gene
Externí odkaz:
http://arxiv.org/abs/2308.03767
Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption fr
Externí odkaz:
http://arxiv.org/abs/1906.08864
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of
Externí odkaz:
http://arxiv.org/abs/1812.11894
Publikováno v:
In Computers and Electrical Engineering May 2022 100
Autor:
Afifi, Mahmoud, Hussain, Khaled F.
In spite of the high accuracy of the existing optical mark reading (OMR) systems and devices, a few restrictions remain existent. In this work, we aim to reduce the restrictions of multiple choice questions (MCQ) within tests. We use an image registr
Externí odkaz:
http://arxiv.org/abs/1711.00972
Publikováno v:
In Pattern Recognition December 2020 108
Publikováno v:
In Operations Research Perspectives 2019 6
Publikováno v:
In Clinical Lymphoma, Myeloma and Leukemia October 2022 22 Supplement 2:S190-S190
Autor:
Taim A. Muayqil, Mohammed H. Alanazy, Hassan M. Almalak, Hussain Khaled Alsalman, Faroq Walid Abdulfattah, Abdullah Ibrahim Aldraihem, Fawaz Al-hussain, Bandar N. Aljafen
Publikováno v:
BMC Neurology, Vol 18, Iss 1, Pp 1-6 (2018)
Abstract Background Little is known of how accurately a first-time seizure witness can provide reliable details of a semiology. Our goal was to determine how accurately first-time seizure witnesses could identify key elements of an epileptic event th
Externí odkaz:
https://doaj.org/article/493da094ceb74649a1244240ff70f8d6
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