BOUDERAH Brahim

Prof. BOUDERAH Brahim

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Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 December 2024 in Fractal and Fractional
Verified
Journal Article
Open access
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AAAwatif Muflih Alqahtani
Awatif Muflih Alqahtani
HMHamza Mihoubi
Hamza Mihoubi
YAYacine ARIOUA
Yacine ARIOUA
BOUDERAH BrahimBOUDERAH Brahim
BOUDERAH Brahim
The aim of this article is to introduce analytical and approximate techniques to obtain the solution of time-fractional Navier–Stokes equations. This proposed technique consists is coupling the homotopy perturbation method (HPM) and Laplace transform (LT). The time-fractional derivative used is the Caputo–Hadamard fractional derivative (CHFD). The effectiveness of this method is demonstrated and validated through two test problems. The results show that the proposed method is robust, efficient, and easy to implement for both linear and nonlinear problems in science and engineering. Additionally, its computational efficiency requires less computation compared to other schemes.
1 January 2025 in Journal of Intelligent Systems
Verified
Journal Article
Open access
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YBYaakoub Boualleg
Yaakoub Boualleg
KDKheir Eddine Daouadi
Kheir Eddine Daouadi
OGOussama Guehairia
Oussama Guehairia
CDChawki Djeddi
Chawki Djeddi
ACAbbas Cheddad
Abbas Cheddad
BOUDERAH BrahimBOUDERAH Brahim
BOUDERAH Brahim
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent severe visual impairment. Despite numerous proposed classification techniques, challenges persist due to the high parameter count of deep learning algorithms, imbalanced datasets, and limited performance. This study introduces a novel framework for DR classification that leverages multi-view deep features, multilinear whitened principal component analysis, tensor exponential discriminant analysis, synthetic minority oversampling technique, and deep random forest. We evaluated this architecture using the APTOS blindness dataset under a standard protocol. The results demonstrate that our architecture significantly improves classification accuracy, surpassing existing methods. Our contributions highlight a promising approach for enhancing DR classification performance.