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  • 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

    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 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%,… More >

  • Open Access

    ARTICLE

    High Efficiency Crypto-Watermarking System Based on Clifford-Multiwavelet for 3D Meshes Security

    Wajdi Elhamzi1,2,*, Malika Jallouli3, Yassine Bouteraa1,4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4329-4347, 2022, DOI:10.32604/cmc.2022.030954

    Abstract Since 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users and saved in distant multimedia databases. The novelty of our approach is that it uses a new Clifford-multiwavelet transform to insert copyright data in a multiresolution domain, allowing us to greatly expand the size of the watermark. After that, our method does two rounds of insertion, each applying a different type of Clifford-wavelet transform. Before being placed into the Clifford-multiwavelet coefficients, the watermark, which is a mixture of the mesh description, source mesh signature (produced using SHA512),… More >

  • Open Access

    ARTICLE

    Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

    Chaosheng Tang1, Deepak Ranjan Nayak2, Shuihua Wang1,3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069

    Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More >

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