Mussel farming is one of the most important aquaculture industries. The main
risk to mussel farming is harmful algal blooms (HABs), which pose a risk to
human consumption. In Galicia, the Spanish main producer of cultivated mussels,
the opening and closing of the production areas is controlled by a monitoring
program. In addition to the closures resulting from the presence of toxicity
exceeding the legal threshold, in the absence of a confirmatory sampling and
the existence of risk factors, precautionary closures may be applied. These
decisions are made by experts without the support or formalisation of the
experience on which they are based. Therefore, this work proposes a predictive
model capable of supporting the application of precautionary closures.
Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and
0.75 respectively, the kNN algorithm has provided the best results. This allows
the creation of a system capable of helping in complex situations where
forecast errors are more common.

传统经验不足以支持预防性关闭措施的制定，因此本研究提出了基于 kNN 算法的预测模型，能够在复杂情况下帮助决策，实现敏感性、准确度和 kappa 指数值分别为 97.34%、91.83% 和 0.75。

机器学习在脂溶性生物毒素引起的预防性关闭管理中的应用

Machine Learning in management of precautionary closures caused by  lipophilic biotoxins

In machining processes, monitoring the condition of the tool is a crucial
aspect to ensure high productivity and quality of the product. Using different
machine learning techniques in Tool Condition Monitoring TCM enables a better
analysis of the large amount of data of different signals acquired during the
machining processes. The real time force signals encountered during the process
were acquired by performing numerous experiments. Different tool wear
conditions were considered during the experimentation. A comprehensive
statistical analysis of the data and feature selection using decision trees was
conducted, and the KNN algorithm was used to perform classification.
Hyperparameter tuning of the model was done to improve the models performance.
Much research has been done to employ machine learning approaches in tool
condition monitoring systems, however, a model agnostic approach to increase
the interpretability of the process and get an in depth understanding of how
the decision making is done is not implemented by many. This research paper
presents a KNN based white box model, which allows us to dive deep into how the
model performs the classification and how it prioritizes the different features
included. This approach helps in detecting why the tool is in a certain
condition and allows the manufacturer to make an informed decision about the
tools maintenance.

这篇研究论文提出了基于 KNN 的白盒子模型，通过深入分析模型的分类过程和特征优先级，帮助制造商了解工具的特定状态，使其能够做出知情的维护决策。

基于 k 近邻的模型不可知方法在铣削中的刀具维护中进行明智决策

Making informed decisions in cutting tool maintenance in milling: A KNN  based model agnostic approach

The existing sonar image classification methods based on deep learning are
often analyzed in Euclidean space, only considering the local image features.
For this reason, this paper presents a sonar classification method based on
improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to
multiple types imaging sonar. This method quantifies the correlation
relationship between nodes based on the joint calculation of color proximity
and spatial proximity that represent the sonar characteristics in non-Euclidean
space, then the KNN (K-Nearest Neighbor) algorithm is used to determine the
neighborhood range and adjacency matrix in the graph attention mechanism, which
are jointly considered with the attention coefficient matrix to construct the
key part of the SI-GAT. This SI-GAT is superior to several CNN (Convolutional
Neural Network) methods based on Euclidean space through validation of real
data.

本文提出了一种基于改进图形注意力网络 (SI-GAT) 的声纳分类方法，该方法可以在多种类型成像声纳中应用，并通过实际数据验证优于几种以欧几里得空间为基础的卷积神经网络方法。