NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

Table 3

Validation analysis of the NSGA-III-based CNN model.

Model	Specificity	AUC	Sensitivity	F-measure	Accuracy
TCMSearch [17]	78.461.10	78.791.10	78.111.76	78.311.61	78.121.71
SVM [16]	78.311.62	78.241.68	78.211.59	78.421.62	78.241.73
G-Bean [3]	78.351.68	78.411.61	78.431.69	78.501.42	78.451.53
TTA10 [19]	78.511.46	78.581.35	78.361.55	78.461.51	78.481.31
ViLiP [18]	78.561.46	78.551.33	78.601.43	78.541.35	78.631.35
SOSC [1]	77.321.64	77.231.34	77.591.63	77.131.48	77.181.44
GeoNames [20]	77.461.54	77.431.40	77.411.39	77.331.25	77.351.33
CRRP [21]	77.511.23	77.521.44	77.321.30	77.521.21	77.321.35
ASE [22]	77.460.68	77.720.74	77.650.72	77.640.46	77.650.39
CNN [23]	77.681.21	77.820.72	77.821.33	77.701.32	77.791.32
VDP [23]	78.130.36	78.350.78	78.560.75	78.650.32	78.760.36
Proposed DCNN	81.560.78	81.520.74	81.150.69	81.560.89	81.360.89