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For a massive land grab, Sekai Camera will have to come up with a viable business model, achieve a critical mass of tags to make the app worthwhile and optimize the technology.
Since the dynamics of the experimental data and those of the generalised VJ model achieve a close match, we explore numerically tractable simplifications to the equations.
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The mean AIC is 346.12 in the GWR model, achieving a slightly better goodness-of-fit.
The multiband source/filter model achieves a flatter frequency response for the residual signals.
Our model achieves a (R^{2}=0.35) on the test set with mean absolute error of 9.7 years.
Their forecast model achieved a mean absolute percentage error (MAPE) of 9.9%.
The final consensus model achieved a precision, which was similar to the estimated experimental accuracy.
The model achieved a mean square error ranging from 5.7% to 15.33%.
The neural network model achieved a better performance than the linear model.
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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