Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences
Bri Seddik
Moulay Ismail University
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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

Found 2 Documents
Search

 1 
Analytic Estimation of Two-Dimensional Electron Gas Density and Current-Voltage Characteristic in AlGaN/GaN HEMT’s Asmae Babaya; Bri Seddik; Saadi Adil
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (409.08 KB) | DOI: 10.11591/ijece.v8i2.pp954-962

Abstract

This paper is mainly dedicated to understand the phenomena governing the formation of two-dimensional electron gas (2DEG) confined in the quantum well which hold the role of the channel in the high electron density transistors (HEMT) based on AlGaN / GaN heterojunction. The theory takes into account: the crystal structure, the spontaneous and piezoelectric polarization concept, the formation mechanism of two-dimensional electron gas at the AlGaN / GaN interface, the approximate resolution of the Poisson and Schrödinger equations to determine the density of Two-dimensional electron gas after the analytical formula of the current-voltage characteristic is established. Our study is also concerned with the dependence of the two-dimensional electron gas density on the following technological parameters: Aluminum molare fraction, AlGaN layer thickness and AlGaN layer doping, In order to control the influence of these parameters on the device performance. Finally, the current-voltage characteristic which reflects the variation of the drain-source current as a function of the modulation of the gate voltage has been discussed.
Intrusions detection using optimized support vector machine Mehdi Moukhafi; Khalid El Yassini; Bri Seddik
International Journal of Advances in Applied Sciences Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1007.916 KB) | DOI: 10.11591/ijaas.v9.i1.pp62-66

Abstract

Computer network technologies are evolving fast and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is main issue of computing because the number attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, specially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study proposes a new method of intrusion detection that uses support vector machine optimizing optimizing by a genetic algorithm. to improve the efficiency of detecting known and unknown attacks, we used a Particle Swarm Optimization algorithm to select the most influential features for learning the classification model.
Co-Authors Asmae Babaya Khalid El Yassini Mehdi Moukhafi Saadi Adil