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Allali, Ayoub
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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.
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 Bouanani, Nouama El Falah, Zineb Rafalia, Najat Sghir, Ayoub