Performance evaluation of machine learning algorithms for the prediction of particle Froude number (F<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si207.svg" display="inline" id="d1e3713"><mml:msub><mml:mrow/><mml:mrow><mml:mi>r</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:math>) using hyper-parameter optimizations techniques-Reference-Cited by-同舟云学术

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Performance evaluation of machine learning algorithms for the prediction of particle Froude number (Frn) using hyper-parameter optimizations techniques

Published:2024-12 Issue: Volume:256 Page:124960
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Shakya Deepti^ORCID,Deshpande Vishal^ORCID,Safari Mir Jafar Sadegh^ORCID,Agarwal Mayank^ORCID

Publisher

Elsevier BV

Reference41 articles.

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2. ANFIS-based approach for predicting sediment transport in clean sewer;Azamathulla;Applied Soft Computing,2012

3. Pattern recognition and machine learning;Bishop,2006

4. Random forests;Breiman;Machine Learning,2001

5. Application of soft computing techniques for particle Froude number estimation in sewer pipes;Danandeh Mehr;Journal of Pipeline Systems Engineering and Practice,2020

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