4 research outputs found
Driver drowsiness detection using Gray Wolf Optimizer based on voice recognition
Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respective
Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition
Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively
Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer
In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy
Ensemble learning using multi-objective optimisation for arabic handwritten words
Arabic handwriting recognition is a dynamic and stimulating field of study within
pattern recognition. This system plays quite a significant part in today's global
environment. It is a widespread and computationally costly function due to cursive
writing, a massive number of words, and writing style. Based on the literature, the
existing features lack data supportive techniques and building geometric features.
Most ensemble learning approaches are based on the assumption of linear
combination, which is not valid due to differences in data types. Also, the existing
approaches of classifier generation do not support decision-making for selecting the
most suitable classifier, and it requires enabling multi-objective optimisation to handle
these differences in data types. In this thesis, new type of feature for handwriting using
Segments Interpolation (SI) to find the best fitting line in each of the windows with a
model for finding the best operating point window size for SI features. Multi-Objective
Ensemble Oriented (MOEO) formulated to control the classifier topology and provide
feedback support for changing the classifiers' topology and weights based on the
extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated
as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons
and accuracy. Evaluation metrics from two perspectives classification and Multiobjective
optimization. The experimental design based on two subsets of the
IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22).
The features were tested with Support Vector Machine (SVM) and Extreme Learning
Machine (ELM). This work improved due to the SI feature. SI shows a significant
result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy
with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased
81% compared to NSGA-II 78%. Future work may consider introducing more features
to the system, applying them to other languages, and integrating it with sequence
learning for more accuracy