Search alternatives:
significant optimization » significant limitation (Expand Search), significant application (Expand Search), significant association (Expand Search)
optimization data » optimization paths (Expand Search), optimization based (Expand Search), optimization _ (Expand Search)
a decrease » _ decreased (Expand Search), _ decreases (Expand Search)
_ decrease » _ decreased (Expand Search)
significant optimization » significant limitation (Expand Search), significant application (Expand Search), significant association (Expand Search)
optimization data » optimization paths (Expand Search), optimization based (Expand Search), optimization _ (Expand Search)
a decrease » _ decreased (Expand Search), _ decreases (Expand Search)
_ decrease » _ decreased (Expand Search)
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A Wearable Dual-Modal Patch for Rapid Pre-Hospital Diagnosis of Acute Myocardial Infarction
Published 2025“…The time required for diagnosing ischemia was significantly reduced to 50 min after its onset. The patch is optimally integrated into a stamp-sized band-aid, accompanied by a smartphone app for data visualization and real-time analysis. …”
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A Wearable Dual-Modal Patch for Rapid Pre-Hospital Diagnosis of Acute Myocardial Infarction
Published 2025“…The time required for diagnosing ischemia was significantly reduced to 50 min after its onset. The patch is optimally integrated into a stamp-sized band-aid, accompanied by a smartphone app for data visualization and real-time analysis. …”
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A Wearable Dual-Modal Patch for Rapid Pre-Hospital Diagnosis of Acute Myocardial Infarction
Published 2025“…The time required for diagnosing ischemia was significantly reduced to 50 min after its onset. The patch is optimally integrated into a stamp-sized band-aid, accompanied by a smartphone app for data visualization and real-time analysis. …”
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Parameter optimization results of BiLSTM.
Published 2025“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”
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Heat map of multi-objective optimization results.
Published 2024“…The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. …”