论文标题
我们可以比农民早检测乳腺炎吗？
Can We Detect Mastitis earlier than Farmers?
论文作者
论文摘要
这项研究的目的是建立一个建模框架，该框架将使我们能够通过引入机器学习技术来检测乳腺炎感染。在此制作中，我们创建了两个不同的建模框架，该框架的前提是在一种体细胞计数中检测到次临床乳腺炎感染的前提，事先记录了一个名为SMA，另一个试图检测两种临床乳腺炎感染和临床乳腺炎感染一样，随时牛奶被称为AMA。我们还介绍了我们的研究两个不同特征集的想法，这些特征代表了不同的特征，应在检测感染时考虑到这些特征，这些特征是牛与农场平均值不同以及泌乳趋势的想法。我们报告说，SMA的结果比AMA为亚临床感染创建的结果更好，但由于我们如何将亚临床感染记录为躯体细胞计数测量值时，它仅能对亚临床感染进行分类，因此仅能对下临床感染进行分类，这是一个很大的缺点，因为在某个地方，在某个地方都可能出现在乳液的任何阶段。因此，在某些情况下，AMA的准确性值实际上可能对农民更有益。
The aim of this study was to build a modelling framework that would allow us to be able to detect mastitis infections before they would normally be found by farmers through the introduction of machine learning techniques. In the making of this we created two different modelling framework's, one that works on the premise of detecting Sub Clinical mastitis infections at one Somatic Cell Count recording in advance called SMA and the other tries to detect both Sub Clinical mastitis infections aswell as Clinical mastitis infections at any time the cow is milked called AMA. We also introduce the idea of two different feature sets for our study, these represent different characteristics that should be taken into account when detecting infections, these were the idea of a cow differing to a farm mean and also trends in the lactation. We reported that the results for SMA are better than those created by AMA for Sub Clinical infections yet it has the significant disadvantage of only being able to classify Sub Clinical infections due to how we recorded Sub Clinical infections as being any time a Somatic Cell Count measurement went above a certain threshold where as CM could appear at any stage of lactation. Thus in some cases the lower accuracy values for AMA might in fact be more beneficial to farmers.
