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learning greatest » learning rates (Expand Search), learning rate (Expand Search), learning test (Expand Search)
greatest decrease » treatment decreased (Expand Search), greater increase (Expand Search)
marked decrease » marked increase (Expand Search)
learning greatest » learning rates (Expand Search), learning rate (Expand Search), learning test (Expand Search)
greatest decrease » treatment decreased (Expand Search), greater increase (Expand Search)
marked decrease » marked increase (Expand Search)
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Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs....
Published 2025“…<p>Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. …”
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Image 2_Computed tomography and magnetic resonance imaging features of primary liver perivascular epithelioid cell tumor with renal angiomyolipoma: a case report and literature rev...
Published 2025“…The enhancement slightly decreased in the equilibrium phase and the delayed phase. …”
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Image 1_Computed tomography and magnetic resonance imaging features of primary liver perivascular epithelioid cell tumor with renal angiomyolipoma: a case report and literature rev...
Published 2025“…The enhancement slightly decreased in the equilibrium phase and the delayed phase. …”
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Data Sheet 1_Empagliflozin’s cardioenergetic protective effects through PPARα pathway modulation in heart failure.pdf
Published 2025“…Post-treatment, MRGlu and glucose uptake decreased markedly in the empagliflozin (EMPG) group, while no significant changes were observed in the fenofibrate (FF) group. …”
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Importance of random forest model.
Published 2025“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”
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Schematic of the Baidu SVI collection.
Published 2025“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”
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Map of the study area.
Published 2025“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”
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Research framework.
Published 2025“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”
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Perception type distribution and typical images.
Published 2025“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”
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