Diagnosis of Commutation Failure in a High- Voltage Direct Current Transmission System Based on Fuzzy Entropy Feature Vectors and a PCNN-GRU by Cao Ruirui, Yang Taigang, Li Guohui, Chen Shilong

“…Subsequently, the PCNN-GRU architecture performs deep feature extraction through two distinct mechanisms: the PCNN branch employs dual-path convolutional kernels of varying sizes for multidimensional feature mining, whereas the GRU network enhances temporal feature extraction capabilities. …”
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Extreme Short-Term Prediction of Unmanned Surface Vessel Nonlinear Motion Under Waves by Yiwen Wang, Jian Li, Shan Wang, Hantao Zhang, Long Yang, Weiguo Wu

“…To improve the prediction accuracy, a VMD-CNN-LSTM combined prediction model was applied based on Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM) neural network. …”
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LCC-Net: Swin transformer-CNN hybrid for enhanced land cover classification in natural disaster monitoring by P. Shailaja, Pala Mahesh Kumar, Nalla Nikhitha, Kunta Neeraj Kumar Reddy, Enthala Mukesh Reddy, Goli Ganesh Reddy, Vadde Indu

“…The core of LCC-Net employs the Swin Transformer Convolutional Neural Network (ST-CNN), which leverages self-attention mechanisms to capture intricate spatial features and temporal dynamics. …”
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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…We tackled two key obstacles—the limited fusion of heterogeneous sensor data and inadequate modeling of long-range temporal patterns—by integrating wearable accelerometer and heart-rate streams with a convolutional neural network–LSTM (CNN-LSTM) architecture and optional attention modules. …”
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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…[Purposes] Two-stream convolutional networks primarily achieve high recognition accuracy by fusing spatial and temporal features of videos. …”
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WiCNNAct: Wi-Fi-Based Human Activity Recognition Utilizing Deep Learning on the Edge Computing Devices by Venkata Raghava Shashank Viswanathuni, Rakesh Reddy Yakkati, Sreenivasa Reddy Yeduri, Linga Reddy Cenkeramaddi

“…The proposed approach utilizes the channel state information (CSI) measurements (complex values) from Wi-Fi and processes the different combinations of the real, imaginary, and absolute values using multi-channel 1D convolutional neural networks (1D-CNN). After conducting preliminary investigations, we validated various combinations of multi-channel 1D-CNNs and identified three methods for accurate activity recognition: a three-channel (real, imaginary, and absolute) setup, two channels (real and imaginary/real and absolute/imaginary and absolute), single channel (real/imaginary/absolute). …”
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Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction by Deyong Guan, Na Ren, Ke Wang, Qi Wang, Hualong Zhang

“…Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. …”
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Enhancing Wind Power Forecasting Accuracy Based on OPESC-Optimized CNN-BiLSTM-SA Model by Lele Wang, Dongqing Zhang

“…This study proposes OPESC-CNN-BiLSTM-SA, a hybrid model combining an optimized escape algorithm (OPESC), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) network, and self-attention (SA). …”
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Monitoring and Analyzing Driver Physiological States Based on Automotive Electronic Identification and Multimodal Biometric Recognition Methods by Shengpei Zhou, Nanfeng Zhang, Qin Duan, Xiaosong Liu, Jinchao Xiao, Li Wang, Jingfeng Yang

“…Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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One-dimensional time-frequency dual-channel visual transformer for bearing fault diagnosis under strong noise and limited data conditions by Shaobin Cai, Yuchen Wang, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…To further enhance sensitivity to local patterns and periodic variations, a cross-scale convolution module and a periodic feedforward network are introduced. …”
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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…With tool wear prediction as the application scenario, a deep learning model based on the fusion of multi-scale convolutional neural network, residual network, bidirectional long short-term memory network, and gated recurrent unit (MSCNN-ResNet-BiLSTM-GRU) was proposed. …”
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An Integrated CNN-BiLSTM-Transformer Framework for Improved Anomaly Detection Using Surveillance Videos by Sarfaraz Natha, Mohammad Siraj, Fareed Ahmed, Majid Altamimi, Muslim Syed

“…This system utilizes a Convolutional Neural Network (CNN) to extract the best spatial key features from the video stream, which are converted into time series data and coupled with Recurrent Neural Network (RNN) deep learning model Bidirectional Long Short-Term Memory (BiLSTM) extract temporal key features, and Multi-Head Self-Attention (MHSA) allows for the detection of short-term frame correlations. …”
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Dynamic facial expression recognition integrating spatiotemporal features by LIU Baobao, TAO Lu, YANG Jingjing, WANG Heying

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Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder by V. P. Kulagin, D. A. Akimov, S. A. Pavelyev, E. O. Guryanova

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Downhole Pressure Pulse Signal Recognition Based on SSA-CNN-LSTM by JIANG Panqin, LIU Xingbin, JIANG Zhicheng, LI Shanwen, HE Zhuang

“…Aiming at the problems of signal distortion and increased bit error rate caused by the interference of downhole sensor noise, fluid turbulent fluctuations, and wall friction effects on the wireless pressure pulse signals in the intelligent stratified water injection system, this paper designs a hybrid prediction model that integrates the sparrow search algorithm (SSA) and the convolutional long short-term memory network (CNN-LSTM). …”
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CALFUSE-KAN: A Multi-Scale Feature Fusion Network for Aircraft Trajectory Prediction by Yalan Zhang, Ruxin Sun, Fang Liu, Yongchao Wei

“…Aircraft trajectory prediction using Deep Learning models has become a significant challenge in the field of civil aviation. Traditional Convolutional Neural Network (CNN) models are difficult to capture the global features of the aircraft trajectories due to the fixed size of the receptive field of the convolution kernel, whereas attentional mechanism based Transformer, despite being able to handle long-term dependencies, has a high computational complexity and lacks local inductive bias. …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…To address these limitations, this study introduces an innovative attention-based ensemble model for fall detection; by integrating a convolutional neural network with channel attention and a bidirectional long short-term memory with temporal attention, the model prioritizes relevant information within time series data. …”
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Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI by Reem Ibrahim Alkanhel, Hager Saleh, Ahmed Elaraby, Saleh Alharbi, Hela Elmannai, Saad Alaklabi, Saeed Hamood Alsamhi, Sherif Mostafa

“…To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. …”
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Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting by Xiangyue Zhang, Chao Li, Ling Ji, Yuyun Kang, Mingming Pan, Zhuo Liu, Qiang Qi

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Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

“…To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. …”
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