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10801 |
Performance of machine learning algorithms for lung cancer prediction: a comparative approach
Enthalten in Scientific reports Bd. 14, 9.8.2024, Nr. 1, date:12.2024: 1-11
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10802 |
PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR MAPPING AND FORECASTING OF FLASH FLOOD SUSCEPTIBILITY IN TETOUAN, MOROCCO
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVI-4/W3-2021, 2022: 305-313. 9 S.
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10803 |
Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite
Enthalten in Scientific reports Bd. 14, 18.1.2024, Nr. 1, date:12.2024: 1-14
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10804 |
Performance of machine learning methods for cattle identification and recognition from retinal images
Enthalten in Applied intelligence Bd. 55, 15.3.2025, Nr. 7, date:5.2025: 1-20
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10805 |
Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
Enthalten in Applied water science Bd. 11, 6.11.2021, Nr. 12, date:12.2021: 1-20
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10806 |
Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis
Enthalten in Scientific reports Bd. 15, 25.5.2025, Nr. 1, date:12.2025: 1-12
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10807 |
Performance of machine learning models in estimation of ground reaction forces during balance exergaming
Enthalten in Journal of neuroEngineering and rehabilitation Bd. 19, 13.2.2022, Nr. 1, date:12.2022: 1-12
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10808 |
Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method
Enthalten in BMC emergency medicine Bd. 25, 21.2.2025, Nr. 1, date:12.2025: 1-15
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10809 |
Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images
Enthalten in European radiology experimental Bd. 3, 5.8.2019, Nr. 1, date:12.2019: 1-8
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10810 |
Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems
Enthalten in Forest ecosystems Bd. 8, 15.3.2021, Nr. 1, date:12.2021: 1-14
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