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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Mohammed Bouhorma
University Abdelmalek Essaadi
Computer Science & IT Electrical & Electronics Engineering Engineering

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Novel Antennas for UHF RFID Tags: Design and Miniaturization Anas Sofi; Khalid Roky; Mohammed Bouhorma; Ibrahim Hadj Baraka
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 1: February 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (248.05 KB)

Abstract

This article focuses of study the nature and characteristics of the antenna, The collective electrical signals acquired from RFID antennas require advanced techniques for feeding, gains and radiation patterns. After an introduction to RFID technology itself (principle and characteristics of different RFID tags), the article offers some examples of applications of this technology in everyday life or in the industry. In order to use radio frequency identification (RFID) antenna for wireless communication and real world applications military and personal communication systems, mobile phones, personal digital assistant (PDA), blue-tooth systems, wireless local area networks (WLAN), road tolling systems, animal traceability etc, studying the nature and characteristics of the antenna is an important use. A novel printed antenna is proposed for Radio Frequency Identification. The antenna has a much wider bandwidth than known printed antenna, mostly planar antennas. The antenna geometry is much smaller than a printed dipole antenna at the same frequency band.DOI:http://dx.doi.org/10.11591/ijece.v4i1.4751
Machine learning based augmented reality for improved learning application through object detection algorithms Anasse Hanafi; Lotfi Elaachak; Mohammed Bouhorma
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1724-1733

Abstract

Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-of-the-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset Ikram Ben Abdel Ouahab; Lotfi Elaachak; Mohammed Bouhorma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1836-1844

Abstract

In recent times, malware visualization has become very popular for malwareclassification in cybersecurity. Existing malware features can easily identifyknown malware that have been already detected, but they cannot identify newand infrequent malwares accurately. Moreover, deep learning algorithmsshow their power in term of malware classification topic. However, we foundthe use of imbalanced data; the Malimg database which contains 25 malwarefamilies don’t have same or near number of images per class. To address theseissues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.
Co-Authors Anas Sofi Anasse Hanafi Ibrahim Hadj Baraka Ikram Ben Abdel Ouahab Khalid Roky Lotfi Elaachak