| dc.contributor | 資訊系 | |
| dc.creator (作者) | 彭彥璁 | |
| dc.creator (作者) | Peng, Yan-Tsung;Lai, Yi-Ting;Lien, Wei-Cheng;Cheng, Yun-Chiao;Lin, Yi-Ting;Liao, Chen-Jie;Chiu, Yu-Shao | |
| dc.date (日期) | 2024-05 | |
| dc.date.accessioned | 12-Dec-2024 09:27:59 (UTC+8) | - |
| dc.date.available | 12-Dec-2024 09:27:59 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Dec-2024 09:27:59 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154755 | - |
| dc.description.abstract (摘要) | The aquatic market price is an essential indicator for fishermen in making decisions regarding conveyance, fish-catching, and wholesale strategies. As a guide for the aquaculture industry, understanding market prices, the geographical distribution of fish prices, and the prediction of fish prices are vital. In Taiwanese fisheries, however, predicting aquatic prices is challenging due to their drastic fluctuations caused by tropical and subtropical climate variations, export/import fish quantity changes, and political and economic situation uncertainty. Therefore, instead of using a single machine learning model to address the price fluctuations, we propose a hybrid fish price prediction model integrating multiple machine learning and deep learning models to identify and predict fish price dynamics in various wholesale markets. The extensive experimental results on actual market data from the Taiwan Council of Agriculture (COA) show that the proposed models can achieve >90% accuracy in predicting future aquatic prices. Additionally, we developed a fully automated learning and prediction system architecture on the cloud, allowing us to acquire data automatically and fine-tune the AI models continuously to achieve better performance with long-term operability. | |
| dc.format.extent | 113 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Aquaculture, Vol.586, 740741 | |
| dc.title (題名) | Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1016/j.aquaculture.2024.740741 | |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.aquaculture.2024.740741 | |
