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The depth accuracy was found to be 38 µm.
The predicted accuracy was found to be very good for the absorptivity prediction.
The decision tree algorithm's (CART) classification accuracy was found better than other two techniques.
Model accuracy was found to be 96.65% from the data obtained from validation experiment.
Resultant bit accuracy was found to be 10.4 for 122 MHz input signal band.
Relevant impact of temporal reporting accuracy was found for these validation datasets.
With higher cone angles and/or bed heights, the computational accuracy was found to deteriorate.
The predictive accuracy was found to range from 6.4 to 3.7 K, depending on polymer class.
While in the direct unsplinted technique, no difference in accuracy was found between parallel condition and 15° angulated condition.
This was done for under and over compensation and the accuracy was found to be over 97%%.
The highest accuracy was found when the voxel method was used for pruned biomass prediction (R2 = 0.731).
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