Open Access
| Issue |
SHS Web Conf.
Volume 129, 2021
The 21st International Scientific Conference Globalization and its Socio-Economic Consequences 2021
|
|
|---|---|---|
| Article Number | 02015 | |
| Number of page(s) | 8 | |
| Section | Behavioral Economics and Decision-Making | |
| DOI | https://doi.org/10.1051/shsconf/202112902015 | |
| Published online | 16 December 2021 | |
- Atiroglu, A., Atiroglu, A., Ozsoy, M., Atiroglu, V. & Ozacar, M. (2022). COVID-19 in Adults and Children, Symptoms and Treatment. Biointerface Research in Applied Chemistry. 12(2), 1735–1748. https://doi.org/10.33263/BRIAC122.17351748. [Google Scholar]
- Carletti, V., Greco, A., Percannella, G., & Vento, M. (2020). Age from faces in the deep-learning revolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2113–2132. [CrossRef] [Google Scholar]
- Delac, K. & Grgic M. (2004). A survey of biometric recognition methods. Proceedings. Elmar-2004. 46th International Symposium on Electronics in Marine, 184–193. [Google Scholar]
- Flir. (2021, 24. July). Pro-Grade Thermal Camera for Smartphones FLIR ONE Pro. https://www.flir.com/products/flir-one-pro/ [Google Scholar]
- Hadi, A. G., Kadhom, M., Hairunisa, N., Yousif, E. & Mohammed, S. A., (2020). A Review on COVID-19: Origin, Spread, Symptoms, Treatment, and Prevention. Biointerface Research in Applied Chemistry, 10(6) 7234–7242. https://doi.org/10.33263/briac106.7234.7242 [CrossRef] [Google Scholar]
- Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51–59. https://doi.org/10.1038/nature12047. [CrossRef] [Google Scholar]
- Jain, A. K., Ross, A. & Prabhakar, S. (2004). An introduction to biometric recognition, IEEE Transactions on Circuits and Systems for Video Technology, 14 (1), 4-20, Jan. https://doi.org/10.1109/TCSVT.2003.818349. [CrossRef] [Google Scholar]
- Krizhevsky, A., Sutskever I. & Hinton G.E. (2017). Imagenet classification with deep convolutional neural networks. Commun. ACM, 60 (6) 84–90, https://doi.org/10.1145/3065386 [Google Scholar]
- Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. 25, 1097–1105. [Google Scholar]
- Malik, M., Iqbal, M.W., Shahzad, S. K., Mushtaq, M. T., Naqvi, M. R., Kamran M., Khan B. A. & Tahir M. U. (2022). Determination of COVID-19 Patients Using Machine Learning Algorithms. Intelligent Automation & Soft Computing, 31(1), 207–222. https://doi.org/10.32604/iasc.2022.018753. [CrossRef] [Google Scholar]
- Mei, X., Zhang, Y., Zhu, H., Ling, Y., Zou, Y., Zhang, Z., et al. (2020). Observations about symptomatic and asymptomatic infections of 494 patients with COVID-19 in shanghai, china. American Journal of Infection Control, 48(9), 1045–1050. [CrossRef] [Google Scholar]
- Prihodova, K. & Hub, M. (2020). Hand-based biometric recognition technique – survey. Advances in Science, Technology and Engineering Systems. 5(6), 689 – 698. https://doi.org/10.25046/aj050683 [CrossRef] [Google Scholar]
- Prihodova, K. & Jech, J. (2019). Low-Cost System for Gender Recognition Using Convolutional Neural Network. 34th International-Business-Information-Management-Association (IBIMA) Conference Madrid, Spain. 6316–6322. [Google Scholar]
- Wayman, J. A Definition of “Biometrics”. Collected works 1997-2000. U.S. National Biometric Test Center. San José State University. August, 2003. 20–23. [Google Scholar]
- Zhumazhanova, S. S. (2018). The Problem of Biometric Identification of a Subject and Subject's Changed State: Perspectives of New Features Application in Analysis of Face and Neck Thermograms, 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), 1-4, https://doi.org/10.1109/EDM.2018.8434960. [Google Scholar]
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