Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization
SH Wang, K Muhammad, J Hong, AK Sangaiah… - Neural Computing and …, 2020 - Springer
Alcoholism changes the structure of brain. Several somatic marker hypothesis network-
related regions are known to be damaged in chronic alcoholism. Neuroimaging approach …
related regions are known to be damaged in chronic alcoholism. Neuroimaging approach …
Alcoholism identification based on an AlexNet transfer learning model
SH Wang, S Xie, X Chen, DS Guttery, C Tang… - Frontiers in …, 2019 - frontiersin.org
Aim: This paper proposes a novel alcoholism identification approach that can assist
radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning …
radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning …
Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure.
Recently, scholars tend to use computer vision based techniques to detect AUD. We …
Recently, scholars tend to use computer vision based techniques to detect AUD. We …
[HTML][HTML] VISPNN: VGG-inspired stochastic pooling neural network
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 …
paper aims to design a novel artificial intelligence model that can recognize alcoholism …
Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI
Currently, classification of alcohol use disorder (AUD) is made on clinical grounds; however,
robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry …
robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry …
Classification of alcoholic EEG signals using a deep learning method
Most of the traditional alcoholism detection methods are developed based on machine
learning based methods that cannot extract the deep concealed characteristics of …
learning based methods that cannot extract the deep concealed characteristics of …
Identification of Alcoholism Based on Wavelet Renyi Entropy and Three‐Segment Encoded Jaya Algorithm
SH Wang, K Muhammad, Y Lv, Y Sui, L Han… - …, 2018 - Wiley Online Library
The alcohol use disorder (AUD) is an important brain disease, which could cause the
damage and alteration of brain structure. The current diagnosis of AUD is mainly done …
damage and alteration of brain structure. The current diagnosis of AUD is mainly done …
Automated alcoholism detection using fourier-bessel series expansion based empirical wavelet transform
In this paper, the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-
EWT) is proposed for automated alcoholism detection using electroencephalogram (EEG) …
EWT) is proposed for automated alcoholism detection using electroencephalogram (EEG) …
An EEG-based machine learning method to screen alcohol use disorder
Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity
involved in the process. Hence, robust and objective methods are needed to automate the …
involved in the process. Hence, robust and objective methods are needed to automate the …
An EEG-based functional connectivity measure for automatic detection of alcohol use disorder
Background The abnormal alcohol consumption could cause toxicity and could alter the
human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately …
human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately …