Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Saber, Mohamed
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Low power pseudo-random number generator based on lemniscate chaotic map Saber, Mohamed; Eid, Marwa M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp863-871

Abstract

Lemniscate chaotic map (LCM) provides a wide range of control parameters, canceling the need for several rounds of substitutions, and excellent performance in the confusion process. Unfortunately, the hardware model of LCM is complex and consumes high power. This paper presents a proposed low power hardware model of LCM called practical lemniscate chaotic map (P-LCM) depending on trigonometric identities to reduce the complexity of the conventional model. The hardware model designed and implement into the field programmable gate array (FPGA) board, Spartan-6 SLX45FGG484-3. The proposed model achieves a 48.3 % reduction in used resources and a 34.6 % reduction in power consumption compared to the conventional LCM. We also introduce a new pseudo-random number generator based on a proposed low power P-LCM model and perform the randomization tests for the proposed encryption system.
Object detection for indoor mobile robot: deep learning approaches review Messbah, Hind; Emharraf, Mohamed; Saber, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3520-3527

Abstract

Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Co-Authors Eid, Marwa M. Emharraf, Mohamed Messbah, Hind