R
Rashid Mehmood
Researcher at King Abdulaziz University
Publications - 188
Citations - 4449
Rashid Mehmood is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Big data & Computer science. The author has an hindex of 29, co-authored 147 publications receiving 3050 citations. Previous affiliations of Rashid Mehmood include University of Cambridge & Swansea University.
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Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
TL;DR: The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments).
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Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications
TL;DR: The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare, and a cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described.
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Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)
TL;DR: Preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds, compared to ANNs and DLANNs, which can provide highly accurate results but are computationally expensive.
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UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities
TL;DR: A ubiquitous healthcare framework, UbeHealth, is proposed that leverages edge computing, deep learning,big data, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges.
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Exploring the influence of big data on city transport operations: a Markovian approach
TL;DR: The authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city and suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption.