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25961 |
Quantitative Classification and Prediction of Diagenetic Facies in Tight Gas Sandstone Reservoirs via Unsupervised and Supervised Machine Learning Models: Ledong Area, Yinggehai Basin
Enthalten in Natural resources research Bd. 32, 21.9.2023, Nr. 6, date:12.2023: 2685-2710
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25962 |
Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning
Enthalten in Scientific reports Bd. 14, 5.9.2024, Nr. 1, date:12.2024: 1-20
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25963 |
Quantitative design rules for protein-resistant surface coatings using machine learning
Enthalten in Scientific reports Bd. 9, 22.1.2019, Nr. 1, date:12.2019: 1-12
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25964 |
Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies
Enthalten in Scientific reports Bd. 12, 11.6.2022, Nr. 1, date:12.2022: 1-10
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25965 |
Quantitative evaluation of image segmentation algorithms based on fuzzy convolutional neural network
Enthalten in International journal of information technology Bd. 15, 21.8.2023, Nr. 7, date:10.2023: 3807-3812
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25966 |
Quantitative Gaussian approximation of randomly initialized deep neural networks
Enthalten in Machine learning Bd. 113, 25.6.2024, Nr. 9, date:9.2024: 6373-6393
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25967 |
Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning
Enthalten in Scientific reports Bd. 15, 26.3.2025, Nr. 1, date:12.2025: 1-11
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25968 |
Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients
Enthalten in Respiratory research Bd. 25, 28.10.2024, Nr. 1, date:12.2024: 1-15
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25969 |
Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography
Enthalten in Nature Communications Bd. 14, 16.11.2023, Nr. 1, date:12.2023: 1-11
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25970 |
Quantized hashing: enabling resource-efficient deep learning models at the edge
Enthalten in International journal of information technology Bd. 16, 16.3.2024, Nr. 4, date:4.2024: 2353-2361
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