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Driver Dozy Discernment Using Neural Networks with SVM Variants
Muskan Kamboj
, Janaki Bhagya Sri
, Tarusree Banik
, Swastika Ojha
, Karuna Kadian
, Vimal Dwivedi
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Keyphrases
Accuracy Function
33%
Artificial Intelligence Learning
33%
Classification Methods
33%
Closed Eyes
33%
Continuous Monitoring
33%
Convolutional Neural Network
33%
Convolutional Neural Network Model
33%
Driver Awareness
33%
Driver Condition
33%
Driver Distraction
33%
Driver Drowsiness
33%
Driver Drowsiness Detection
33%
Drowsiness
66%
Effective Techniques
33%
Eye Closure
33%
Eye Movements
33%
Eyes Open
33%
Face Movement
33%
Further Training
33%
Image Processing Techniques
33%
India
33%
Lack of Concentration
33%
Loss Function
33%
Machine Learning Algorithms
33%
Neural Network
100%
Real-world Application
33%
Road Traffic Accidents
33%
Support Vector Machine
100%
Computer Science
Artificial Intelligence
33%
Classification Technique
33%
Convolutional Neural Network
66%
Drowsiness Detection
33%
Image Processing Technique
33%
Machine Learning Algorithm
33%
Neural Network
100%
Neural Network Model
33%
Support Vector Machine
100%
World Application
33%
Engineering
Artificial Intelligence
33%
Convolutional Neural Network
66%
Highway Accidents
33%
Image Processing
33%
Loss Function
33%
Machine Learning Algorithm
33%
Main Reason
33%
Network Model
33%
Processing Technique
33%
Real World Application
33%
Support Vector Machine
100%
Medicine and Dentistry
Awareness
100%
Eye Movement
100%
Eyelid Closure
100%
Chemical Engineering
Artificial Intelligence
33%
Learning System
33%
Neural Network
100%
Support Vector Machine
100%