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25651 |
Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology
Enthalten in The international journal of advanced manufacturing technology Bd. 116, 30.6.2021, Nr. 3-4, date:9.2021: 1247-1257
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25652 |
Predicting the value of football players: machine learning techniques and sensitivity analysis based on FIFA and real-world statistical datasets
Enthalten in Applied intelligence Bd. 55, 4.1.2025, Nr. 4, date:2.2025: 1-26
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25653 |
Predicting the Viscosity of CaF2-Based Slag and Reverse Design of Electroslag Systems Using Explainable Machine Learning
Enthalten in Metallurgical and materials transactions / B / Process metallurgy and materials processing science Bd. 56, 14.4.2025, Nr. 3, date:6.2025: 3125-3139
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25654 |
Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
Enthalten in Scientific reports Bd. 15, 8.3.2025, Nr. 1, date:12.2025: 1-22
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25655 |
Predicting thermal conductivity of granite subjected to high temperature using machine learning techniques
Enthalten in Environmental earth sciences Bd. 84, 15.4.2025, Nr. 8, date:4.2025: 1-18
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25656 |
Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration
Enthalten in Nature Communications Bd. 16, 2.1.2025, Nr. 1, date:12.2025: 1-15
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25657 |
Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms
Enthalten in Scientific reports Bd. 13, 30.9.2023, Nr. 1, date:12.2023: 1-13
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25658 |
Predicting tool life and sound pressure levels in dry turning using machine learning models
Enthalten in The international journal of advanced manufacturing technology Bd. 135, 31.10.2024, Nr. 7-8, date:12.2024: 3777-3793
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25659 |
Predicting torsional capacity of reinforced concrete members by data-driven machine learning models
Enthalten in Frontiers of structural and civil engineering Bd. 18, 28.5.2024, Nr. 3, date:3.2024: 444-460
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25660 |
Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors
Enthalten in BMC health services research Bd. 25, 12.3.2025, Nr. 1, date:12.2025: 1-27
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