بدائل البحث:
complement cscap » complement cascade (توسيع البحث), complement 5a (توسيع البحث), complement c3 (توسيع البحث)
under algorithm » new algorithm (توسيع البحث), tide algorithm (توسيع البحث), kepler algorithm (توسيع البحث)
cscap algorithm » cc3d algorithm (توسيع البحث), ipca algorithm (توسيع البحث), custom algorithm (توسيع البحث)
elements under » elements tended (توسيع البحث), sediments under (توسيع البحث), elements over (توسيع البحث)
level finding » novel findings (توسيع البحث), review finding (توسيع البحث), level coding (توسيع البحث)
complement cscap » complement cascade (توسيع البحث), complement 5a (توسيع البحث), complement c3 (توسيع البحث)
under algorithm » new algorithm (توسيع البحث), tide algorithm (توسيع البحث), kepler algorithm (توسيع البحث)
cscap algorithm » cc3d algorithm (توسيع البحث), ipca algorithm (توسيع البحث), custom algorithm (توسيع البحث)
elements under » elements tended (توسيع البحث), sediments under (توسيع البحث), elements over (توسيع البحث)
level finding » novel findings (توسيع البحث), review finding (توسيع البحث), level coding (توسيع البحث)
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Algorithmic experimental parameter design.
منشور في 2024"…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…"
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Spatial spectrum estimation for three algorithms.
منشور في 2024"…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…"
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Data Sheet 1_Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs.pdf
منشور في 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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