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Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques
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SYSNO 0547633 Title Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques Author(s) Dropka, N. (DE)
Böttcher, K. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Crystals. Roč. 11, č. 10 (2021). - : MDPI Article number 1218 Document Type Článek v odborném periodiku Grant GA18-18080S GA ČR - Czech Science Foundation (CSF), CZ - Czech Republic Institutional support UIVT-O - RVO:67985807 Language eng Country CH Keywords VGF-GaAs growth * machine learning * data mining * decision trees * correlation analysis * PCA biplot * k-means clustering Cooperating institutions Leibniz-Institut für Kristallzüchtung, Berlin (Germany)
Leibniz Institute for Catalysis, Rostock (Germany)URL http://dx.doi.org/10.3390/cryst11101218 Permanent Link http://hdl.handle.net/11104/0323829 File Download Size Commentary Version Access 0547633-afin.pdf 3 3.2 MB OA CC BY 4.0 Publisher’s postprint open-access
Number of the records: 1