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    An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

    Published 2025-06-01
    “…Model performance was assessed using the coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) and RMSE, with XGBoost achieving the highest accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mspace width="3.33333pt"></mspace><mo>=</mo><mspace width="3.33333pt"></mspace><mn>0.9490</mn></mrow></semantics></math></inline-formula>, RMSE 888) and LightGBM being close behind (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mspace width="3.33333pt"></mspace><mo>=</mo><mspace width="3.33333pt"></mspace><mn>0.9474</mn></mrow></semantics></math></inline-formula>, RMSE 902), with both substantially exceeding the industry baseline model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mspace width="3.33333pt"></mspace><mo>=</mo><mspace width="3.33333pt"></mspace><mn>0.9028</mn></mrow></semantics></math></inline-formula>, RMSE 1500). …”
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