Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Precious M. Radingoana"'
Autor:
Ronald Machaka, Precious M. Radingoana
Publikováno v:
Data in Brief, Vol 51, Iss , Pp 109654- (2023)
This article refers to data derived from a research article entitled “Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning” [1]. It is based on the knowledge that alloying Ti-Ni-based shape memory alloys (SM
Externí odkaz:
https://doaj.org/article/6044cb48be2f4c31b085d60456f102b7
Autor:
Ronald Machaka, Glenda T. Motsi, Lerato M. Raganya, Precious M. Radingoana, Silethelwe Chikosha
Publikováno v:
Data in Brief, Vol 38, Iss , Pp 107346- (2021)
A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that resea
Externí odkaz:
https://doaj.org/article/b6eee4bfc2dd43d99316a1d861fa54b8
Autor:
Ronald Machaka, Precious M. Radingoana
Publikováno v:
Materials Today Communications. 35:105806
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aeeb37efa2c922d36082d1ea83bb69ba
https://doi.org/10.1016/j.mtcomm.2023.105806
https://doi.org/10.1016/j.mtcomm.2023.105806
Publikováno v:
Data in Brief
Data in Brief, Vol 38, Iss, Pp 107346-(2021)
Data in Brief, Vol 38, Iss, Pp 107346-(2021)
A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that resea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::954adb211e80b900fcd5cbd945917751
https://doi.org/10.1016/j.dib.2021.107346
https://doi.org/10.1016/j.dib.2021.107346