Table 3 Fitted generalized pareto and power law exponent (SOC) to the data of subjects belonging to group C.

From: Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality

 

C

\(C_1\)

\(C_2\)

\(C_3\)

\(C_{11}\)

\(C_{12}\)

\(C_{13}\)

\(C_{21}\)

\(C_{22}\)

\(C_{23}\)

\(C_{31}\)

\(C_{32}\)

\(C_{33}\)

\(GP(k,\sigma )\)

(0.0869,0.0001)

(0.1013,0.0001)

(0.1697,0.0001)

(0.5628,0.0030)

(0.4382,0.0033)

(1.0249,0.0020)

(1.1599,0.0004)

(0.0897,0.0031)

(0.4650,0.0016)

\(\Gamma (\beta ,\theta )\)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

\(\gamma \)

\(-\) 1.53

\(-\) 1.52

\(-\) 1.45

\(-\) 1.20

\(-\) 1.20

\(-\) 1.21

\(-\) 1.30

\(-\) 1.21

\(-\) 1.23

  1. \(C_{ij}\) is the jth video segment of subject i.
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