M
Maha Driss
Researcher at Taibah University
Publications - 52
Citations - 817
Maha Driss is an academic researcher from Taibah University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 10, co-authored 37 publications receiving 283 citations. Previous affiliations of Maha Driss include University of Rennes & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions
TL;DR: This survey provides a review of the literature regarding the use of IoT and DL to develop smart cities and outlines the current challenges and issues faced during the development of smart city services.
Journal ArticleDOI
A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity.
TL;DR: In this paper, the authors conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020, and identified 93 papers from the review articles as primary studies from an initial pool of over 700 papers addressing obesity.
Journal ArticleDOI
A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations
Fawad Masood,Maha Driss,Wadii Boulila,Jawad Ahmad,Sadaqat Ur Rehman,Sana Ullah Jan,Abdullah Qayyum,William J. Buchanan +7 more
TL;DR: The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
Journal ArticleDOI
RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification
Wadii Boulila,Wadii Boulila,Mokhtar Sellami,Maha Driss,Maha Driss,Mohammed Al-Sarem,Mahmood Safaei,Fuad A. Ghaleb +7 more
TL;DR: In this paper, a distributed convolutional-neural-networks (DCNN) based approach for big remote sensing image classification is proposed, which is the first study of its kind, which proposes a novel distributed deep learning-based approach for the classification of big Remote Sensing images.
Proceedings ArticleDOI
Selection of Composable Web Services Driven by User Requirements
TL;DR: This paper uses a real case study of 901 services to show how to accomplish an efficient selection of services satisfying a specified set of functional and non-functional requirements.