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25541 |
Predicting metastasis in gastric cancer patients: machine learning-based approaches
Enthalten in Scientific reports Bd. 13, 13.3.2023, Nr. 1, date:12.2023: 1-12
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25542 |
Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning
Enthalten in Science China / Technological sciences Bd. 67, 8.12.2023, Nr. 1, date:1.2024: 259-270
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25543 |
Predicting Microstructure-Sensitive Fatigue-Crack Path in 3D Using a Machine Learning Framework
Enthalten in JOM Bd. 71, 1.7.2019, Nr. 8, date:8.2019: 2680-2694
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25544 |
Predicting mild cognitive impairment progression to Alzheimer’s disease based on machine learning analysis of cortical morphological features
Enthalten in Aging clinical and experimental research Bd. 35, 5.7.2023, Nr. 8, date:8.2023: 1721-1730
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25545 |
Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach
Enthalten in Scientific reports Bd. 14, 1.8.2024, Nr. 1, date:12.2024: 1-18
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25546 |
Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries
Enthalten in BMC health services research Bd. 23, 25.5.2023, Nr. 1, date:12.2023: 1-12
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25547 |
Predicting Model for Device Density of States of Quantum-Confined SiC Nanotube with Magnetic Dopant: An Integrated Approach Utilizing Machine Learning and Density Functional Theory
Enthalten in Silicon Bd. 16, 10.9.2024, Nr. 16, date:11.2024: 5991-6009
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25548 |
Predicting model of I–V characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework
Enthalten in Journal of computational electronics Bd. 22, 23.5.2023, Nr. 4, date:8.2023: 999-1009
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25549 |
Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy
Enthalten in Theoretical and applied genetics Bd. 134, 3.8.2021, Nr. 11, date:11.2021: 3743-3757
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25550 |
Predicting morphodynamics in dam-break flows using combined machine learning and numerical modelling
Enthalten in Modeling earth systems and environment Bd. 11, 11.1.2025, Nr. 1, date:2.2025: 1-22
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