Zobrazeno 1 - 10
of 57 776
pro vyhledávání: '"A. Afzal"'
Autor:
K. N. Fossum, C. Lin, N. O'Sullivan, L. Lei, S. Hellebust, D. Ceburnis, A. Afzal, A. Tremper, D. Green, S. Jain, S. Byčenkienė, C. O'Dowd, J. Wenger, J. Ovadnevaite
Publikováno v:
Atmospheric Chemistry and Physics, Vol 24, Pp 10815-10831 (2024)
Source apportionment quantitatively links pollution to its source but can be difficult to perform in areas like ports where emissions from shipping and other port-related activities are intrinsically linked. Here we present the analysis of aerosol ch
Externí odkaz:
https://doaj.org/article/f79de5286f4542a9a2635c2e3d1b6969
Autor:
Khan, Mohammad Sadil, Sinha, Sankalp, Sheikh, Talha Uddin, Stricker, Didier, Ali, Sk Aziz, Afzal, Muhammad Zeshan
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework fo
Externí odkaz:
http://arxiv.org/abs/2409.17106
Autor:
Ahmed, Afzal, Raees, Muhammad
In today's businesses, marketing has been a central trend for growth. Marketing quality is equally important as product quality and relevant metrics. Quality of Marketing depends on targeting the right person. Technology adaptations have been slow in
Externí odkaz:
http://arxiv.org/abs/2409.09956
Autor:
Aghaei, Alireza Afzal
In this paper, we introduce the KANtrol framework, which utilizes Kolmogorov-Arnold Networks (KANs) to solve optimal control problems involving continuous time variables. We explain how Gaussian quadrature can be employed to approximate the integral
Externí odkaz:
http://arxiv.org/abs/2409.06649
Autor:
Aghaei, Alireza Afzal
This paper introduces a novel methodology for solving distributed-order fractional differential equations using a physics-informed machine learning framework. The core of this approach involves extending the support vector regression (SVR) algorithm
Externí odkaz:
http://arxiv.org/abs/2409.03507
Autor:
Raees, Muhammad, Ahmed, Afzal
Long-distance transport plays a vital role in the economic growth of countries. However, there is a lack of systems being developed for monitoring and support of long-route vehicles (LRV). Sustainable and context-aware transport systems with modern t
Externí odkaz:
http://arxiv.org/abs/2409.02434
This paper introduces an efficient tensor-vector product technique for the rapid and accurate approximation of integral operators within physics-informed deep learning frameworks. Our approach leverages neural network architectures to evaluate proble
Externí odkaz:
http://arxiv.org/abs/2409.01899
Autor:
Abassy, Mervat, Elozeiri, Kareem, Aziz, Alexander, Ta, Minh Ngoc, Tomar, Raj Vardhan, Adhikari, Bimarsha, Ahmed, Saad El Dine, Wang, Yuxia, Afzal, Osama Mohammed, Xie, Zhuohan, Mansurov, Jonibek, Artemova, Ekaterina, Mikhailov, Vladislav, Xing, Rui, Geng, Jiahui, Iqbal, Hasan, Mujahid, Zain Muhammad, Mahmoud, Tarek, Tsvigun, Akim, Aji, Alham Fikri, Shelmanov, Artem, Habash, Nizar, Gurevych, Iryna, Nakov, Preslav
The widespread accessibility of large language models (LLMs) to the general public has significantly amplified the dissemination of machine-generated texts (MGTs). Advancements in prompt manipulation have exacerbated the difficulty in discerning the
Externí odkaz:
http://arxiv.org/abs/2408.04284
Autor:
Wang, Yanhu, Afzal, Muhammad Muzammil, Li, Zhengyang, Zhou, Jie, Feng, Chenyuan, Guo, Shuaishuai, Quek, Tony Q. S.
Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated te
Externí odkaz:
http://arxiv.org/abs/2408.03631
Iris recognition is widely used in several fields such as mobile phones, financial transactions, identification cards, airport security, international border control, voter registration for living persons. However, the possibility of identifying dece
Externí odkaz:
http://arxiv.org/abs/2408.03448