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40151 |
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI
Enthalten in BMC geriatrics Bd. 25, 17.3.2025, Nr. 1, date:12.2025: 1-11
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40152 |
Utilizing machine learning to model interdependency of bulk molecular weight, solution concentration, and thickness of spin coated polystyrene thin films
Enthalten in Materials Research Society: MRS communications Bd. 14, 12.2.2024, Nr. 2, date:4.2024: 230-236
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40153 |
Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine)
Enthalten in Scientific reports Bd. 15, 24.1.2025, Nr. 1, date:12.2025: 1-13
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40154 |
Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm
Enthalten in Scientific reports Bd. 15, 16.7.2025, Nr. 1, date:12.2025: 1-13
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40155 |
Utilizing machine learning to predict participant response to follow-up health surveys in the Millennium Cohort Study
Enthalten in Scientific reports Bd. 14, 28.10.2024, Nr. 1, date:12.2024: 1-12
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40156 |
Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction
Enthalten in Scientific reports Bd. 14, 23.5.2024, Nr. 1, date:12.2024: 1-13
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40157 |
Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods
Enthalten in Scientific reports Bd. 14, 5.3.2024, Nr. 1, date:12.2024: 1-9
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40158 |
Utilizing reinforcement learning for de novo drug design
Enthalten in Machine learning Bd. 113, 8.4.2024, Nr. 7, date:7.2024: 4811-4843
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40159 |
Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand
Enthalten in Scientific reports Bd. 15, 11.3.2025, Nr. 1, date:12.2025: 1-12
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40160 |
Utilizing social media and machine learning for personality and emotion recognition using PERS
Enthalten in Neural computing & applications Bd. 35, 5.9.2023, Nr. 33, date:11.2023: 23927-23941
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Administration
