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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Azouaoui, Ahmed
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Dual soft decoding of linear block codes using memetic algorithm Sliman, Rajaa; Azouaoui, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5263-5273

Abstract

In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting code is equivalent to the ratio k/n, the rate is belong the interval [0,1]. Hence a good code is the one that ensures a certain error correcting capability at minimum ratio. The results proved that this approach using a combination of genetic algorithm and local search algorithm provides a sufficiently good solution to an optimization problem; the new decoder is applied on linear codes where the structure is given by a parity check matrix.
Tifinagh handwritten character recognition using optimized convolutional neural network Niharmine, Lahcen; Outtaj, Benaceur; Azouaoui, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4164-4171

Abstract

Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
Enhance big data security based on HDFS using the hybrid approach Zine-Dine, Fayçal; Alcabnani, Sara; Azouaoui, Ahmed; El Kafi, Jamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1256-1264

Abstract

Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Hadoop has become increasingly critical. In this study, the authors introduce a novel strategy aimed at bolstering data security within the Hadoop storage framework. This approach specifically employs a hybrid encryption technique that leverages the advantages of both advanced encryption standard (AES) and data encryption standard (DES) algorithms, whereby files are encrypted in HDFS and subsequently decrypted during the map task. To assess the efficacy of this method, the authors performed experiments with various file sizes, benchmarking the outcomes against other established security measures.
Dual simulated annealing soft decoder for linear block codes Alaoui, Hicham Tahiri; Azouaoui, Ahmed; El Kafi, Jamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2776-2787

Abstract

This paper proposes a new approach to soft decoding for linear block codes called dual simulated annealing soft decoder (DSASD) which utilizes the dual code instead of the original code, using the simulated annealing algorithm as presented in a previously developed work. The DSASD algorithm demonstrates superior decoding performance across a wide range of codes, outperforming classical simulated annealing and several other tested decoders. We conduct a comprehensive evaluation of the proposed algorithm's performance, optimizing its parameters to achieve the best possible results. Additionally, we compare its decoding performance and algorithmic complexity with other decoding algorithms in its category. Our results demonstrate a gain in performance of approximately 2.5 dB at a bit error rate (BER) of 6×10⁻⁶ for the LDPC (60,30) code.
Co-Authors Alaoui, Hicham Tahiri Alcabnani, Sara El Kafi, Jamal Niharmine, Lahcen Outtaj, Benaceur Sliman, Rajaa Zine-Dine, Fayçal