Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00007
Number of page(s) 13
DOI https://doi.org/10.1051/bioconf/20249700007
Published online 05 April 2024
  • Road traffic injuries (who.int) [Google Scholar]
  • Albadawi, Y., Takruri, M., & Awad, M. (2022). A review of recent developments in driver drowsiness detection systems. Sensors, 22(5), 2069. [CrossRef] [PubMed] [Google Scholar]
  • Arefnezhad, S., Samiee, S., Eichberger, A., Frühwirth, M., Kaufmann, C., & Klotz, E. (2020). Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures. Expert Systems with Applications, 162, 113778. [CrossRef] [Google Scholar]
  • Kao, I. H., & Chan, C. Y. (2022). Comparison of eye and face features on drowsiness analysis. Sensors, 22(17), 6529. [CrossRef] [PubMed] [Google Scholar]
  • Wadhwa, A., & Roy, S. S. (2021). Driver drowsiness detection using heart rate and behavior methods: A study. Data Analytics in Biomedical Engineering and Healthcare, 163–177. [CrossRef] [Google Scholar]
  • Tuncer, T., Dogan, S., Ertam, F., & Subasi, A. (2021). A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cognitive neurodynamics, 15, 223–237. [CrossRef] [PubMed] [Google Scholar]
  • Geoffroy, G., Chaari, L., Tourneret, J. Y., & Wendt, H. (2021, August). Drowsiness detection using joint EEG-ECG data with deep learning. In 2021 29th European Signal Processing Conference (EUSIPCO) (pp. 955–959). IEEE. [CrossRef] [Google Scholar]
  • El-Nabi, S. A., El-Shafai, W., El-Rabaie, E. S. M., Ramadan, K. F., Abd El-Samie, F. E., & Mohsen, S. (2023). Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review. Multimedia Tools and Applications, 1–37. [Google Scholar]
  • Zhan, Z. H., Li, J. Y., & Zhang, J. (2022). Evolutionary deep learning: A survey. Neurocomputing, 483, 42–58. [CrossRef] [Google Scholar]
  • Chakladar, D. D., Dey, S., Roy, P. P., & Dogra, D. P. (2020). EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomedical Signal Processing and Control, 60, 101989 [CrossRef] [Google Scholar]
  • Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2217–2226 [CrossRef] [Google Scholar]
  • Vu, T. H., Dang, A., & Wang, J. C. (2019). A deep neural network for real-time driver drowsiness detection. IEICE TRANSACTIONS on Information and Systems, 102(12), 2637–2641 [CrossRef] [Google Scholar]
  • Magán, E., Sesmero, M. P., Alonso-Weber, J. M., & Sanchis, A. (2022). Driver drowsiness detection by applying deep learning techniques to sequences of images. Applied Sciences, 12(3), 1145. [CrossRef] [Google Scholar]
  • Salman, R. M., Rashid, M., Roy, R., Ahsan, M. M., & Siddique, Z. (2021). Driver drowsiness detection using ensemble convolutional neural networks on YawDD. arXiv preprint arXiv:2112.10298. [Google Scholar]
  • Dua, M., Shakshi, Singla, R., Raj, S., & Jangra, A. (2021). Deep CNN models-based ensemble approach to driver drowsiness detection. Neural Computing and Applications, 33, 3155–3168. [CrossRef] [Google Scholar]
  • Valeriano, L. C., Napoletano, P., & Schettini, R. (2018, September). Recognition of driver distractions using deep learning. In 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin) (pp. 1–6). IEEE. [Google Scholar]
  • Jeon, Y., Kim, B., & Baek, Y. (2021). Ensemble CNN to detect drowsy driving with in-vehicle sensor data. Sensors, 21(7), 2372. [CrossRef] [PubMed] [Google Scholar]
  • Gjoreski, M., Gams, M. Ž., Luštrek, M., Genc, P., Garbas, J. U., & Hassan, T. (2020). Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals. IEEE access, 8, 70590–70603 [CrossRef] [Google Scholar]
  • Pavlidis, I., Dcosta, M., Taamneh, S., Manser, M., Ferris, T., Wunderlich, R., … & Tsiamyrtzis, P. (2016). Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific reports, 6(1), 25651 [CrossRef] [PubMed] [Google Scholar]
  • Reddy, B., Kim, Y. H., Yun, S., Seo, C., & Jang, J. (2017). Real-time driver drowsiness detection for embedded system using model compression of deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 121–128). [Google Scholar]
  • Dreiβig, M., Baccour, M. H., Schäck, T., & Kasneci, E. (2020, December). Driver drowsiness classification based on eye blink and head movement features using the k-NN algorithm. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 889–896). IEEE. [CrossRef] [Google Scholar]
  • Savaş, B. K., & Becerikli, Y. (2020). Real time driver fatigue detection system based on multi-task ConNN. Ieee Access, 8, 12491–12498 [CrossRef] [Google Scholar]
  • Alajlan, N. N., & Ibrahim, D. M. (2023). DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning. Sensors, 23(12), 5696. [CrossRef] [PubMed] [Google Scholar]
  • Alharbey, R., Dessouky, M. M., Sedik, A., Siam, A. I., & Elaskily, M. A. (2022). Fatigue state detection for tired persons in presence of driving periods. IEEE Access, 10, 79403–79418. [CrossRef] [Google Scholar]
