M
Mohammed Aledhari
Researcher at Kennesaw State University
Publications - 24
Citations - 7082
Mohammed Aledhari is an academic researcher from Kennesaw State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 8, co-authored 22 publications receiving 5122 citations. Previous affiliations of Mohammed Aledhari include Western Michigan University.
Papers
More filters
Journal ArticleDOI
Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.
Osama Shahid,Mohammad Nasajpour,Seyedamin Pouriyeh,Reza M. Parizi,Meng Han,Maria Valero,Fangyu Li,Mohammed Aledhari,Quan Z. Sheng +8 more
TL;DR: A comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus can be found in this article, where the authors present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective.
Posted Content
Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance
Osama Shahid,Mohammad Nasajpour,Seyedamin Pouriyeh,Reza M. Parizi,Meng Han,Maria Valero,Fangyu Li,Mohammed Aledhari,Quan Z. Sheng +8 more
TL;DR: A journey of what role ML has played so far in combating the COVID-19 virus, mainly looking at it from a screening, forecasting, and vaccine perspective is presented.
Proceedings ArticleDOI
Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs
TL;DR: A deep learning algorithm based on convolutional neural networks to identify and classify pneumonia cases from chest x-ray images has the potential to predict at higher accuracy than human specialists and will help subsidies and reduce the cost of diagnosis across the globe.
Proceedings ArticleDOI
A new cryptography algorithm to protect cloud-based healthcare services
TL;DR: The proposed system is based on an emerging innovative technology between the genomic encryptions and the deterministic chaos method to provide a quick and secure cryptography algorithm for real-time health monitoring that permits for threats to patient confidentiality to be addressed.
Journal ArticleDOI
A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets
TL;DR: A novel deep learning-based data minimization algorithm that minimizes the datasets during transfer over the carrier channels and protects the data from the man-in-the-middle (MITM) and other attacks by changing the binary representation several times for the same dataset.