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A Review of Research on Industrial Time Series Classification for Machinery based on Deep Learning.

, , , , and . MENACOMM, page 89-94. IEEE, (2022)

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Lightweight Feature-based Priority Sampling for Industrial IoT Multivariate Time Series., , , and . AICCSA, page 1-8. IEEE, (2023)A deep learning scheme for efficient multimedia IoT data compression., , , , and . Ad Hoc Networks, (2023)Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net., , , and . Mob. Networks Appl., 27 (3): 984-996 (2022)Using DenseNet for IoT multivariate time series classification., , and . ISCC, page 1-6. IEEE, (2020)Robust IoT time series classification with data compression and deep learning., , , and . Neurocomputing, (2020)Deep recurrent neural network-based autoencoder for photoplethysmogram artifacts filtering., , , and . Comput. Electr. Eng., (2021)An Efficient and Robust MIoT Communication Solution using a Deep Learning Approach., , , , and . IWCMC, page 366-371. IEEE, (2022)A Review of Research on Industrial Time Series Classification for Machinery based on Deep Learning., , , , and . MENACOMM, page 89-94. IEEE, (2022)XorshiftH128+: A hybrid random number generator for lightweight IoT., , , , , , and . AIBThings, page 1-5. IEEE, (2023)Using Adaptive Sampling and DWT Lifting Scheme for Efficient Data Reduction in Wireless Body Sensor Networks., , , , and . WiMob, page 1-8. IEEE, (2018)