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learning","volume":"44","author":"Du","year":"2025","journal-title":"J Shenyang Ligong Univ"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.cirpj.2021.04.007","article-title":"Estimating surface roughness for different EDM processing parameters on Inconel 718 using GEP and ANN","volume":"33","author":"Varol Ozkavak","year":"2021","journal-title":"CIRP J Manuf Sci Technol"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TNNLS.2020.3009776","article-title":"GBDT-MO: gradient-boosted decision trees for multiple outputs","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"WIREs Data Min Knowl 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Research on hot deformation behavior and microstructure-properties control of GH4151 alloy [dissertation]. Beijing, China: University of Science and Technology Beijing; 2021."},{"key":"ref32","first-page":"219","article-title":"High temperature creep and fracture behavior of a directionally solidified Ni-base superalloy","volume":"14","author":"Yuan","year":"1998","journal-title":"J Mater Sci Technol"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1016\/j.actamat.2004.04.028","volume":"52","author":"Murakumo","year":"2004","journal-title":"Acta Mater"},{"key":"ref34","first-page":"17","author":"Reed","year":"2006","journal-title":"The superalloys: fundamentals and applications"},{"key":"ref35","unstructured":"Ni C. First-principles study on the effect of alloying elements on the rafting behavior of \u03b3\u2032 phase in nickel-based single crystal superalloys [dissertation]. 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