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Myungsung Koo

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Articles

A Policy-Machine Learning Hybrid Approach to Evaluate Trap Mesh Selectivity: A Case Study on Pseudopleuronectes yokohamae

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By Bycatch rate, Myungsung Koo, Inyeong Kwon
| brnw.ch
4. Discussion This study used a machine learning-based utility model to estimate policy–utility (ΔU) by integrating productivity (α), the immature fish proportion (β), and the bycatch rate (γ). The validation results showed strong stability (r = 0.901; RMSE = 0.22 on independent 2015–2016 data), demonstrating that the proposed model can reliably capture biological selectivity and the economic and ecological trade-offs that occur in the fishing industry.
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A Machine Learning Technique for Deriving the Optimal Mesh Size of a Gizzard Shad (Konosirus punctatus) Gillnet

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By Myungsung Koo, Inyeong Kwon
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Evaluation of Post-Surgical Recovery in Olive Flounder (Paralichthys olivaceus) by Assessing Behavior, Heart Rate, and Wound Healing

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By Man-Ki Jeong, Myungsung Koo, Inyeong Kwon
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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