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- W4385712813 abstract "Abstract: The expanding aquaculture sector can greatly benefit from having access to comprehensive fish information, which includes data on ecosystems, food requirements, and related details. Relational databases play a crucial role in storing the diverse range of fish species data in the aquaculture industry. However, due to the extensive variety of species and their associated data, organizing and categorizing this information can be a challenging task. Nonetheless, fishermen can derive significant advantages from visual representations of the data and a recommendation system that suggests the most suitable fish species and optimal ecological conditions. By leveraging data on fish species, names, taxonomy, and survival characteristics, a connected user can receive personalized recommendations based on their interests. The proposed system also offers appropriate food variety suggestions for the fish, ensuring scalability and effective generation of recommendations. Through the integration of information from multiple sources, a database system can efficiently process, manage, store, and retrieve relational data. Furthermore, implementing the linear discriminant algorithm enhances the accuracy and speed of food recommendations for fish." @default.
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- W4385712813 date "2023-08-31" @default.
- W4385712813 modified "2023-09-26" @default.
- W4385712813 title "Substantiation of Sequential Trait with Independent Correlation" @default.
- W4385712813 doi "https://doi.org/10.22214/ijraset.2023.55010" @default.
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