Search alternatives:
custom algorithm » fusion algorithm (Expand Search), control algorithm (Expand Search), lasso algorithm (Expand Search)
complement box » complement low (Expand Search), complement _ (Expand Search), complement 5a (Expand Search)
box algorithm » best algorithm (Expand Search), _ algorithm (Expand Search), ii algorithm (Expand Search)
level finding » novel findings (Expand Search), review finding (Expand Search), level coding (Expand Search)
custom algorithm » fusion algorithm (Expand Search), control algorithm (Expand Search), lasso algorithm (Expand Search)
complement box » complement low (Expand Search), complement _ (Expand Search), complement 5a (Expand Search)
box algorithm » best algorithm (Expand Search), _ algorithm (Expand Search), ii algorithm (Expand Search)
level finding » novel findings (Expand Search), review finding (Expand Search), level coding (Expand Search)
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Customer point clustering results.
Published 2025“…First, the paper systematically sorts out the classification and definition of no-fly zones as well as their impact mechanisms on UAV path planning, and elaborates on the theoretical basis of vehicle-UAV collaborative delivery, including the constituent elements of the problem, methods for quantifying customer satisfaction, and the application framework of heuristic algorithms. …”
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Basic information of customer points.
Published 2025“…First, the paper systematically sorts out the classification and definition of no-fly zones as well as their impact mechanisms on UAV path planning, and elaborates on the theoretical basis of vehicle-UAV collaborative delivery, including the constituent elements of the problem, methods for quantifying customer satisfaction, and the application framework of heuristic algorithms. …”
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Data Sheet 1_Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs.pdf
Published 2025“…</p>Results<p>In the internal validation, the Lung Abnormality and Tuberculosis models achieved an AUC of 0.94, while the Radiological Findings model yielded a mean AUC of 0.84. During the external validation, utilizing the ground truth generated by board-certified thoracic radiologists, the algorithm achieved better sensitivity in 6 out of 11 classes than physicians with varying experience levels. …”
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