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Ergebnis der Suche nach: "Machine Learning"
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Artikel 26771 The power of AI in marketing: enhancing efficiency and improving customer perception through AI-generated storyboards
Enthalten in International journal of information technology Bd. 16, 19.12.2023, Nr. 1, date:1.2024: 137-144
Online Ressource
Artikel 26772 The power of machine learning
Enthalten in Nature physics Bd. 15, 2.12.2019, Nr. 12, date:12.2019: 1208
Online Ressource
Artikel 26773 The power of one clean qubit in supervised machine learning
Enthalten in Scientific reports Bd. 13, 15.11.2023, Nr. 1, date:12.2023: 1-11
Online Ressource
Artikel 26774 The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy
Enthalten in Cancer imaging Bd. 24, 7.2.2024, Nr. 1, date:12.2024: 1-12
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Artikel 26775 The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning
Enthalten in Methodology Bd. 20, 22.03.2024, Nr. 1
Online Ressource
Artikel 26776 The prediction of aquifer groundwater level based on spatial clustering approach using machine learning
Enthalten in Environmental monitoring and assessment Bd. 193, 9.3.2021, Nr. 4, date:4.2021: 1-20
Online Ressource
Artikel 26777 The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models
Enthalten in BMC medical informatics and decision making Bd. 21, 5.4.2021, Nr. 1, date:12.2021: 1-9
Online Ressource
Artikel 26778 The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model
Enthalten in BMC medical informatics and decision making Bd. 23, 21.4.2023, Nr. 1, date:12.2023: 1-14
Online Ressource
Artikel 26779 The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models
Enthalten in European journal of medical research Bd. 28, 18.1.2023, Nr. 1, date:12.2023: 1-13
Online Ressource
Artikel 26780 The prediction of part thickness using machine learning in aluminum hot stamping process with partition temperature control
Enthalten in The international journal of advanced manufacturing technology Bd. 119, 10.1.2022, Nr. 5-6, date:3.2022: 3891-3902
Online Ressource


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