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Second, we compare computer strategies, showing that modeling pragmatics on a semantic parsing model achieves a higher accuracy for more strategic players.
Our deep learning model achieves strong performance across a broad range of materials.
Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.
Thus, the granular model achieves an important step in predicting hot tearing formation.
The proposed model achieves acceptable result in comparison to experimental results and a literature model.
This model achieves 3D visualized modeling for each group of structural fractures and their attribute characteristics.
The empirical results show that the proposed model achieves the improved forecasting accuracy.
The results show that the DNN model achieves the best balanced precision and recall.
The results show that PMS model achieves more standard vertical descent profiles over vectoring.
Results obtained through simulation show that the model achieves a high degree of accuracy.
These results indicate that the proposed model achieves a satisfactory balance between efficiency and efficacy.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com