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  • Open Access


    VISPNN: VGG-Inspired Stochastic Pooling Neural Network

    Shui-Hua Wang1, Muhammad Attique Khan2, Yu-Dong Zhang3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.019447

    Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More >

  • Open Access


    Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier

    Xianqing Chen1,2, Yan Yan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 325-343, 2021, DOI:10.32604/cmes.2021.014489

    Abstract Alcoholism is an unhealthy lifestyle associated with alcohol dependence. Not only does drinking for a long time leads to poor mental health and loss of self-control, but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs. Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health. As their drinking increases, they become dependent on alcohol and it affects their daily lives. Therefore, it is important… More >

  • Open Access


    Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

    Emad-ul-Haq Qazi, Muhammad Hussain*, Hatim A. AboAlsamh

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3329-3348, 2021, DOI:10.32604/cmc.2021.013589

    Abstract The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain… More >

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