Anomaly Detection in WBANs Using CNN-Autoencoders and LSTMs
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Publisher
Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-57870-0_17
Reference14 articles.
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2. Harun Al Rasyid, M.U., Setiawan, F., Nadhori, I.U., Sudarsonc, A., Tamami, N.: Anomalous data detection in WBAN measurements. In: 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia, pp. 303–309 (2018). https://doi.org/10.1109/KCIC.2018.8628522
3. Canizo, M., Triguero, I., Conde, A., Onieva, E.: Multi-head CNN-RNN for multi-time series anomaly detection: an industrial case study. Neurocomputing 363, 246–260 (2019). https://doi.org/10.1016/j.neucom.2019.07.034
4. Khan, F.A., Haldar, N.A.H., Ali, A., Iftikhar, M., Zia, T.A., Zomaya, A.Y.: A continuous change detection mechanism to identify anomalies in ECG signals for WBAN-based healthcare environments. IEEE Access 5, 13531–13544 (2017). https://doi.org/10.1109/ACCESS.2017.2714258
5. Provotar, O.I., Linder, Y.M., Veres, M.M.: Unsupervised anomaly detection in time series using LSTM-based autoencoders. In: 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, pp. 513–517 (2019). https://doi.org/10.1109/ATIT49449.2019.9030505
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