|
26641 |
The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP
Enthalten in Scientific reports Bd. 13, 6.10.2023, Nr. 1, date:12.2023: 1-13
|
|
|
|
26642 |
The importance of planning CT-based imaging features for machine learning-based prediction of pain response
Enthalten in Scientific reports Bd. 13, 13.10.2023, Nr. 1, date:12.2023: 1-11
|
|
|
|
26643 |
The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease
Enthalten in World journal of urology Bd. 42, 1.11.2024, Nr. 1, date:12.2024: 1-9
|
|
|
|
26644 |
The influence of different factors on the bond strength of lithium disilicate-reinforced glass–ceramics to Resin: a machine learning analysis
Enthalten in BMC oral health Bd. 25, 18.2.2025, Nr. 1, date:12.2025: 1-12
|
|
|
|
26645 |
The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis
Enthalten in La Radiologia medica Bd. 129, 13.5.2024, Nr. 7, date:7.2024: 1025-1037
|
|
|
|
26646 |
The influence of negative training set size on machine learning-based virtual screening
Enthalten in Journal of cheminformatics Bd. 6, 11.6.2014, Nr. 1, date:12.2014: 1-9
|
|
|
|
26647 |
The influence of the inactives subset generation on the performance of machine learning methods
Enthalten in Journal of cheminformatics Bd. 5, 5.4.2013, Nr. 1, date:12.2013: 1-8
|
|
|
|
26648 |
The information content of jet quenching and machine learning assisted observable design
Enthalten in Journal of high energy physics Bd. 2022, 3.10.2022, Nr. 10, date:10.2022: 1-33
|
|
|
|
26649 |
The International Metallographic Society Presents ‘Characterization of Materials and Microstructure through Metallography, Image Analysis, Machine Learning, and Mechanical Testing: Fundamental and Applied Studies’ at IMAT 2022
Enthalten in Metallography, microstructure, and analysis Bd. 11, 25.7.2022, Nr. 3, date:6.2022: 349-350
|
|
|
|
26650 |
The interplay of machine learning-based resonant anomaly detection methods
Enthalten in The European physical journal / C / Particles and fields Bd. 84, 8.3.2024, Nr. 3, date:3.2024: 1-21
|
|
Administration
