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Alcoholism Detection by wavelet energy entropy and Linear Regression Classifier

Computer Modeling in Engineering & Sciences 2021年第4期127卷 325-343页

作者： Xianqing Chen Yan Yan School of Electronic Science and Engineering Nanjing UniversityNanjing210093China Department of Electrical Engineering College of EngineeringZhejiang Normal UniversityJinhua321004China Educational Information Center Liming Vocational UniversityQuanzhou362000China

Alcoholism is an unhealthy lifestyle associated with alcohol *** 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 *** 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 *** their drinking increases,they become dependent on alcohol and it affects their daily ***,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as *** assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression *** with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.

关键词： Alcohol detection wavelet energy entropy linear regression classifier cross-validation computer-aided diagnosis

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Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy

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Journal of Coal Science & Engineering(China) 2012年第2期18卷 213-217页

作者： ZHANG Hua-rong QU Guo-qing RENTing School of Architectural Engineering Shandong University of TechnologyZibo 255049China

There are various influencing factors that affect the deformation observation, and deformation signals show differ- ent characteristics under different scales. wavelet analysis possesses multi-scale property, and the information entropy has great representational capability to the complexity of information. By hamming window to the wavelet coefficients and windowed wavelet energy obtained by multi-resolution analysis （MRA）, it can be achieved to measure the wavelet time entropy （WTE） and wavelet energy entropy （WEE）. The paper established deformation signals, selected the parameters, and compared the sin- gularity detection ability and anti-noise ability of two kinds of wavelet entropy and applied them to the singularity detection at the GPS continuously operating reference stations. It is shown that the WTE performs well in weak singularity information de- tection in finite frequency components signals and the WEE is more suitable for detecting the singularity in the signals with complex, strong background noise.

关键词： deformation signal wavelet time entropy wavelet energy entropy singularity detection

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