M
Mohamed Elhoseny
Researcher at Mansoura University
Publications - 287
Citations - 11252
Mohamed Elhoseny is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 49, co-authored 240 publications receiving 7044 citations. Previous affiliations of Mohamed Elhoseny include Maharaja Agrasen Institute of Technology & Cairo University.
Papers
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Journal ArticleDOI
Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G
Denis A. Pustokhin,Irina V. Pustokhina,Poonam Rani,Vineet Kansal,Mohamed Elhoseny,Mohamed Elhoseny,Gyanendra Prasad Joshi,K. Shankar +7 more
TL;DR: A Link-based Quasi Oppositional Binary Particle Swarm Optimization Algorithm is used in feature selection to narrow down an optimal set of features and the application of quasi-oppositional mechanism in BPSO algorithm helps in increasing the convergence rate.
Book ChapterDOI
Internet of Vehicles Over Named Data Networking: Current Status and Future Challenges
Chaker Abdelaziz Kerrche,Farhan Ahmad,Mohamed Elhoseny,Asma Adnane,Zeeshan Ahmad,Boubakr Nour +5 more
TL;DR: Details of IoV over NDN are provided, focused on the architectural details and requirements of NDN-enabled IoV, which advocates for content-centric communication paradigm for IoV applications where the information is of the essence than the source and/or location of information.
Journal ArticleDOI
IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain
TL;DR: An IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain that uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data and achieves efficient services for the healthcare system.
Journal ArticleDOI
Leveraging mist and fog for big data analytics in IoT environment
TL;DR: A proposed hybrid real‐time remote patient monitoring framework introduced that consists of the integration among the mist, fog, and cloud for healthcare treatment, which remote‐monitors patients continuously.
Book ChapterDOI
Extending Homogeneous WSN Lifetime in Dynamic Environments Using the Clustering Model
TL;DR: This chapter proposes a new clustering model for WSN used in dynamic environments that implies that the sensor nodes shared the burden of relaying messages and, hence, elongated the overall network life.