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Ahmed Ghoneim
Researcher at King Saud University
Publications - 86
Citations - 2751
Ahmed Ghoneim is an academic researcher from King Saud University. The author has contributed to research in topics: Web service & Software evolution. The author has an hindex of 25, co-authored 84 publications receiving 1769 citations. Previous affiliations of Ahmed Ghoneim include Menoufia University & Tanta University.
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Energy Efficient Task Caching and Offloading for Mobile Edge Computing
TL;DR: This paper introduces a new concept of task caching, and proposes efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm, which outperforms others in terms of less energy cost.
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Cervical cancer classification using convolutional neural networks and extreme learning machines
TL;DR: A cervical cancer cell detection and classification system based on convolutional neural networks (CNNs) and an extreme learning machine (ELM)-based classifier that achieved 99.5% accuracy in the detection problem and 91.2% in the classification problem.
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Edge-CoCaCo: Toward Joint Optimization of Computation, Caching, and Communication on Edge Cloud
TL;DR: A new concept of computing task caching is introduced and the optimal computing task caches policy is given and joint optimization of computation, caching, and communication on the edge cloud, dubbed Edge-CoCaCo, is proposed and the solution to that optimization problem is given.
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A Lightweight and Robust Secure Key Establishment Protocol for Internet of Medical Things in COVID-19 Patients Care
Mehedi Masud,Gurjot Singh Gaba,Salman A. AlQahtani,Ghulam Muhammad,Brij B. Gupta,Pardeep Kumar,Ahmed Ghoneim +6 more
TL;DR: This article proposes a lightweight and physically secure mutual authentication and secret key establishment protocol that uses physical unclonable functions (PUFs) to enable the network devices to verify the doctor’s legitimacy and sensor node before establishing a session key.
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Medical Image Forgery Detection for Smart Healthcare
TL;DR: A new medical image forgery detection system for the healthcare framework to verify that images related to healthcare are not changed or altered and works seamlessly and in real time.