Summary of Machine Learning in Management Of Precautionary Closures Caused by Lipophilic Biotoxins, By Andres Molares-ulloa et al.
Machine Learning in management of precautionary closures caused by lipophilic biotoxins
by Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos, Daniel Rivero
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model is developed to support the application of precautionary closures in mussel farming, addressing the risk of harmful algal blooms (HABs). The model leverages the k-Nearest Neighbors (kNN) algorithm and achieves high sensitivity, accuracy, and kappa index values. This predictive model can aid experts in making informed decisions, improving the management of mussel farming production areas and reducing the economic impacts of HABs. By formalizing expert experience and incorporating machine learning techniques, this study contributes to the development of a decision-support system for complex scenarios where forecast errors are more common. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a special computer program to help decide when it’s safe or not to open mussel farms due to algae blooms. These blooms can make mussels poisonous and affect people who eat them. The new program uses an algorithm called kNN, which is really good at making predictions. It helps experts make better decisions about whether to close the farms temporarily, and this reduces the risk of harming people or causing economic losses. |
Keywords
* Artificial intelligence * Machine learning
