Browsing by Author "Taleb, A."

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    Analytical and numerical analysis of bifurcations in thermal convection of viscoelastic fluids saturating a porous square box
    (American Institute of Physics, 2016) Taleb, A.; BenHamed, H.; Ouarzazi, M. N.; Beji, H.
    We report theoretical and numerical results on bifurcations in thermal instability for a viscoelastic fluid saturating a porous square cavity heated from below. The modified Darcy law based on the Oldroyd-B model was used for modeling the momentum equation. In addition to Rayleigh number ℜ, two more dimensionless parameters are introduced, namely, the relaxation time λ1 and the retardation time λ2. Temporal stability analysis showed that the first bifurcation from the conductive state may be either oscillatory for sufficiently elastic fluids or stationary for weakly elastic fluids. The dynamics associated with the nonlinear interaction between the two kinds of instabilities is first analyzed in the framework of a weakly nonlinear theory. For sufficiently elastic fluids, analytical expressions of the nonlinear threshold above which a second hysteretic bifurcation from oscillatory to stationary convective pattern are derived and found to agree with two-dimensional numerical simulations of the full equations. Computations performed with high Rayleigh number indicated that the system exhibits a third transition from steady single-cell convection to oscillatory multi-cellular flows. Moreover, we found that an intermittent oscillation regime may exist with steady state before the emergence of the secondary Hopf bifurcation. For weakly elastic fluids, we determined a second critical value ℜOsc 2 (λ1, λ2) above which a Hopf bifurcation from steady convective pattern to oscillatory convection occurs. The well known limit of ℜOsc 2 (λ1 = 0, λ2 = 0) = 390 for Newtonian fluids is recovered, while the fluid elasticity is found to delay the onset of the Hopf bifurcation. The major new findings were presented in the form of bifurcation diagrams as functions of viscoelastic parameters for ℜ up to 420. Published by AIP Publishing
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    Optimizing Gene Selection for Cancer Classification Using Mutual Information and the Social Spider Algorithm
    (IEEE, 2025) Cherif, Chahira; Maiza, Mohammed; Chouraqui, Samira; Taleb, A.
    Cancer remains a major global health challenge, underscoring the need for advanced methods to enable early and accurate diagnosis. While microarray technology facilitates high-throughput gene expression profiling, the high dimensionality of the data poses challenges for effective cancer classification. To address this limitation, we propose a novel hybrid approach combining the Social Spider Optimization (SSO) algorithm with Mutual Information (MI)-based feature selection techniques-including Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and Max-Relevance MinRedundancy (MRMR)-to identify the most discriminative genes. We evaluate four machine learning classifiers-Random Forest (RF), XGBoost (XGB), Neural Networks (NN), and Support Vector Machines (SVM)-with and without feature selection. Our results demonstrate that SSO-enhanced feature selection significantly improves classification accuracy, with SVM paired with MRMR achieving near-perfect performance on leukemia and lymphoma datasets. Moreover, MIM and JMI exhibit competitive performance in reducing data redundancy and enhancing computational efficiency. The proposed method effectively optimizes feature selection, providing a robust framework for improved cancer diagnosis