Table 5.

Accuracy of SVM, BN, KNN, CapsNet, and CNN networks in image classification by ten features, including subband power, mean, standard deviation, zero-crossing rate, fractal dimension, entropy, and correlation dimension.

Classifiers	Features
	Power theta	Power alpha	Power beta	Power gamma	Mean	Standard deviation	Zero-crossingrate	Fractal dimension	Approximate entropy	Correlation dimension	SAETM
SVM	0.5132 ± 0.02	0.5818 ± 0.01	0.4363 ± 0.06	0.5527 ± 0.01	0.3280 ± 0.13	0.3620 ± 0.02	0.4750 ± 0.12	0.5145 ± 0.03	0.4239 ± 0.05	0.3840 ± 0.01	0.7536 ± 0.01
BN	0.4746 ± 0.13	0.5373 ± 0.02	0.5248 ± 0.05	0.4323 ± 0.01	0.4129 ± 0.01	0.3359 ± 0.01	0.3984 ± 0.03	0.4719 ± 0.11	0.3487 ± 0.15	0.4602 ± 0.04	0.6906 ± 0.12
KNN	0.5601 ± 0.02	0.4982 ± 0.12	0.4880 ± 0.05	0.3760 ± 0.05	0.3717 ± 0.01	0.3129 ± 0.04	0.4573 ± 0.04	0.3985 ± 0.03	0.4604 ± 0.04	0.4228 ± 0.02	0.7158 ± 0.04
CNN	0.6534 ± 0.03	0.6710 ± 0.04	0.5730 ± 0.07	0.5915 ± 0.04	0.4872 ± 0.06	0.3916 ± 0.01	0.4716 ± 0.02	0.5610 ± 0.04	0.5201 ± 0.07	0.5072 ± 0.12	0.8305 ± 0.02
CapsNet	0.6721 ± 0.06	0.6430 ± 0.11	0.5928 ± 0.02	0.6592 ± 0.07	0.5340 ± 0.16	0.4924 ± 0.12	0.5935 ± 0.04	0.6026 ± 0.15	0.5873 ± 0.02	0.6453 ± 0.13	0.8159 ± 0.01
Average (ACC)	0.5747 ± 0.052	0.5863 ± 0.06	0.5230 ± 0.05	0.5223 ± 0.036	0.4268 ± 0.074	0.3790 ± 0.04	0.4792 ± 0.05	0.5097 ± 0.072	0.4681 ± 0.066	0.4839 ± 0.064	0.7613 ± 0.04