Table 7 Performance comparison with different percentage Split for dataset #2

90% training set − 10% testing set

 MLP-NN

79.16

84.61

72.73

0.5795

0.7857

0.8148

0.7844

 RBF-NN

80.56

77.78

83.33

0.612

0.8223

0.7999

0.8050

 RNN

79.17

72.72

84.62

0.5795

0.8

0.7619

0.7844

 LSTM

79.17

81.82

76.92

0.5853

0.75

0.7826

0.7933

 SEFRON [Dataset#2]

85.41

82.61

88

0.7079

0.8636

0.8445

0.8526

85% training set − 15% testing set

 MLP-NN

77.78

73.68

82.35

0.5604

0.8235

0.7778

0.7789

 RBF-NN

83.33

92.30

72.72

0.6693

0.8

0.8571

0.7844

 RNN

72.22

66.67

77.78

0.4472

0.75

0.7059

0.72

 LSTM

80.56

77.78

83.33

0.612

0.823

0.799

0.805

 SEFRON [Dataset#2]

86.61

84.21

88.23

0.7233

0.1389

0.8649

0.8619

80% training set − 20% testing set

 MLP-NN

81.25

78.26

84

0.6242

0.8181

0.8

0.8108

 RBF-NN

87.5

82.61

92

0.7513

0.9048

0.8260

0.8636

 RNN

81.25

83.33

79.17

0.6255

0.8

0.8163

0.8122

 LSTM

81.25

87.5

75

0.6299

0.7778

0.8235

0.81

 SEFRON [Dataset#2]

89.58

91.3

88

0.7923

0.875

0.8936

0.8963

70% training set − 30% testing set

 MLP-NN

83.33

77.78

88.89

0.6708

0.875

0.823

0.8314

 RBF-NN

84.72

77.78

91.68

0.7012

0.9032

0.8358

0.8443

 RNN

87.5

89.19

85.71

0.7499

0.8684

0.88

0.8743

 LSTM

83.33

89.19

77.14

0.6696

0.8049

0.8461

0.8295

 SEFRON [Dataset#2]

88.89

89.47

88.23

0.7771

0.8947

0.8947

0.8885