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

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Customized convolutional neural networks for Moroccan traffic signs classification Khalloufi, Fatima Ezzahra; Rafalia, Najat; Abouchabaka, Jaafar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp469-476

Abstract

Recognition of traffic signs is a challenging task that can enhance road safety. Deep neural networks have demonstrated remarkable results in numerous applications, such as traffic signs recognition. In this paper, we propose an innovative and efficient system for recognizing traffic signs, based on customized convolutional neural network (CNN) developed through hyperparameters optimization. The effectiveness of the proposed system is assessed using a novel dataset, the Moroccan traffic signs dataset. The results show that the proposed design recognizes traffic signs with an accuracy of 0.9898, outperforming several CNN architectures such as VGGNet, DensNet, and ResNet.
Advancing elderly care through big data analytics and machine learning for daily activity characterization Allali, Ayoub; Bouanani, Nouama; Abouchabaka, Ibtihal; Rafalia, Najat
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1969-1975

Abstract

Confronted with the ongoing demographic shift characterized by an aging population, society grapples with emerging challenges that extend beyond the provision of targeted health services for the elderly. The focus has broadened to encompass the promotion of well-being and vitality throughout the aging process. Addressing these multifaceted issues demands a comprehensive approach that integrates biomedical components with physical, psychological, and social interventions. In the context of my project, a unique strategy is employed, placing significant emphasis on leveraging big data analytics and machine learning. The primary objective is to systematically observe and characterize the physiological conditions of the elderly, facilitating healthcare professionals in monitoring behaviors and promoting active aging. This undertaking involves meticulous data collection and analysis, employing machine learning algorithms (support vector machine (SVM), gradient boosting) within a framework that harnesses extensive data analytics. Ultimately, this approach enables the identification and characterization of daily routines and physiological states of individuals, contributing to a holistic understanding of aging.
Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction Rafalia, Najat; Moumen, Idriss; Raji, Fatima Zahra; Abouchabaka, Jaafar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp503-510

Abstract

The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.
A comparative analysis of GPUs, TPUs, DPUs, and QPUs for deep learning with python Allali, Ayoub; El Falah, Zineb; Sghir, Ayoub; Abouchabaka, Jaafar; Rafalia, Najat
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.pp1324-1330

Abstract

In the rapidly evolving field of deep learning, the computational demands for training sophisticated models have escalated, prompting a shift towards specialized hardware accelerators such as graphics processing units (GPUs), tensor processing units (TPUs), data processing units (DPUs), and quantum processing units (QPUs). This article provides a comprehensive analysis of these heterogeneous computing architectures, highlighting their unique characteristics, performance metrics, and suitability for various deep learning tasks. By leveraging python, a predominant programming language in the data science domain, the integration and optimization techniques applicable to each hardware platform is explored, offering insights into their practical implications for deep learning research and application. the architectural differences that influence computational efficiency is examined, parallelism, and energy consumption, alongside discussing the evolving ecosystem of software tools and libraries that support deep learning on these platforms. Through a series of benchmarks and case studies, this study aims to equip researchers and practitioners with the knowledge to make informed decisions when selecting hardware for their deep learning projects, ultimately contributing to the acceleration of model development and innovation in the field.
Co-Authors Abouchabaka, Ibtihal Abouchabaka, Jaafar Allali, Ayoub Bouanani, Nouama El Falah, Zineb Khalloufi, Fatima Ezzahra Moumen, Idriss Raji, Fatima Zahra Sghir, Ayoub