Table 7 Result analysis of the ablation study of SERDP-DLEOCE methodology.

From: Improving real-time emotion recognition system in assistive communication technologies for disabled persons using deep learning with equilibrium algorithm

Model

\(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\)

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\)

\(\:\varvec{F}{1}_{\varvec{S}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\)

SERDP-DLEOCE

95.15

80.95

71.65

75.00

ENN Model

94.61

80.17

71.11

74.21

EO Algorithm

94.02

79.35

70.34

73.33

Word2Vec Technique

93.24

78.72

69.83

72.66

Back to article page