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Ashraf Darwish
Researcher at Helwan University
Publications - 140
Citations - 2596
Ashraf Darwish is an academic researcher from Helwan University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 19, co-authored 125 publications receiving 1742 citations. Previous affiliations of Ashraf Darwish include Cairo University.
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Journal ArticleDOI
Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring
TL;DR: The important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care is explained.
Journal ArticleDOI
The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems
Ashraf Darwish,Ashraf Darwish,Aboul Ella Hassanien,Mohamed Elhoseny,Mohamed Elhoseny,Arun Kumar Sangaiah,Khan Muhammad,Khan Muhammad +7 more
TL;DR: A comprehensive review of the current literature on integration of CC and IoT to solving various problems in healthcare applications such as smart hospitals, medicine control, and remote medical services and a new concept of the integration ofCC and IoT for healthcare applications, called the CloudIoT-Health paradigm is presented.
Journal ArticleDOI
Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications
TL;DR: state-of-art of nine of recent bio-inspired algorithms, gap analysis, and its applications namely; Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Al algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithms (ESA), Chicken Swarmoptimization Algorithm(CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization(GW
Book ChapterDOI
Hybrid Intelligent Intrusion Detection Scheme
TL;DR: A hybrid scheme that combines the advantages of deep belief network and support vector machine to classify the intrusion into five outcome; Normal, R2L, DoS, U2R, and Probing is introduced.
Journal ArticleDOI
An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis
TL;DR: The problem of the imbalanced used dataset has been solved by using random minority oversampling and random majority undersampling methods, and some restrictions in terms of both the number and diversity of samples have been overcome.