A
Ali Safaa Sadiq
Researcher at University of Wolverhampton
Publications - 37
Citations - 1446
Ali Safaa Sadiq is an academic researcher from University of Wolverhampton. The author has contributed to research in topics: Routing protocol & Optimization problem. The author has an hindex of 12, co-authored 37 publications receiving 660 citations. Previous affiliations of Ali Safaa Sadiq include Monash University & Monash University Malaysia Campus.
Papers
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Proceedings ArticleDOI
Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms
Halgurd S. Maghdid,Aras Asaad,Kayhan Zrar Ghafoor,Ali Safaa Sadiq,Seyedali Mirjalili,Muhammad Khurram Khan +5 more
TL;DR: A simple convolution neural network and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images and the result shows that the utilized models can provide accuracy up to 98% via pre- trained network and 94.1% accuracy by using the modified CNN.
Proceedings ArticleDOI
A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study
Halgurd S. Maghded,Kayhan Zrar Ghafoor,Ali Safaa Sadiq,Kevin Curran,Danda B. Rawat,Khaled M. Rabie +5 more
TL;DR: In this article, a new framework is proposed to detect COVID-19 using built-in smartphone sensors, which provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes.
Journal ArticleDOI
Millimeter-Wave Communication for Internet of Vehicles: Status, Challenges, and Perspectives
Kayhan Zrar Ghafoor,Linghe Kong,Sherali Zeadally,Ali Safaa Sadiq,Gregory Epiphaniou,Mohammad Hammoudeh,Ali Kashif Bashir,Shahid Mumtaz +7 more
TL;DR: An in-depth survey of the existing research, published in the last decade, and the applications of mmWave communications in vehicular communications are described, which focus on MAC and physical layers and discuss related issues.
Posted Content
A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study
Halgurd S. Maghdid,Kayhan Zrar Ghafoor,Ali Safaa Sadiq,Kevin Curran,Danda B. Rawat,Khaled M. Rabie +5 more
TL;DR: A new framework is proposed to detect COVID-19 using built-in smartphone sensors and reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.
Journal ArticleDOI
A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic
TL;DR: A pipeline of DTL over Edge Computing is drawn as a future scope to assist the mitigation of any pandemic.