Elsayed A. Sallam
Tanta University
39 Papers
138 Citations
Elsayed A. Sallam is an academic researcher from Tanta University. The author has contributed to research in topics: Computer science & Sliding mode control. The author has an hindex of 9, co-authored 39 publications.
Chat about Author
Papers
A hybrid network intrusion detection framework based on random forests and weighted k-means
TL;DR: In the proposed hybrid framework, the anomaly part is improved by replacing the k-means algorithm with another one called weighted k-Means algorithm, moreover, it uses a proposed method in choosing the anomalous clusters by injecting known attacks into uncertain connections data.
190
Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators
Ahmed F. Amer,Elsayed A. Sallam,Wael M. Elawady +2 more
- 01 Dec 2011
TL;DR: Simulation results that are compared with the results of conventional SMC with PID sliding surface indicate that the control performance of the robot system is satisfactory and the proposed AFSMC can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.
145
Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images
TL;DR: A feature representation with a stacked sparse auto-encoder that is based on deep learning technology is proposed that outperforms the three state-of-the-art techniques for classification of medical images.
109
Deep Belief Networks-based framework for malware detection in Android systems
TL;DR: The proposed framework merges high level static analysis, dynamic analysis and system calls in feature extraction in order to achieve the highest accuracy in malware detection and demonstrates that Deep Belief Networks technique can realize 99.1% accuracy with the presented dataset.
44
•Proceedings Article
Enhancing the Vector-Based Forwarding Routing Protocol for Underwater Wireless Sensor Networks: A Clustering Approach
Dina M. Ibrahim,Tarek E. Eltobely,Mahmoud Fahmy,Elsayed A. Sallam +3 more
- 22 Jun 2014
TL;DR: Simulation results demonstrate that the proposed algorithm reduces the energy consumption especially in dense networks, increases the packet delivery ratio especially in sparse networks, and decreases the average end-to-end delay in both sparse and dense networks.