Enhancing Diagnosis Precision in Alcohol Addiction Detection Through CNN Analysis with SMOTE-ENN Data Augmentation

Alcoholism is a major concern in modern culture, demanding effective and prompt detection measures to limit its negative consequences. Electroencephalography (EEG) data processing has emerged as a viable option for detecting alcoholism by utilizing the brain's unique electrical patterns. Howeve...

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Vydané v:	2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) s. 520 - 526
Hlavní autori:	Ashif H, Mohamed, Kanaga, E. Grace Mary
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 03.05.2024
Predmet:	Accuracy Addiction Alcohol Addiction Alcoholism Brain modeling CNN Data augmentation Data Augmentation Techniques Deep learning Deep Learning Algorithms EEG KNN LSTM Machine learning algorithms Naive Bayes Random Forest SMOTE SMOTE-ENN SMOTE-TOMEK
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