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14061 - 14070 von 14076

	14061	Winning determinants between top and second division in Chinese professional football leagues: an interpretable machine learning approach Enthalten in BMC sports science, medicine & rehabilitation Bd. 17, 21.4.2025, Nr. 1, date:12.2025: 1-11
	14062	With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots Enthalten in Scientific reports Bd. 14, 3.3.2024, Nr. 1, date:12.2024: 1-10
	14063	WOOD-LEAF UNSUPERVISED CLASSIFICATION OF SILVER BIRCH TREES FOR BIOMASS ASSESSMENT USING OBLIQUE POINT CLOUDS Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 1795-1802. 8 S.
	14064	WorMachine: machine learning-based phenotypic analysis tool for worms Enthalten in BMC biology Bd. 16, 16.1.2018, Nr. 1, date:12.2018: 1-11
	14065	Wrist accelerometry and machine learning sensitively capture disease progression in prodromal Parkinson’s disease Enthalten in npj Parkinson's Disease Bd. 11, 17.6.2025, Nr. 1, date:12.2025: 1-7
	14066	X-Detect: explainable adversarial patch detection for object detectors in retail Enthalten in Machine learning Bd. 113, 19.6.2024, Nr. 9, date:9.2024: 6273-6292
	14067	X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head Enthalten in Scientific reports Bd. 15, 20.4.2025, Nr. 1, date:12.2025: 1-13
	14068	X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for CHD Diagnosis Enthalten in Scientific data Bd. 12, 21.3.2025, Nr. 1, date:12.2025: 1-8
	14069	XAI-based cross-ensemble feature ranking methodology for machine learning models Enthalten in International journal of information technology Bd. 15, 29.4.2023, Nr. 4, date:4.2023: 1759-1768
	14070	XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance Enthalten in Machine learning Bd. 113, 16.7.2024, Nr. 9, date:9.2024: 6871-6910

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