  • Florez, R., Palomino-Quispe, F., Coaquira-Castillo, R. J., Herrera-Levano, J. C., Paixão, T., & Alvarez, A. B. (2023). A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification. Applied Sciences, 13(13), 7849. [CrossRef] [Google Scholar]
  • Gomaa, M. W., Mahmoud, R. O., & Sarhan, A. M. (2022). A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction. Journal of Engineering Research, 6(3), 59–70. [Google Scholar]
  • Belakhdar, I., Kaaniche, W., Djmel, R., & Ouni, B. (2016, March). A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel. In 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 443–446). IEEE. [CrossRef] [Google Scholar]
  • Anber, S., Alsaggaf, W., & Shalash, W. (2022). A hybrid driver fatigue and distraction detection model using AlexNet based on facial features. Electronics, 11(2), 285. [CrossRef] [Google Scholar]
  • Albadawi, Y., AlRedhaei, A., & Takruri, M. (2023). Real-time machine learning-based driver drowsiness detection using visual features. Journal of imaging, 9(5), 91. [CrossRef] [PubMed] [Google Scholar]
  • Young, S. R., Rose, D. C., Karnowski, T. P., Lim, S. H., & Patton, R. M. (2015, November). Optimizing deep learning hyper-parameters through an evolutionary algorithm. In Proceedings of the workshop on machine learning in high- performance computing environments (pp. 1–5). [Google Scholar]
  • Taran, S., & Bajaj, V. (2018). Drowsiness detection using adaptive hermite decomposition and extreme learning machine for electroencephalogram signals. IEEE sensors Journal, 18(21), 8855–8862. [CrossRef] [Google Scholar]
  • Kumar, A., Sangwan, K. S., & Dhiraj. (2021). A computer vision based approach fordriver distraction recognition using deep learning and genetic algorithm based ensemble. In Artificial Intelligence and Soft Computing: 20th International Conference, ICAISC 2021, Virtual Event, June 21-23, 2021, Proceedings, Part II20 (pp. 44–56). Springer International Publishing. [Google Scholar]
  • Chui, K. T., Lytras, M. D., & Liu, R. W. (2020). A generic design of driver drowsiness and stress recognition using MOGA optimized deep MKL-SVM. Sensors, 20(5), 1474. [CrossRef] [PubMed] [Google Scholar]
  • Wang, H., Zhang, L., & Yao, L. (2021). Application of genetic algorithm based support vector machine in selection of new EEG rhythms for drowsiness detection. Expert Systems with Applications, 171, 114634. [CrossRef] [Google Scholar]
  • Sarabi, S., Asadnejad, M., & Rajabi, S. (2020). Using neural network for drowsiness detection based on EEG signals and optimization in the selection of its features using genetic algorithm. Innovaciencia, 8(1), 1–9. [CrossRef] [Google Scholar]
  • Chen, L., Zhi, X., Wang, H., Wang, G., Zhou, Z., Yazdani, A., & Zheng, X. (2020). Driver fatigue detection via differential evolution extreme learning machine technique. Electronics, 9(11), 1850. [CrossRef] [Google Scholar]
  • Turner, S., Jassin, S. S., & Hassan, A. K. A. (2022). Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness. Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE), 22(3), 128–136. [Google Scholar]
  • Wang, X., Chen, L., Zhang, Y., Shi, H., Wang, G., Wang, Q., … & Zhong, F. (2022). A real-time driver fatigue identification method based on GA-GRNN. Frontiers in public health, 10, 991350. [CrossRef] [PubMed] [Google Scholar]
  • Ma, Y., Zhang, S., Qi, D., Luo, Z., Li, R., Potter, T., & Zhang, Y. (2020). Driving drowsiness detection with EEG using a modified hierarchical extreme learning machine algorithm with particle swarm optimization: A pilot study. Electronics, 9(5), 775. [CrossRef] [Google Scholar]
  • Al-Libawy, H., Al-Ataby, A., Al-Nuaimy, W., & Al-Taee, M. A. (2018). Modular design of fatigue detection in naturalistic driving environments. Accident Analysis & Prevention, 120, 188–194. [CrossRef] [Google Scholar]
  • Jasim, S. S., Abdul Hassan, A. K., & Turner, S. (2022). Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition. Aro-The Scientific Journal of Koya University, 10(2), 142–151. [CrossRef] [Google Scholar]
  • Chui, K. T., Tsang, K. F., Chi, H. R., Ling, B. W. K., & Wu, C. K. (2016). An accurate ECG-based transportation safety drowsiness detection scheme. IEEE Transactions on Industrial Informatics, 12(4), 1438–1452. [CrossRef] [Google Scholar]
  • Vijaypriya, V., & Uma, M. (2023). Facial Feature-Based Drowsiness Detection With Multi-Scale Convolutional Neural Network. IEEE Access. [Google Scholar]
  • Arefnezhad, S., Samiee, S., Eichberger, A., & Nahvi, A. (2019). Driver drowsiness detection based on steering wheel data applying adaptive neuro-fuzzy feature selection. Sensors, 19(4), 943. [CrossRef] [PubMed] [Google Scholar]

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