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    Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network by Yonghao He, Changjun Wen, Wei Xu

    Published 2025-01-01
    “…In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. …”
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    Predicting vasovagal reactions to needles from video data using 2D-CNN with GRU and LSTM. by Judita Rudokaite, Sharon Ong, Itir Onal Ertugrul, Mart P Janssen, Elisabeth Huis In 't Veld

    Published 2025-01-01
    “…We compared 5 different sequences of videos-45, 30, 20, 10 and 5 seconds to test the shortest video duration required to predict VVR levels. We used 2D-CNN with LSTM and GRU to predict continuous VVR scores and to classify discrete (low and high) VVR values obtained during the blood donation. …”
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  7. 567

    A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

    Published 2024-01-01
    “…Convolutional neural networks (CNNs) have evolved into essential components for a wide range of embedded applications due to their outstanding efficiency and performance. …”
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    A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

    Published 2024-01-01
    “…A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. …”
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    Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

    Published 2025-01-01
    Subjects: “…convolutional neural networks (CNNs)…”
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