Open Access
| Issue |
E3S Web of Conf.
Volume 507, 2024
International Conference on Futuristic Trends in Engineering, Science & Technology (ICFTEST-2024)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/e3sconf/202450701010 | |
| Published online | 29 March 2024 | |
- M. Jahanbakht, W. Xiang, L. Hanzo, and M. RahimiAzghadi, IEEE Communications Surveys & tutorials, vol. 23, No. 2, second quarter 2021(2021) [Google Scholar]
- M. Valdenegro-Toro,Object Recognition in Forward-Looking Sonar Images with Convolutional Neural Networks, 978-1-5090-1537-5/16, IEEE(2016) [Google Scholar]
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, IEEE Transactions on “Pattern Analysis and Machine Intelligence” (2016) [Google Scholar]
- J. Redmon, Santosh Divvala, Ross Girshick, A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection IEEE” (2016) [Google Scholar]
- Mohammad yaseliani, Ali zenial hamadani, Abtinijadimaghsoodi, and Amir mosavi, IEEE Access, volume 10 (2022) [Google Scholar]
- Md. Farukh Hashmi, SatyarthKatiyar, AbdulWahab Hashmi & Avinash G. keskar, “Pneumonia detection in chest X-ray images using compound scaled deep learning model”, Journal for control, measurement, electronics, computing and communications, volume 62, 2021(2021) [Google Scholar]
- MehediMasud, Anupam Kumar Bairagi, Abdullah-Al Nahid, NiloySikder, Saeed Rubaiee, Anas Ahmed, and DivyaAnand, “A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm” Volume 2021, Article ID 8862089 (2021) [Google Scholar]
- Widodo, Chomsin S., Naba, Agus, Mahasin, Muhammad M., Yueniwati, Yuyun, Putranto, TerawanA., Patra, Pangeran, UBNet: “Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients, Journal of XRay Science and Technology”, vol. 30, No. 1, pp. 57-71(2022) [Google Scholar]
- U. Masud, T. Saeed, H. M. Malaikah, F. U. Islam and G. Abbas, “Smart Assistive System for Visually Impaired People Obstruction Avoidance Through Object Detection and Classification,” in IEEE Access, vol. 10, pp. 13428–13441(2022) [CrossRef] [Google Scholar]
- M.vadivel, S.Vimal, V. G. Sivakumar, V. V. Baskar and M. Selvi, International Conference on Innovative Data Communication Technologies and Application (ICIDCA), Uttarakhand, India, 2023, pp. 1055–1059 (2023) [Google Scholar]
- A. U. Kulkarni, A. M. Potdar, S .Hegde and V. P. Baligar, 1st International Conference on Advances in Information Technology (ICAIT), Chikmagalur, India, 2019, pp. 204–209 (2019) [Google Scholar]
- H. Thanh Le, S. L. Phung, P. B. Chapple, A. Bouzerdoum, C. H. Ritz and L. C. Tran, “Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery,” in IEEE Access, vol. 8, pp. 94126–94139 (2020) [Google Scholar]
- G. Huo, Z. Wu and J. Li, “Underwater Object Classification in Sidescan Sonar Images Using Deep Transfer Learning and Semisynthetic Training Data,” in IEEE Access, vol. 8, pp. 47407–47418 (2020) [CrossRef] [Google Scholar]
- A. R. Mishra, S. K. Pippal, Asif, A. Kumar, D. Singh and A. Singh, 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2021, pp. 1–5 (2021) [Google Scholar]
- W. Wei, F. Zang, L. Huang and W. Xue, International Conference on Networking and Network Applications (NaNA), Qingdao, China, 2023, pp. 539–544 (2023) [Google Scholar]
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