Intelligent algorithms for determining physiological states of yawning for fatigue assessment

Назаров, Файзулло, Хамидов, Мунис

Рақамли технологияларнинг назарий ва амалий масалалари · 2024-yil

Annotatsiya

In this study, yawning, which is considered one of the physiological deviations of humans, mouth opening state classification and several machine learning algorithms were used. The CNN model outperformed traditional machine learning methods such as SVM, MLP and KNN with an accuracy of 97.07%. In the study, a dataset was formed using mouth part images extracted from face images using CNN and the model was trained. In addition, the overall performance of the model is improved by dataset normalization and dropout methods. The results demonstrate the effectiveness of the CNN algorithm in detecting the mouth opening state (yawning) and open the possibility for further improvement of fatigue state detection systems in the future.

Maqola ma’lumotlari
MualliflarНазаров, Файзулло, Хамидов, Мунис
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2024-10-09
Jild7
Son3
Betlar131-136
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v7i3.207
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v7i3.207 · Maqolaning asl sahifasi

Kalit so‘zlar

обнаружение зевания, сверточные нейронные сети (CNN), машинное обучение, классификация изображений, извлечение признаков изображения, yawn detection, convolutional neural networks (CNN), machine learning, image classification, image feature extraction

Ilmiy soha

Texnika

Iqtibos keltirish

APAНазаров, Файзулло, & Хамидов, Мунис (2024). Intelligent algorithms for determining physiological states of yawning for fatigue assessment. Рақамли технологияларнинг назарий ва амалий масалалари, 7(3), 131-136. https://doi.org/10.62132/ijdt.v7i3.207
GOST (OAK)Назаров, Файзулло, Хамидов, Мунис Intelligent algorithms for determining physiological states of yawning for fatigue assessment // Рақамли технологияларнинг назарий ва амалий масалалари. – 2024. – T. 7, № 3. – P. 131-136. – DOI: 10.62132/ijdt.v7i3.207.
MLAНазаров, Файзулло, and Хамидов, Мунис. “Intelligent algorithms for determining physiological states of yawning for fatigue assessment.” Рақамли технологияларнинг назарий ва амалий масалалари, 7(3), 131-136, 2024. https://doi.org/10.62132/ijdt.v7i3.207
ChicagoНазаров, Файзулло, and Хамидов, Мунис. “Intelligent algorithms for determining physiological states of yawning for fatigue assessment.” Рақамли технологияларнинг назарий ва амалий масалалари, 7(3), 131-136 (2024). https://doi.org/10.62132/ijdt.v7i3.207
HarvardНазаров, Файзулло, and Хамидов, Мунис (2024) ‘Intelligent algorithms for determining physiological states of yawning for fatigue assessment’, Рақамли технологияларнинг назарий ва амалий масалалари, 7(3), 131-136. https://doi.org/10.62132/ijdt.v7i3.207
BibTeX
@article{article2024,
  title = {Intelligent algorithms for determining physiological states of yawning for fatigue assessment},
  author = {Назаров, Файзулло and Хамидов, Мунис},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2024},
  volume = {7},
  number = {3},
  pages = {131-136},
  doi = {10.62132/ijdt.v7i3.207},
  url = {https://doi.org/10.62132/ijdt.v7i3.207}
}

Boshqa maqola uchun iqtibos tayyorlash

Рақамли технологияларнинг назарий ва амалий масалалари jurnalidan boshqa maqolalar

Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar · 2024-yil arxivi

Назаров, Файзулло — boshqa maqolalari

Texnika sohasidagi boshqa maqolalar

Texnika sohasidagi OAK jurnallari