과제정보
본 연구는 전남대학교 국립대학교 육성사업비 교내 신진 학술연구비 (과제번호: 2020-2020)와 한국산업기술평가관리원 연구비 (과제번호: 2020-3414)의 지원에 의하여 진행되었다. 또한 전남대학교 4단계 BK21사업 인공지능 융합 인재 양성 사업단의 지속적인 관심과 지원에 깊은 감사를 표현한다.
참고문헌
- Ayanian, JZ. (1999). Perceived risks of heart disease and cancer among cigarette smokers, JAMA, 281(11), (pp. 1019-1-21). https://doi.org/10.1001/jama.281.11.1019
- Carosella, AM, Ossip-Klein, DJ, Watt, CA, and Podgorski, C. (2002). Smoking history, knowledge, and attitudes among older residents of a long-term care facility, Nicotine & Tobacco Research, 4, (pp. 161-169). https://doi.org/10.1080/14622200210123987
- Chen, WW, and Lindsey, R. (2001). Evaluation of a tobacco prevention program on knowledge, attitudes, intention and behavior of tobacco use among fourth grade students - a preliminary study, J Drug Educ, 31(4), (pp. 399-410). https://doi.org/10.2190/LN45-RRD7-B4EV-5FCN
- Frank, E, Denniston, M, and Pederson, L. (2002). Declines in smokers' understanding of tobacco's hazards between 1986 and 1998: a report from north Georgia, South Med J , 95(7), (pp. 675-680). https://doi.org/10.1097/00007611-200207000-00005
- Geckova, A, Van Dijk, JP, van lttersum-Gritter, T, and Groothoff, JW. (2002). Determinants of adolescents' smoking behaviour: A literature review, Cent Eur J publ Health, 10(3), (pp. 79-87).
- Hakeem, R, Thomas, J, and Badruddin, SH. (2001). Urbanisation and health related knowledge and attitudes of South Asian children, J Pak Med Assoc, 51(12), (pp. 437-443).
- He, K, Zhang, X, Ren, S, and Sun, J. (2016). Deep residual learning for image recognition, CVPR, (pp. 770-778).
- He, Y, Lin, J, Liu, Z, Wang, H, Li, LJ, and Han, S. (2018). Amc: Automl for model compression and acceleration on mobile devices, ECCV.
- Howard, AG, Zhu, M, Chen, B, Kalenichenko, D, Wang, W, Weyand, T, Andreetto, M, and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv: 1704.04861.
- Hu, J, Shen, L, and Sun, G. (2018). Squeeze-and-excitation networks, CVPR.
- Huang, Y, Cheng, Y, Chen, D, Lee, H, Ngiam, J, Le, QV, and Chen, Z. (2018). Gpipe: Efficient training of giant neural networks using pipeline parallelism, arXiv preprint arXiv: 1808.07233.
- Jeong, YS, Lee, HR, Baek, JH, Kim, KH, Chung YS, and Lee, CW. (2020). Deep Learning-based Rice Seed Segmentation for Phynotyping, Journal of the Korea Industrial Information Systems Research, 25(5), (pp. 23-29). https://doi.org/10.9723/JKSIIS.2020.25.5.023
- Kale, YS, Vibhute, N, Belgaumi, U, Kadashetti, V, Bommanavar, S, and Kamate, W. (2019). Effect of using tobacco on taste perception, J Family Med P rim Care, 8(8), (pp. 2699-2702). https://doi.org/10.4103/jfmpc.jfmpc_457_19
- Kim, SW, Cha, KA, and Park, SH. (2020). Lip-reading System based on Bayesian Classifier, Journal of the Korea Industrial Information Systems Research, 25(4), (pp. 9-16). https://doi.org/10.9723/JKSIIS.2020.25.4.009
- Kim, DH, Hwang, BW, Lee, SW, and Kwak, SY. (2020). 3D Human Shape Deformation using Deep Learning, Journal of the Korea Industrial Information Systems Research, 25(2), (pp. 19-27). https://doi.org/10.9723/JKSIIS.2020.25.2.019
- Kornblith, S, Shlens, J, and Le, QV. (2019). Do better imagenet models transfer better?, CVPR.
- Krizhevsky, A, Sutskever, I, and Hinton, GE. (2012). Imagenet classification with deep convolutional neural networks, NIPS, (pp. 1097-1105).
- Lu, Z, Pu, H, Wang, F, Hu, Z, and Wang, L. (2018). The expressive power of neural networks: A view from the width, NeurIPS.
- Pavlos, P, Vasilios, N, Antonia, A, Dimitrios, K, Georgios, K, and Georgios, A. (2009). Evaluation of young smokers and non-smokers with Electrogustometry and Contact Endoscopy, BMC Ear Nose Throat Disord, 9(9), doi: 10.1186/1472-6815-9-9.
- Sargent, JD, Beach, ML, Dalton, MA, Mott, LA, and Tickle, JJ, (2001). Ahrens, MB, Heatherton, TF. Effect of seeing tobacco use in films on trying smoking among adolescents: cross sectional study, BMJ , 323(7326), (1394-1397). https://doi.org/10.1136/bmj.323.7326.1394
- Sharir, O, and Shashua, A. (2018). On the expressive power of overlapping architectures of deep learning, ICLR.
- Suga, S, Otomo, A, Jeong, H, and Ohno, Y. (2019). Image Similarity Check of Nailfold Capillary by Template Matching, IEEE 8th Global Conference on Consumer Elecctronics (GCCE), Osaka, Japan.
- Szegedy, C, Liu, W, Jia, Y, Sermanet, P, Reed, S, Anguelov, D, Erhan, D, Vanhoucke, V, and Rabinovich, A. (2015). Going deeper with convolutions, CVPR, (pp. 1-9).
- Tan, M, and V. Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California, PMLR 97.
- Tomassini, S, Cuoghi, V, Catalani, E, Casini, G, and Bigiani, A. (2007). Long-term effects of nicotine on rat fungiform taste buds, Neuroscience, 147(3), (pp. 803-810). https://doi.org/10.1016/j.neuroscience.2007.04.053
- Torabi, MR, Yang, J, and Li, J. (2002). Comparison of tobacco use knowledge, attitude and practice among college students in China and the United states, Health promot int, 17(3), (pp. 247-254). https://doi.org/10.1093/heapro/17.3.247
- World Health Organization (WHO). (1983). Guidelines for the conduct of tobacco-smoking surveys among health professionals, Report of a WHO meeting held in Winnipeg, Canada.
- Woo, E. (2003). Study on the Knowledge Level of Smoking and Smoking Behavior, Master Thesis, The Graduate School of Yonsei Univeristy, Seoul, Korea.
- Zagoruyko, S, and Komodakis, N. (2016). Wide residual networks, BMVC.