EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 465 (2023) E3S Web of Conf., 465 (2023) 02031 References
Open Access
Issue
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
Article Number 02031
Number of page(s) 6
Section Symposium on Electrical, Information Technology, and Industrial Engineering
DOI https://doi.org/10.1051/e3sconf/202346502031
Published online 18 December 2023
  1. O. J. Peter, S. Kumar, N. Kumari, F. A. Oguntolu, K. Oshinubi, and R. Musa, “Transmission dynamics of Monkeypox virus: a mathematical modelling approach,” Model Earth Syst Environ, vol. 8, no. 3, pp. 3423–3434, Sep. (2022), doi: 10.1007/s40808-02101313-2. [CrossRef] [PubMed] [Google Scholar]
  2. R. A. Farahat et al., “Monkeypox and human transmission: Are we on the verge of another pandemic?,” Travel Med Infect Dis, vol. 49, Sep. (2022), doi: 10.1016/j.tmaid.2022.102387. [Google Scholar]
  3. WHO, “Multi-country monkeypox outbreak in nonendemic countries,” Monkeypox, May 19, (2022) [Google Scholar]
  4. E. Alakunle, U. Moens, G. Nchinda, and M. I. Okeke, “Monkeypox Virus in Nigeria: Infection Biology, Epidemiology, and Evolution,” Viruses, vol. 12, no. 11, p. 1257, Nov. 2020, doi: 10.3390/v12111257. [CrossRef] [PubMed] [Google Scholar]
  5. D. Onchonga, “Monkeypox viral disease outbreak in non-endemic countries in 2022: What clinicians and healthcare professionals need to know,” Saudi Pharmaceutical Journal, Sep. (2022),doi: 10.1016/j.jsps.2022.09.008. [Google Scholar]
  6. M. Altindis, E. Puca, and L. Shapo, “Diagnosis of monkeypox virus – An overview,” Travel Med Infect Dis, vol. 50, p. 102459, Nov. (2022), doi: 10.1016/j.tmaid.2022.102459. [CrossRef] [PubMed] [Google Scholar]
  7. Y. Li, V. A. Olson, T. Laue, M. T. Laker, and I. K. Damon, “Detection of monkeypox virus with realtime PCR assays,” Journal of Clinical Virology, vol. 36, no. 3, pp. 194–203, Jul. (2006), doi: 10.1016/j.jcv.2006.03.012. [CrossRef] [PubMed] [Google Scholar]
  8. L. Muñoz-Saavedra et al., “Monkeypox diagnosticaid system with skin images using convolutional neural networks,” 2022,[Online].Available:https://ssrn.com/abstract=4186534 [Google Scholar]
  9. S. N. Ali et al., “Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study,” Jul. (2022), [Online]. Available: http://arxiv.org/abs/2207.03342 [Google Scholar]
  10. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016December, pp. 770–778, Dec. (2015), doi: 10.1109/CVPR.2016.90. [Google Scholar]
  11. Mingxing Tan, “EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling,” (2019). https://ai.googleblog.com/2019/05/efficientnetimp roving-accuracy-and.html (accessed Mar. 04, 2023). [Google Scholar]
  12. M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 2019. doi: 10.1145/3305381.3305510. [Google Scholar]
  13. Y. Fu, “Image classification via fine-tuning with EfficientNet,” Jul. 16, (2020) [Google Scholar]
  14. V. Miglani and M. Bhatia, “Skin Lesion Classification: A Transfer Learning Approach Using EfficientNets,” 2021, pp. 315– 324. doi: 10.1007/978981-15-3383-9_29. [Google Scholar]
  15. M. M. Ahsan, M. R. Uddin, M. Farjana, A. N. Sakib, K. Al Momin, and S. A. Luna, “Image Data collection and implementation of deep learningbased model in detecting Monkeypox disease using modified VGG16,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2206.01862 [Google Scholar]
  16. T. Islam, M. A. Hussain, F. U. H. Chowdhury, and B. M. R. Islam, “Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?,” bioRxiv, p. 2022.08.08.503193, Jan. (2022), doi: 10.1101/2022.08.08.503193. [Google Scholar]
  17. R. H. Hridoy, F. Akter, and A. Rakshit, “Computer Vision Based Skin Disorder Recognition using EfficientNet: A Transfer Learning Approach,” in 2021 International Conference on Information Technology (ICIT), IEEE, Jul. 2021, pp. 482–487. doi: 10.1109/ICIT52682.2021.9491776. [Google Scholar]
  18. K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neuroscience Informatics, vol. 2, no. 4, p. 100034, Dec. 2022, doi:10.1016/j.neuri.2021.100034. [CrossRef] [Google Scholar]
  19. A. E. Minarno, L. R. Wandani, and Y. Azhar, “Classification of Breast Cancer Based on Histopathological Image Using EfficientNet-B0 on Convolutional Neural Network,” International Journal of Emerging Technology and Advanced Engineering, vol. 12, no. 8, pp. 70–77, Aug. 2022, doi: 10.46338/ijetae0822_09. [CrossRef] [Google Scholar]
  20. H. Gunwant, A. Joshi, M. Sharma, and D. Gupta, “Automated Medical Diagnosis and Classification of Skin Diseases Using Efficinetnet-B0 Convolutional Neural Network,” 2022, pp. 3–19. doi: 10.1007/9783-031-08266-5_1. [Google Scholar]
  21. S. Gang, N. Fabrice, D. Chung, and J. Lee, “Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning,” Sensors, vol. 21, no. 9, p. 2921, Apr. (2021), doi: 10.3390/s21092921. [CrossRef] [PubMed] [Google Scholar]
  22. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Jun. (2018), pp. 4510–4520. doi: 10.1109/CVPR.2018.00474. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.