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10731 |
Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline
Enthalten in Brain Informatics Bd. 12, 8.2.2025, Nr. 1, date:12.2025: 1-27
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10732 |
Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
Enthalten in Scientific reports Bd. 15, 5.3.2025, Nr. 1, date:12.2025: 1-15
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10733 |
Novel method of building train and test sets for evaluation of machine learning models related to software bugs assignment
Enthalten in Scientific reports Bd. 13, 6.12.2023, Nr. 1, date:12.2023: 1-11
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10734 |
Novel models by machine learning to predict prognosis of breast cancer brain metastases
Enthalten in Journal of translational medicine Bd. 21, 21.6.2023, Nr. 1, date:12.2023: 1-18
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10735 |
Novel models by machine learning to predict the risk of cardiac disease-specific death in young patients with breast cancer
Enthalten in Discover oncology Bd. 15, 18.12.2024, Nr. 1, date:12.2024: 1-13
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10736 |
Novel Surface Topography and Microhardness Characterization of Laser Clad Layer on TC4 Titanium Alloy Using Laser-Induced Breakdown Spectroscopy and Machine Learning
Enthalten in Metallurgical and materials transactions / A / Physical metallurgy and materials science Bd. 53, 14.8.2022, Nr. 10, date:10.2022: 3639-3653
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10737 |
Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO 2 -SWCNTs) fluid flow
Enthalten in Scientific reports Bd. 15, 19.2.2025, Nr. 1, date:12.2025: 1-16
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10738 |
Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns
Enthalten in Scientific reports Bd. 14, 12.8.2024, Nr. 1, date:12.2024: 1-35
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10739 |
Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach
Enthalten in Journal of scientific computing Bd. 92, 24.6.2022, Nr. 2, date:8.2022: 1-30
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10740 |
Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models
Enthalten in Earth, planets and space Bd. 74, 17.8.2022, Nr. 1, date:12.2022: 1-10
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