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39031 |
XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features
Enthalten in International journal of speech technology Bd. 27, 16.10.2024, Nr. 4, date:12.2024: 1055-1083
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39032 |
XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban
Enthalten in BMC geriatrics Bd. 23, 10.7.2023, Nr. 1, date:12.2023: 1-12
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39033 |
XGBoost model for predicting erectile dysfunction risk after radical prostatectomy: development and validation using machine learning
Enthalten in Discover oncology Bd. 16, 19.5.2025, Nr. 1, date:12.2025: 1-16
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39034 |
XGBoosting Cricket: Enhancing Predictive Modeling for Twenty20 Match Results Using Machine Learning and Statistical Techniques
Enthalten in SN Computer Science Bd. 5, 9.11.2024, Nr. 8, date:12.2024: 1-7
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39035 |
XMID-MQTT: explaining machine learning-based intrusion detection system for MQTT protocol in IoT environment
Enthalten in International journal of information security Bd. 24, 14.5.2025, Nr. 3, date:6.2025: 1-22
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39036 |
Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning
Enthalten in Behavioral ecology and sociobiology Bd. 77, 31.3.2023, Nr. 4, date:4.2023: 1-12
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39037 |
Year-round daily wildfire prediction and key factor analysis using machine learning: a case study of Gangwon State, South Korea
Enthalten in Scientific reports Bd. 15, 15.8.2025, Nr. 1, date:12.2025: 1-12
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39038 |
YI 2.1 Pulse Wave Velocity Estimation from the Radial Pulse Waveform using Gaussian Process Regression: A Machine Learning Based Study
Enthalten in Artery research Bd. 26, 31.12.2020, Nr. 1, date:12.2020: S12
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39039 |
Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean
Enthalten in Plant methods Bd. 16, 1.6.2020, Nr. 1, date:12.2020: 1-16
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39040 |
Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms
Enthalten in Discover food Bd. 5, 20.3.2025, Nr. 1, date:12.2025: 1-23
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Administration
