n=18, Wilcoxon signed rank,<italic toggle="yes">P=0.325,<italic toggle="yes">Z=1.0, Cliff's delta=0.01; Fig 4
n=18, Wilcoxon signed rank,<italic toggle="yes">P=0.442
n=27, Wilcoxon signed rank,<italic toggle="yes">P=0.005,<italic toggle="yes">Z=2.8, Cliff's delta=0.36; Fig 4
n=27, Wilcoxon signed rank,<italic toggle="yes">P=0.006
n=26, Wilcoxon signed rank,<italic toggle="yes">P=0.027,<italic toggle="yes">Z=2.2, Cliff's delta=0.21; Fig 4
n=21, Wilcoxon signed rank,<italic toggle="yes">P=0.058,<italic toggle="yes">Z=1.9, Cliff's delta=0.10
toggle="yes">P=0.258,<italic toggle="yes">Z=1.17, Cliff's delta=0.17; Fig 6
n=21, Wilcoxon signed rank,<italic toggle="yes">P=0.058

n=18, Wilcoxon signed rank,<italic toggle="yes">P=0.325,<italic toggle="yes">Z=1.0, Cliff's delta=0.01; Fig 4
n=27, Wilcoxon signed rank,<italic toggle="yes">P=0.005,<italic toggle="yes">Z=2.8, Cliff's delta=0.36; Fig 4
n=26, Wilcoxon signed rank,<italic toggle="yes">P=0.027,<italic toggle="yes">Z=2.2, Cliff's delta=0.21; Fig 4
n=21, Wilcoxon signed rank,<italic toggle="yes">P=0.058,<italic toggle="yes">Z=1.9, Cliff's delta=0.10
toggle="yes">P=0.258,<italic toggle="yes">Z=1.17, Cliff's delta=0.17; Fig 6

Multi-scale Xception based depthwise separable convolution for single image super-resolution

well known image super resolution method

pone.0249278.e006">removedGRAPHIC O (y, x, l)=∑ j=1
pone.0249278.e007">removedGRAPHIC L (θ)=1 m ∑ i=1

A face recognition software framework based on principal component analysis

most representative neural network method
efficient data dimensionality reduction method
variations of pca based image representation method
background noise does affect most face recognition algorithms
neural network based face detection method
researchers integrate color based method

n</disp formula><disp formula id="pone.0254965.e002">removedGRAPHIC s=∑ i=1
x ¯ 2 n -1</disp formula><disp formula id="pone.0254965.e003">removedGRAPHIC s 2=∑ i=1

Revisiting the relationship between constructive alignment and learning approaches: A perceived alignment perspective