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The proposed approach was compared with the traditional NB approach.
The modeling approach was compared and validated with experimental measurements.
This approach was compared to Detrended Fluctuations Analysis (DFA) in Fig. 2 and in supplementary Figures S1 S4.
This approach was compared with results obtained from a commercially available ELISA immunosensor.
The approach was compared to the standard Simplex square residual minimization of EIS data.
Composite approach was compared to LOCF (ANCOVA) and MMRM in 59 industry sponsored RCT's.
This simultaneous optimization approach was compared with the desirability function method.
This new approach was compared to traditional soil heat flux measurements.
The proposed DMO approach was compared with the empirical design and the existing method in commercial software.
The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods.
This approach was compared to the classical approach assuming a fixed HRF for each voxel in the brain.
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