EfficientNet Model for Multiclass Classification of The Correctness of Wearing Face Mask
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References
World Health Organization (WHO), “Mask use in the context of COVID-19,” Dec. 2020. Accessed: Aug. 12, 2023. [Online]. Available: WHO/2019-nCoV/IPC_Masks/2020.5
World Health Organization (WHO), “WHO Director-General’s opening remarks at the media briefing on COVID-19,” 11 March 2020, Accessed: Aug. 09, 2023. [Online]. Available: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
World Health Organization, “WHO updates COVID-19 guidelines on masks, treatments and patient care,” https://www.who.int/news/item/13-01-2023-who-updates-covid-19-guidelines-on-masks--treatments-and-patient-care.
C. Matuschek et al., “Face masks: Benefits and risks during the COVID-19 crisis,” Aug. 12, 2020, BioMed Central Ltd. doi: 10.1186/s40001-020-00430-5.
Y. Goh, B. Y. Q. Tan, C. Bhartendu, J. J. Y. Ong, and V. K. Sharma, “The face mask: How a real protection becomes a psychological symbol during Covid-19?,” Aug. 01, 2020, Academic Press Inc. doi: 10.1016/j.bbi.2020.05.060.
Y. Lecun, K. Kavukcuoglu, and C. Farabet, “Convolutional Networks and Applications in Vision.” [Online]. Available: http://www.cs.nyu.edu/
S. Sagar and J. Singh, “An experimental study of tomato viral leaf diseases detection using machine learning classification techniques,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 1, pp. 451–461, Feb. 2023, doi: 10.11591/eei.v12i1.4385.
X. Kong et al., “Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework,” IEEE Internet Things J, vol. 8, no. 21, pp. 15929–15938, Nov. 2021, doi: 10.1109/JIOT.2021.3051844.
S. A. Sanjaya and S. A. Rakhmawan, “Face Mask Detection Using MobileNetV2 in The Era of COVID-19 Pandemic,” in International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer: IEEE, Oct. 2020.
M. Umer et al., “Face mask detection using deep convolutional neural network and multi-stage image processing,” Image Vis Comput, vol. 133, May 2023, doi: 10.1016/j.imavis.2023.104657.
C. Thai, V. Tran, M. Bui, D. Nguyen, H. Ninh, and H. Tran, “Real-time masked face classification and head pose estimation for RGB facial image via knowledge distillation,” Inf Sci (N Y), vol. 616, pp. 330–347, Nov. 2022, doi: 10.1016/j.ins.2022.10.074.
C. Z. Basha, B. N. L. Pravallika, and E. B. Shankar, “An efficient face mask detector with pytorch and deep learning,” EAI Endorsed Trans Pervasive Health Technol, vol. 7, no. 25, pp. 1–8, 2021, doi: 10.4108/eai.8-1-2021.167843.
X. Ying, “An Overview of Overfitting and its Solutions,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Mar. 2019. doi: 10.1088/1742-6596/1168/2/022022.
Y. Sun, A. K. C. Wong, and M. S. Kamel, “Classification of imbalanced data: A review,” Intern J Pattern Recognit Artif Intell, vol. 23, no. 4, pp. 687–719, Jun. 2009, doi: 10.1142/S0218001409007326.
M. Koklu, I. Cinar, and Y. S. Taspinar, “CNN-based bi-directional and directional long-short term memory network for determination of face mask,” Biomed Signal Process Control, vol. 71, Jan. 2022, doi: 10.1016/j.bspc.2021.103216.
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in International Conference on Machine Learning, Long Beach, USA, Jun. 2019, pp. 6105–6114. Accessed: May 14, 2024. [Online]. Available: https://proceedings.mlr.press/v97/tan19a.html
Khadijah, R. Kusumaningrum, Rismiyati, and A. Mujadidurrahman, “An Efficient Masked Face Classifier Using EfficientNet,” in 5th International Conference on Informatics and Computational Sciences (ICICoS), Semarang: IEEE, Nov. 2021.
Z. Huang, L. Su, J. Wu, and Y. Chen, “Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism,” Applied Sciences (Switzerland), vol. 13, no. 5, Mar. 2023, doi: 10.3390/app13053180.
F. Zulfiqar, U. Ijaz Bajwa, and Y. Mehmood, “Multi-class classification of brain tumor types from MR images using EfficientNets,” Biomed Signal Process Control, vol. 84, Jul. 2023, doi: 10.1016/j.bspc.2023.104777.
A. Rafay and W. Hussain, “EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases,” Biomed Signal Process Control, vol. 85, Aug. 2023, doi: 10.1016/j.bspc.2023.104869.
Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol Inform, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
H. F. D. Ul Haq et al., “EfficientNet Optimization on Heartbeats Sound Classification,” in Proceedings - International Conference on Informatics and Computational Sciences, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 216–221. doi: 10.1109/ICICoS53627.2021.9651818.
A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Health, vol. 19, Mar. 2021, doi: 10.1016/j.smhl.2020.100144.
M. Tan and Q. V. Le, “Rethinking model scaling for convolutional neural networks,” in Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA, May 2019, pp. 6105–6114. doi: https://doi.org/10.48550/arXiv.1905.11946.
J. Krohn, G. Beyleveld, and A. Bassens, Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series) 1st Edition, 1st ed. Boston: Pearson Addison-Wesley, 2020.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.04381
J. Han and M. Kamber, Data Mining: Concepts and Techniques Second Edition. San Farnsisco: Elsevier Inc., 2006.
A. Howard et al., “Searching for MobileNetV3,” May 2019, Accessed: Oct. 26, 2023. [Online]. Available: https://arxiv.org/abs/1905.02244
C. Szegedy, V. Vanhoucke, S. Ioffe, and J. Shlens, “Rethinking the Inception Architecture for Computer Vision.” Accessed: Oct. 23, 2023. [Online]. Available: https://arxiv.org/abs/1512.00567
H. D. Jahja, N. Yudistira, and Sutrisno, “Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation,” Mar. 2022, [Online]. Available: http://arxiv.org/abs/2203.11542
N. Azouji, A. Sami, and M. Taheri, “EfficientMask-Net for face authentication in the era of COVID-19 pandemic,” Signal Image Video Process, vol. 16, no. 7, pp. 1991–1999, Oct. 2022, doi: 10.1007/s11760-022-02160-z.
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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