H
Hesham F. A. Hamed
Researcher at Minia University
Publications - 152
Citations - 1447
Hesham F. A. Hamed is an academic researcher from Minia University. The author has contributed to research in topics: Encryption & CMOS. The author has an hindex of 14, co-authored 135 publications receiving 775 citations. Previous affiliations of Hesham F. A. Hamed include Egyptian Russian University & Ohio University.
Papers
More filters
Journal ArticleDOI
Intrusion detection systems for IoT-based smart environments: a survey
TL;DR: A comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms, and deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT Architecture is provided.
Journal ArticleDOI
A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
Mahmoud Khaled Abd-Ellah,Ali Ismail Awad,Ali Ismail Awad,Ashraf A. M. Khalaf,Hesham F. A. Hamed +4 more
TL;DR: This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis and identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes.
Journal ArticleDOI
Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
Mahmoud Khaled Abd-Ellah,Ali Ismail Awad,Ali Ismail Awad,Ashraf A. M. Khalaf,Hesham F. A. Hamed +4 more
TL;DR: The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature and demonstrated the superiority of the suggested system concerning both tumours detection and localization.
Proceedings ArticleDOI
Design and implementation of a computer-aided diagnosis system for brain tumor classification
TL;DR: A two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs) and has achieved promising results using a non-standard MRIs database.
Journal ArticleDOI
Hiding Data Using Efficient Combination of RSA Cryptography, and Compression Steganography Techniques
TL;DR: In this paper, a hybrid data compression algorithm was proposed to increase the security level of the compressed data by using RSA (Rivest-Shamir-Adleman) cryptography.