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  • Open Access


    Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

    Batyrkhan Omarov1,*, Meirzhan Baikuvekov1, Zeinel Momynkulov2, Aray Kassenkhan3, Saltanat Nuralykyzy3, Mereilim Iglikova3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627

    Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of… More >

  • Open Access


    Use of Local Region Maps on Convolutional LSTM for Single-Image HDR Reconstruction

    Seungwook Oh, GyeongIk Shin, Hyunki Hong*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4555-4572, 2022, DOI:10.32604/cmc.2022.022086

    Abstract Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps More >

  • Open Access


    AttEF: Convolutional LSTM Encoder-Forecaster with Attention Module for Precipitation Nowcasting

    Wei Fang1,2,*, Lin Pang1, Weinan Yi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 453-466, 2021, DOI:10.32604/iasc.2021.016589

    Abstract Precipitation nowcasting has become an essential technology underlying various public services ranging from weather advisories to citywide rainfall alerts. The main challenge facing many algorithms is the high non-linearity and temporal-spatial complexity of the radar image. Convolutional Long Short-Term Memory (ConvLSTM) is appropriate for modeling spatiotemporal variations as it integrates the convolution operator into recurrent state transition functions. However, the technical characteristic of encoding the input sequence into a fixed-size vector cannot guarantee that ConvLSTM maintains adequate sequence representations in the information flow, which affects the performance of the task. In this paper, we propose… More >

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