Computing in Kent

 A Recent paper on using Voice Recognition and machine learning to detect drowsiness when driving. Jasim, S. S., Abdul Hassan , A. K. and Turner , S. (2022) “Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition”,  ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY , 10(2), pp. 142-151. doi: 10.14500/aro.11000. Full text at:  https://aro.koyauniversity.org/index.php/aro/article/view/1000 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 re...

  Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking Sarah S. Jasim Department of IT, Technical College of Management-Baghdad, Middle Technical University, Baghdad, Iraq https://orcid.org/0000-0002-1237-147X Alia K. Abdul Hassan Department of Computer Science, University of Technology, Baghdad, Iraq https://orcid.org/0000-0002-6835-8872 Scott Turner Director of Computing, School of Engineering, Design, and Technology, Canterbury Christ Church University, Kent, United Kingdom https://orcid.org/0000-0003-2735-3220 DOI:  https://doi.org/10.14500/aro.10928 Keywords:  Artificial neural network, Drowsiness, Feature extraction, Gray wolf optimizer, Normalization, Segmentation ABSTRACT It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (...