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34181 |
Predictive modeling of CsFABiCuI6-based PSC with Nd-doped ZnO as ETL using machine learning and numerical simulation
Enthalten in Multiscale and multidisciplinary modeling, experiments and design Bd. 8, 26.4.2025, Nr. 6, date:6.2025: 1-23
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34182 |
Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches
Enthalten in Biodegradation Bd. 36, 28.12.2024, Nr. 1, date:2.2025: 1-17
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34183 |
Predictive modeling of gestational weight gain: a machine learning multiclass classification study
Enthalten in BMC pregnancy and childbirth Bd. 24, 8.11.2024, Nr. 1, date:12.2024: 1-11
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34184 |
Predictive Modeling of Hypoglycemia Risk with Basal Insulin Use in Type 2 Diabetes: Use of Machine Learning in the LIGHTNING Study
Enthalten in Diabetes therapy Bd. 10, 14.2.2019, Nr. 2, date:4.2019: 605-615
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34185 |
Predictive modeling of ICU-AW inflammatory factors based on machine learning
Enthalten in BMC neurology Bd. 24, 19.12.2024, Nr. 1, date:12.2024: 1-13
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34186 |
Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods
Enthalten in Scientific reports Bd. 14, 8.7.2024, Nr. 1, date:12.2024: 1-11
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34187 |
Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making
Enthalten in Zeitschrift für Kinderheilkunde Bd. 184, 11.2.2025, Nr. 2, date:2.2025: 1-21
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34188 |
Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models
Enthalten in Plant methods Bd. 18, 11.4.2022, Nr. 1, date:12.2022: 1-24
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34189 |
Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer
Enthalten in BMC pregnancy and childbirth Bd. 25, 19.3.2025, Nr. 1, date:12.2025: 1-13
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34190 |
Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience
Enthalten in Scientific reports Bd. 10, 11.11.2020, Nr. 1, date:12.2020: 1-10
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