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- W3209669657 abstract "Seafood being one of the major source of omega-3 fatty acid, is widely consumed around the world, thus at the same time affects baby-growth due to high mercury. Therefore, an automatic and intelligent seafood classification system is demanded greatly. This study proposed a shallow but effective deep learning based computationally intelligent system that can classify nine different fish species. The model is trained with publicly available data set, called A Large Scale Fish Dataset. 35% of data is used for testing while rest is reserved for training and validation. Experimental results shows that proposed model achieved an overall accuracy of 94%, thus surpasses many previously proposed models. Detail analysis shows that the model secured better F1-score (98%) on Red Mullet fish." @default.
- W3209669657 created "2021-11-08" @default.
- W3209669657 creator A5073181310 @default.
- W3209669657 date "2021-09-01" @default.
- W3209669657 modified "2023-09-26" @default.
- W3209669657 title "A Sustainable Deep Learning based Computationally Intelligent Seafood Monitoring System for Fish Species Screening" @default.
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- W3209669657 doi "https://doi.org/10.1109/icaiot53762.2021.00008" @default.
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