A Drowsiness Detection Decision Support System using Self-Organising Map

被引：0

作者：

Emmanuel, Fabunmi Temitayo ^{[1
]}

Ojo, Adedayo Olukayode ^{[1
]}

Gbadamosi, Saheed Lekan ^{[1
]}

机构：

[1] Afe Babalola Univ Ado Ekiti ABUAD, Dept Elect Elect & Comp Engn, Coll Engn, Ado Ekiti, Ekiti State, Nigeria

来源：

2022 IEEE NIGERIA 4TH INTERNATIONAL CONFERENCE ON DISRUPTIVE TECHNOLOGIES FOR SUSTAINABLE DEVELOPMENT (IEEE NIGERCON) | 2022年

关键词：

Drowsiness detection; neuromodels; decision support system; self-organizing map; artificial neural networks; CLASSIFICATION;

D O I：

10.1109/NIGERCON54645.2022.9803139

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Machine learning is currently having its large share of attention in terms of application in several fields and unsupervised learning techniques are at the forefront of this trend. This article harnesses the classification properties of Self-Organizing Maps (SOMs) to accurately classify acquired signals from car drivers in order to support decision-making mechanism in drowsiness detection system. The drowsiness detection SOM facilitates easier and more rapid representation of the input vector space which helps in understanding the intertwined relationship between different inputs as well as categorize inputs in terms of relevance and significance to overall drowsiness detection process.

引用

页码：307 / 310

页数：4

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