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...

Full description

Saved in:

Bibliographic Details
Published in:	2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) pp. 520 - 526
Main Authors:	Ashif H, Mohamed, Kanaga, E. Grace Mary
Format:	Conference Proceeding
Language:	English
Published:	IEEE 03.05.2024
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!