|Tytuł||Ensemble residual network-based gender and activity recognition method with signals|
|Publication Type||Journal Article|
|Autorzy||Tuncer T, Ertam F, Dogan S, Aydemir E, Pławiak P|
|Journal||Springer, The Journal of Supercomputing|
|Słowa kluczowe||daily sport activity recognition, ensemble residual network, gender identification, Machine learning, sensor signals|
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.