01639nas a2200205   4500000000100000000000100001008004100002260001200043100001800055700001600073700001700089700001800106700001800124245008800142856006500230300001600295490000700311520109000318022002501408       2020                            d  c03/20201 aTurker Tuncer1 aFatih Ertam1 aSengul Dogan1 aEmrah Aydemir1 aPawel Plawiak00aEnsemble residual network-based gender and activity recognition method with signals  uhttps://link.springer.com/article/10.1007/s11227-020-03205-1  a2119–21380 v763 a<p>Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals.</p>
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