首页|Data on Machine Learning Discussed by Researchers at University of Thessaly (Det ecting Hull Fouling using Machine Learning Algorithms trained on Ship Propulsion Data to Improve Resource Management and Increase Environmental Benefits)

Data on Machine Learning Discussed by Researchers at University of Thessaly (Det ecting Hull Fouling using Machine Learning Algorithms trained on Ship Propulsion Data to Improve Resource Management and Increase Environmental Benefits)

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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."

University of ThessalyVolosGreeceE uropeAlgorithmsCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.18)