Open Access
| Issue |
ITM Web Conf.
Volume 63, 2024
1st International Conference on Advances in Machine Intelligence, and Cybersecurity Technologies (AMICT2023)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/itmconf/20246301003 | |
| Published online | 13 February 2024 | |
- E. P. Chassignet, X. Xu, and O. Zavala-Romero, “Tracking marine litter with a global ocean model: Where does it go? where does it come from?” Frontiers in Marine Science, vol. 8, (2021). [CrossRef] [Google Scholar]
- I. E. Napper and R. C. Thompson, “Plastic debris in the Marine Environment: History and future challenges,” Global Challenges, vol. 4, no. 6, p. 1900081, (2020). [CrossRef] [Google Scholar]
- P. G. Ryan, C. J. Moore, J. A. van Franeker, and C. L. Moloney, “Monitoring the abundance of plastic debris in the marine environment,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, no. 1526, pp. 1999–2012, (2009). [CrossRef] [Google Scholar]
- A. L. Lusher and S. Primpke, “Finding the balance between research and monitoring: When are methods good enough to understand plastic pollution?” Environmental Science & Technology, vol. 57, no. 15, pp. 6033–6039, (2023). [CrossRef] [Google Scholar]
- A. L. Lusher and S. Primpke, “Finding the balance between research and monitoring: When are methods good enough to understand plastic pollution?” Environmental Science & Technology, vol. 57, no. 15, pp. 6033–6039, (2023). [CrossRef] [Google Scholar]
- G. G. N. Thushari and J. D. M. Senevirathna, “Plastic pollution in the marine environment,” Heliyon, vol. 6, no. 8, Aug. (2020). [Google Scholar]
- I. Marin, S. Mladenović, S. Gotovac, and G. Zaharija, “Deep-feature-based approach to marine debris classification,” Applied Sciences, vol. 11, no. 12, p. 5644, (2021). [CrossRef] [Google Scholar]
- R. Tramoy et al., “Assessment of the plastic inputs from the seine basin to the sea using statistical and field approaches,” Frontiers in Marine Science, vol. 6, (2019). [CrossRef] [Google Scholar]
- B. M, D. S, G. R, and K. KG, “Marine plastic detection using Deep Learning,” Advances in Parallel Computing Algorithms, Tools and Paradigms, Nov. (2022). [Google Scholar]
- T. van Emmerik et al., “A methodology to characterize riverine macroplastic emission into the Ocean,” Frontiers in Marine Science, vol. 5, (2018). [CrossRef] [Google Scholar]
- H. Booth, W. Ma, and O. Karakuş, “High-precision density mapping of marine debris and floating plastics via satellite imagery,” Scientific Reports, vol. 13, no. 1, (2023). [CrossRef] [Google Scholar]
- K. Kylili, I. Kyriakides, A. Artusi, and C. Hadjistassou, “Identifying floating plastic marine debris using a deep learning approach,” Environmental Science and Pollution Research, vol. 26, no. 17, pp. 17091–17099, (2019). [CrossRef] [Google Scholar]
- M. Fulton, J. Hong, M. J. Islam, and J. Sattar, “Robotic detection of marine litter using deep visual detection models,” 2019 International Conference on Robotics and Automation (ICRA), Aug. (2019). [Google Scholar]
- R. de Vries, M. Egger, T. Mani, and L. Lebreton, “Quantifying floating plastic debris at sea using vessel-based optical data and Artificial Intelligence,” Remote Sensing, vol. 13, no. 17, p. 3401, (2021). [CrossRef] [Google Scholar]
- K. Sreelakshmi, S. Akarsh, R. Vinayakumar, and K. P. Soman, “Capsule Neural Networks and visualization for segregation of plastic and non-plastic wastes,” 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Jun. (2019). [Google Scholar]
- P. Wang et al., “Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants,” Science of The Total Environment, vol. 693, p. 133440, Nov. (2019). [CrossRef] [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.