Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Latif U. Khan"'
Publikováno v:
IEEE Access, Vol 12, Pp 67924-67934 (2024)
Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operat
Externí odkaz:
https://doaj.org/article/63f5b06f910d4951a5367331571889b3
Autor:
Zahid U. Khan, Latif U. Khan, Hermi F. Brito, Magnus Gidlund, Oscar L. Malta, Paolo Di Mascio
Publikováno v:
ACS Omega, Vol 8, Iss 38, Pp 34328-34353 (2023)
Externí odkaz:
https://doaj.org/article/5e3d7a83543f4d37babbff9da6b92d37
Publikováno v:
IEEE Access, Vol 11, Pp 4381-4399 (2023)
Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is chal
Externí odkaz:
https://doaj.org/article/cae05d923b064bc38be070c6ea108dc4
Autor:
Messaoud Harfouche, Mahmoud Abdellatief, Yazeed Momani, Anas Abbadi, Mohammad Al Najdawi, Mustafa Al Zoubi, Basil Aljamal, Salman Matalgah, Latif U. Khan, Andrea Lausi, Giorgio Paolucci
Publikováno v:
Journal of Synchrotron Radiation, Vol 29, Iss 4, Pp 1107-1113 (2022)
XAFS/XRF is a general-purpose absorption spectroscopy beamline at the Synchrotron-light for Experimental Science and Applications in the Middle East (SESAME), Jordan. Herein, its optical layout is presented along with its powerful capabilities in col
Externí odkaz:
https://doaj.org/article/b801fbcb6bfe460cac98c3e785a64c8c
Publikováno v:
IEEE Access, Vol 10, Pp 87602-87616 (2022)
Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we prese
Externí odkaz:
https://doaj.org/article/4d2e8047f265416d98056c7c03ca0ee7
Publikováno v:
IEEE Access, Vol 9, Pp 155634-155650 (2021)
Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is gene
Externí odkaz:
https://doaj.org/article/46f427ed361841c381889c306c59b45a
Publikováno v:
IEEE Access, Vol 8, Pp 48060-48073 (2020)
Recently fifth-generation (5G) of cellular networks appeared to enable various smart bandwidth thirsty applications. Frequency re-usability via device-to-device (D2D) communication is one of the possible ways to enhance the 5G network throughput. How
Externí odkaz:
https://doaj.org/article/5e520728c4954befac7f19df54648721
Publikováno v:
IEEE Access, Vol 8, Pp 36009-36028 (2020)
Fifth-generation (5G) and beyond networks are envisioned to provide multi-services with diverse specifications. Network slicing is identified as a key enabling technology to enable 5G networks with multi-services. Network slicing allows a transition
Externí odkaz:
https://doaj.org/article/7dd8385969ee4924bffdc5082bce829a
Publikováno v:
IEEE Access, Vol 8, Pp 168854-168864 (2020)
Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory effic
Externí odkaz:
https://doaj.org/article/41a2d1650ec0443c8b3dd211dd7519ed
Publikováno v:
IEEE Access, Vol 8, Pp 147029-147044 (2020)
Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might
Externí odkaz:
https://doaj.org/article/950e75a0eb0c41d18ab0a19797979579