摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于人工智能的新研究结果已经发表。根据来自希腊沃洛斯的新闻，由NewsRx Cor的受访者，研究表明，“这项研究的目的是开发一种方法来评估船体污垢，基于船舶推进数据，如速度，吃水和天气相关数据。”我们的新闻编辑从塞萨利大学的研究中获得了一句话:“船体污损是船舶中不可避免的现象,会导致更高的燃油消耗,维修频率是最佳的。尽管FA CT认为到目前为止,这项任务主要依靠经验规则。”我们的目标是用机器学习来代替经验规则，因为我们拥有的大量数据对我们的工作有很大的帮助，因此，我们的目标是用机器学习来代替经验规则，这最终变成了一个回归问题，因此，我们的目标是用机器学习来代替经验规则，因为我们拥有的大量数据对我们的工作有很大的帮助。本文对几种监督算法进行了k重交叉验证实验，最终选择了基于集合方法或人工神经网络的模型，并提出了MLP R回归器、随机森林回归器和XGB回归器的潜在用途，因为它们在一些性能指标上都取得了很好的结果。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news originating from Volos, Greece, by NewsRx cor respondents, research stated, "This study aims to develop a methodology to asses s hull fouling based on ship propulsion data such as speed, draft and weather re lated data." Our news editors obtained a quote from the research from University of Thessaly: "Hull fouling is an unavoidable phenomenon in ships and results in higher fuel consumption and the maintenance frequency has be the optimal one. Despite the fa ct that until now this task has primarily relied on empirical rules, it turns ou t that it can be improved by employing machine learning techniques. Using data f rom clean-hull ships, we aim to isolate and consider only the weather in this st udy. Our goal is to replace empirical rules with machine learning, as the vast a mount of data we possess can significantly aid us in this endeavor. It ends up t o be a regression problem, and therefore, we experiment with several supervised algorithms using k-fold cross validation to finally select models based on ensem ble methods or artificial neural networks. We propose the potential use of MLP R egressor, Random Forest Regressor and XGB Regressor since all of them yielded ve ry good results in terms of some performance metrics."
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