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  • Open Access

    ARTICLE

    A Numerical Study of the Caputo Fractional Nonlinear Rössler Attractor Model via Ultraspherical Wavelets Approach

    Ashish Rayal1, Priya Dogra1, Sabri T. M. Thabet2,3,4,*, Imed Kedim5, Miguel Vivas-Cortez6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1895-1925, 2025, DOI:10.32604/cmes.2025.060989 - 30 May 2025

    Abstract The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns, biological systems, and secure communications. So, this work aims to present the numerical performances of the nonlinear fractional Rössler attractor system under Caputo derivatives by designing the numerical framework based on Ultraspherical wavelets. The Caputo fractional Rössler attractor model is simulated into two categories, (i) Asymmetric and (ii) Symmetric. The Ultraspherical wavelets basis with suitable collocation grids is implemented for comprehensive error analysis in the solutions of More >

  • Open Access

    ARTICLE

    WACPN: A Neural Network for Pneumonia Diagnosis

    Shui-Hua Wang1, Muhammad Attique Khan2, Ziquan Zhu1, Yu-Dong Zhang1,*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 21-34, 2023, DOI:10.32604/csse.2023.031330 - 16 August 2022

    Abstract Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of More >

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