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- W2998809266 abstract "Alcohol consumption always leads to a lot of physical and mental health problems. The constant use of alcohol affects almost every part of the human body especially the brain, liver, pancreas, heart and the immune system. Also, there are numerous social and economic challenges like weakness in memory and concentration, lack of proper decision-making skills, emotional and cognitive impairments etc. Therefore, the alcohol detection level of a particular patient has to be known in order to assess the capability of the alcoholic patient to perform a specific task. In this research work, the concept of Correlation Dimension (CD) and the Bayesian Linear Discriminant Analysis (BLDA) is employed to assess the risk rate of alcohol for a single alcoholic patient with the aid of Electroencephalography (EEG) signals. The enumerated Results report a total classification accuracy of 82.016% when CD is used and an average classification accuracy of 84.97% is obtained when BLDA is utilized." @default.
- W2998809266 created "2020-01-23" @default.
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- W2998809266 date "2020-01-01" @default.
- W2998809266 modified "2023-09-27" @default.
- W2998809266 title "Correlation Dimension and Bayesian Linear Discriminant Analysis for Alcohol Risk Level Detection" @default.
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- W2998809266 doi "https://doi.org/10.1007/978-3-030-37218-7_65" @default.
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