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Akademický článek

The challenge of Persian and Arabic poetry and literature at the court of Jacob Laith and his successors

Autor: javad gholamalizadeh, faeze arab yosef abadi

Publikováno v: تاریخ ادبیات, Vol 11, Iss 2, Pp 127-140 (2019)

The challenge of Persian and Arabic poetry and literature at the court of Jacob Laith and his successors AbstractAccording to some scholars from the book of the history of Sistan Ya'qub Laith Safari is the revivalist of Persian language and it's not

Externí odkaz: https://doaj.org/article/72b7b22d57764a279ecf7cc605d71dd4

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Spin-charge separation for paired Dirac fermions in $(1+1)$ dimensions

Autor: Haddad, Laith H.

We study Dirac fermions at finite density coupled to a complex pairing field assumed to obey scalar field theory with quartic self-repulsion. The bulk of our work develops the mathematics that elucidates the propagation of fermionic excitations in su

Externí odkaz: http://arxiv.org/abs/2409.04700

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A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos

Autor: Jebur, Sabah Abdulazeez, Hussein, Khalid A., Hoomod, Haider Kadhim, Alzubaidi, Laith, Saihood, Ahmed Ali, Gu, YuanTong

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this area, existing approaches have st

Externí odkaz: http://arxiv.org/abs/2408.00792

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Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis

Autor: He, Zhipeng, Ouyang, Chun, Alzubaidi, Laith, Barros, Alistair, Moreira, Catarina

Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying

Externí odkaz: http://arxiv.org/abs/2407.11463

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Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging

Autor: Zhao, Zehui, Alzubaidi, Laith, Zhang, Jinglan, Duan, Ye, Naseem, Usman, Gu, Yuantong

Deep learning has significantly advanced automatic medical diagnostics and released the occupation of human resources to reduce clinical pressure, yet the persistent challenge of data scarcity in this area hampers its further improvements and applica

Externí odkaz: http://arxiv.org/abs/2407.06566

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An Experimental Comparison of Transfer Learning against Self-supervised Learning

Autor: Zhao, Zehui, Alzubaidi, Laith, Zhang, Jinglan, Duan, Ye, Naseem, Usman, Gu, Yuantong

Recently, transfer learning and self-supervised learning have gained significant attention within the medical field due to their ability to mitigate the challenges posed by limited data availability, improve model generalisation, and reduce computati

Externí odkaz: http://arxiv.org/abs/2407.05592

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