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The validation or the interaction with the unseen data of our proposed predictor is shown in Figure 10.
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We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme.
Figure 8 illustrates the validating data results of our proposed predictor.
Overall, the data aggregation time of our proposed scheme (DTC+FAS) is up to 67, 60, and 55% shorter than that of SA (LSC+WPS).
Each document exchanged between two roles is a data model (an instance of our proposed information model).
We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes.
Examples with real data show the effectiveness of our proposed techniques by demonstrating that using BNNs can reduce load forecasting errors, compared to various existing techniques.
The results justify that the model is adequate for the data, and the performance of our proposed method is better than the others.
Continuous adaptation in response to data is a cornerstone of our proposed strategy.
The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches.
Nevertheless, our hindlimb ischemia model data support the significance of our proposed molecular mechanisms in a clinically relevant in vivo context, thereby complementing our cell culture, and ex vivo aortic ring data, which demonstrated that glycated VN inhibits VEGFR-2 activation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com