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pro vyhledávání: '"Khan Aftab"'
Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces challenges in terms of model functionality and lifecycle management.
Externí odkaz:
http://arxiv.org/abs/2410.18793
Generative artificial intelligence (GAI) has emerged as a pivotal technology for content generation, reasoning, and decision-making, making it a promising solution on the 6G stage characterized by openness, connected intelligence, and service democra
Externí odkaz:
http://arxiv.org/abs/2410.18790
This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution capable of detecting and mitigating concept drift in Federated Learning (FL) Internet of Things (IoT) environments. Concept drift poses signific
Externí odkaz:
http://arxiv.org/abs/2410.01386
Federated Learning (FL) is a distributed machine learning approach that enables training on decentralized data while preserving privacy. However, FL systems often involve resource-constrained client devices with limited computational power, memory, s
Externí odkaz:
http://arxiv.org/abs/2406.19050
Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investigates Green ML, examining various model architectures and hyperparameter
Externí odkaz:
http://arxiv.org/abs/2406.14328
Autor:
Friji, Hamdi, Mavromatis, Ioannis, Sanchez-Mompo, Adrian, Carnelli, Pietro, Olivereau, Alexis, Khan, Aftab
With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play a crucial role in ensuring network security, actively identifying and
Externí odkaz:
http://arxiv.org/abs/2404.18328
Autor:
Anya Augustine Igwebuike, Akhtar M.W., Abo-Dahab Syed Muhammad, Kaneez Hajra, Khan Aftab, Jahangir Adnan
Publikováno v:
Journal of the Mechanical Behavior of Materials, Vol 27, Iss 5-6, Pp 64-97 (2018)
The present study deals with the reflection of SV-waves at a free surface in the presence of magnetic field, initial stress, voids and gravity. When an SV-wave incident on the free surface of an elastic half space, two damped P-waves and an SV-wave a
Externí odkaz:
https://doaj.org/article/8bff9fc8a99e4c9d8443dc1cdd4daed8
Autor:
Mavromatis, Ioannis, Jin, Yichao, Stanoev, Aleksandar, Portelli, Anthony, Weeks, Ingram, Holden, Ben, Glasspole, Eliot, Farnham, Tim, Khan, Aftab, Raza, Usman, Aijaz, Adnan, Bierton, Thomas, Seto, Ichiro, Patel, Nita, Sooriyabandara, Mahesh
UMBRELLA is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and addres
Externí odkaz:
http://arxiv.org/abs/2401.14829
Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global model aggregation method within Asynchronous Federated Learning (AFL) d
Externí odkaz:
http://arxiv.org/abs/2401.13366
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. This paper provides a guide to the implemented and pro
Externí odkaz:
http://arxiv.org/abs/2401.13346