id	domain	url
fusus-147	doi.org	https://doi.org/10.55670/fpll.fusus.2.1.5
fusus-147	fupubco.com	https://fupubco.com/fusus
fusus-147	doi.org	https://doi.org/10.55670/fpll.fusus.2.1.5
fusus-147	fupubco.com	https://fupubco.com/fusus
fusus-147	github.com	https://github.com/fahel-bin-noor/high-entropyalloy-phase-prediction-using-balanced-dataset
fusus-147	doi.org	https://doi.org/10.1016/j.commatsci.2018.04.003
fusus-147	doi.org	https://doi.org/10.1016/j.actamat.2016.08.081
fusus-147	doi.org	https://doi.org/10.1002/adem.200300567
fusus-147	doi.org	https://doi.org/10.1016/j.msea.2003.10.257
fusus-147	doi.org	https://doi.org/10.1016/j.jmrt.2022.01.172
fusus-147	doi.org	https://doi.org/10.1016/j.pmatsci.2013.10.001
fusus-147	doi.org	https://doi.org/10.1126/science.1254581
fusus-147	doi.org	https://doi.org/10.1063/1.5088921
fusus-147	doi.org	https://doi.org/10.1016/0025-5416(83)902124
fusus-147	doi.org	https://doi.org/10.1002/adma.201907226
fusus-147	doi.org	https://doi.org/10.1016/j.apsusc.2018.01.050
fusus-147	github.com	https://github.com/fahel-bin-noor/high-entropy-alloy-phase-prediction-using-balanced-dataset
fusus-147	github.com	https://github.com/fahel-bin-noor/high-entropy-alloy-phase-prediction-using-balanced-dataset
fusus-147	doi.org	https://doi.org/10.1038/nature17981
fusus-147	doi.org	https://doi.org/10.1016/j.msea.2017.02.077
fusus-147	doi.org	https://doi.org/10.1155/2015/647351
fusus-147	doi.org	https://doi.org/10.1016/j.corsci.2018.01.030
fusus-147	doi.org	https://doi.org/10.1016/j.cej.2021.132410
fusus-147	doi.org	https://doi.org/10.1002/sstr.202200290
fusus-147	doi.org	https://doi.org/10.48550/arxiv.2303.12301
fusus-147	doi.org	https://doi.org/10.1557/s43580023-00611-4
fusus-147	doi.org	https://doi.org/10.1063/9.0000561
fusus-147	doi.org	https://doi.org/10.1063/1.5115885
fusus-147	doi.org	https://doi.org/10.48550/arxiv.2306.01146
fusus-147	doi.org	https://doi.org/10.1016/j.commatsci.2023.112129
fusus-147	doi.org	https://doi.org/10.1016/j.matdes.2020.109260
fusus-147	doi.org	https://doi.org/10.1016/s10036326(20)65482-6
fusus-147	doi.org	https://doi.org/10.1007/s10853-023-08705-y
fusus-147	doi.org	https://doi.org/10.1115/1.4057039
fusus-147	doi.org	https://doi.org/10.1016/j.commatsci.2020.110244
fusus-147	doi.org	https://doi.org/10.1016/j.mtcomm.2020.101871
fusus-147	doi.org	https://doi.org/10.1016/j.scriptamat.2021.113804
fusus-147	doi.org	https://doi.org/10.1016/j.actamat.2019.03.012
fusus-147	doi.org	https://doi.org/10.1016/s1002-0071(12)60080-x
fusus-147	doi.org	https://doi.org/10.5281/zenodo.6403257
fusus-147	doi.org	https://doi.org/10.17632/7fhwrgfh2s.3
fusus-147	doi.org	https://doi.org/10.1016/s00223093(03)00155-8
fusus-147	doi.org	https://doi.org/10.1002/adem.200700240
fusus-147	doi.org	https://doi.org/10.1179/imtr.1984.29.1.168
fusus-147	doi.org	https://doi.org/10.1016/s0921-5093(97)00590-x
fusus-147	www.jstage.jst.go.jp	https://www.jstage.jst.go.jp/article/matertrans/46
fusus-147	doi.org	https://doi.org/10.1109/iecbes.2018.8626714
fusus-147	www.semanticscholar.org	https://www.semanticscholar.org/paper/rectifiernonlinearities-improve-neural-networkmaas/367f2c63a6f6a10b3b64b8729d601e69337ee3
fusus-147	doi.org	https://doi.org/10.1021/ci0341161
fusus-147	doi.org	https://doi.org/10.1109/icdmw.2009.94
fusus-147	doi.org	https://doi.org/10.1016/j.jallcom.2023.171224
fusus-147	doi.org	https://doi.org/10.2320/matertrans.46.2817
fusus-147	doi.org	https://doi.org/10.1016/j.actamat.2014.04.033
fusus-147	doi.org	https://doi.org/10.1016/j.pmatsci.2020.100719
fusus-147	doi.org	https://doi.org/10.1063/1.3587228
fusus-147	creativecommons.org	https://creativecommons.org/licenses/by/4.0/)
fusus-147	creativecommons.org	https://creativecommons.org/licenses/by/4.0
fusus-147	creativecommons.org	https://creativecommons.org/licenses/by/4.0