Zobrazeno 1 - 10
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pro vyhledávání: '"Ghani, A"'
We study differential equations with a linear, path dependent drift and discrete delay in the diffusion term driven by a $\gamma$-H\"older rough path for $\gamma > \frac{1}{3}$. We prove well-posedness of these systems and establish a priori bounds f
Externí odkaz:
http://arxiv.org/abs/2411.04590
This report presents an approach for optimizing metro station locations and line layouts in the area of Selangor, located in Malaysia. The project utilized the genetic algorithm in identifying the locations and lines layout. With population in Selang
Externí odkaz:
http://arxiv.org/abs/2411.03797
In this paper, we develop a way of analyzing the random dynamics of stochastic evolution equations with a non-dense domain. Such problems cover several types of evolution equations. We are particularly interested in evolution equations with non-homog
Externí odkaz:
http://arxiv.org/abs/2410.19509
Autor:
Ghani, Burooj, Kalkman, Vincent J., Planqué, Bob, Vellinga, Willem-Pier, Gill, Lisa, Stowell, Dan
Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performan
Externí odkaz:
http://arxiv.org/abs/2409.15383
We develop a categorical framework for reasoning about abstract properties of differentiation, based on the theory of fibrations. Our work encompasses the first-order fragments of several existing categorical structures for differentiation, including
Externí odkaz:
http://arxiv.org/abs/2409.05763
Autor:
Jagatheesaperumal, Senthil Kumar, Rahouti, Mohamed, Alfatemi, Ali, Ghani, Nasir, Quy, Vu Khanh, Chehri, Abdellah
Publikováno v:
IEEE Internet of Things Magazine, Year: 2024, Volume: 7, Issue: 5
Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data priva
Externí odkaz:
http://arxiv.org/abs/2409.02127
Models for categorical sequences typically assume exchangeable or first-order dependent sequence elements. These are common assumptions, for example, in models of computer malware traces and protein sequences. Although such simplifying assumptions le
Externí odkaz:
http://arxiv.org/abs/2407.19236
Autor:
Sarkar, Soumya, Han, Zirun, Ghani, Maheera Abdul, Strkalj, Nives, Kim, Jung Ho, Wang, Yan, Jariwala, Deep, Chhowalla, Manish
Some van der Waals (vdW) materials exhibit ferroelectricity, making them promising for novel non-volatile memories (NVMs) such as ferroelectric diodes (FeDs). CuInP2S6 (CIPS) is a well-known vdW ferroelectric that has been integrated with graphene fo
Externí odkaz:
http://arxiv.org/abs/2407.09175
Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle's range of functions and considerably reducing the distance t
Externí odkaz:
http://arxiv.org/abs/2407.04717
Autor:
Martinez, Fernando, Mapkar, Mariyam, Alfatemi, Ali, Rahouti, Mohamed, Xin, Yufeng, Xiong, Kaiqi, Ghani, Nasir
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cybersecurit
Externí odkaz:
http://arxiv.org/abs/2406.02632