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Ensemble residual network-based gender and activity recognition method with signals

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Abstract

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.

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Authors and Affiliations

  1. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Turker Tuncer, Fatih Ertam & Sengul Dogan

  2. Department of Computer Engineering, Faculty of Engineering and Architecture, Kirsehir Ahi Evran University, Kirsehir, Turkey

    Emrah Aydemir

  3. Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 st., F-3, 31-155, Krakow, Poland

    Paweł Pławiak

  4. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland

    Paweł Pławiak

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  1. Turker Tuncer
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  2. Fatih Ertam
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  3. Sengul Dogan
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  4. Emrah Aydemir
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  5. Paweł Pławiak
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Correspondence to Turker Tuncer.

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Tuncer, T., Ertam, F., Dogan, S. et al. Ensemble residual network-based gender and activity recognition method with signals. J Supercomput 76, 2119–2138 (2020). https://doi.org/10.1007/s11227-020-03205-1

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  • DOI: https://doi.org/10.1007/s11227-020-03205-1

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