Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method by Yujie Yuan, Yantao Wang, Chun Sing Lai

“…The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. …”
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Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning by Zhangjian Guo, Tongling Wang, Shuxun Chi, Li Huang

“…In this study, a model architecture integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM) network, and an attention mechanism is constructed, with a focus on optimizing local feature extraction, temporal modeling, and dynamic weight allocation capabilities. …”
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GaitPart‑based Cross‑view Gait Recognition Method by LIU Jianhu, LIU Xing, GU Miao, WANG Junzhu, ZHANG Hailong, DENG Hongxia

“…First, a convolutional network was used to extract shallow features from the original input sequence as the input of the two-path network. …”
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A lightweight and efficient gesture recognizer for traffic police commands using spatiotemporal feature fusion by Jun Xiao, Honghan Li, Ji Zhao

“…Additionally, a convolution network branch and a hybrid attention branch are incorporated to further extract skeleton information from the traffic police gesture data, assign different temporal weights to key frames, and enhance the focus on important channels. …”
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Packet-level labeling method for fine-grained multi-webpage browsing behavior recognition by GU Yue, CHEN Li, LI Dan, GAO Kaihui

“…This method combined one-dimensional convolutional neural networks with multi-head attention mechanisms to learn both local and global temporal correlation features between different packets within the same webpage. …”
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Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system. by Ting Xie, Xing Han

“…The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…Subsequently, the Temporal Convolutional Network (TCN) was employed to capture temporal dependencies and dynamic variations in the data. …”
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Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence by Zhao Zhang, Wang Li, Yuyang Zhang

“…With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. …”
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A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets by Yongxiang Wang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…Furthermore, the SCINet model outperforms traditional models such as temporal convolutional networks (TCN), gated recurrent units (GRU), and long short-term memory (LSTM) networks when based solely on historical prices. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…Second, standard convolutional layers are replaced with distribution shifting convolution (DSConv), leveraging dynamic sparsity and quantization mechanisms to reduce computational complexity. …”
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State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM by Yu Zhao, Yonghong Xu, Yidi Wei, Liang Tong, Yiyang Li, Minghui Gong, Hongguang Zhang, Baoying Peng, Yinlian Yan

“…To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. …”
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STARNet: A Deep‐Learning Algorithm for Surface Shortwave Radiation Retrieval From Fengyun‐4A by Mengmeng Song, Dazhi Yang, Hongrong Shi, Yun Chen, Bai Liu, Yanbo Shen, Zijing Ding, Xiang'ao Xia

“…The algorithm holds three technical innovations: (a) a data preprocessing method that highlights the correlation‐ and causality‐type climatology associations in the original reflectance and brightness temperature observations; (b) a graph network cascade that extracts topological spatio‐temporal features, and (c) a multi‐scale convolution network that extracts regular spatio‐temporal features. …”
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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…The proposed model, called DRAM, concatenates a dilated convolutional neural network (DCNN) module with a bidirectional long short-term memory (BiLSTM) module, and integrates an attention mechanism. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…These interpolated images are then used as input for a ConvLSTM (Convolutional Long Short-Term Memory) network, which integrates convolutional layers to capture spatial patterns and LSTM units to model temporal dependencies. …”
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Object detection in motion management scenarios based on deep learning. by Baocheng Pei, Yanan Sun, Yebiao Fu, Ting Ren

“…The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure's ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. …”
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Enhanced real-time Parkinson’s disease monitoring and severity prediction using a multi-faceted deep learning approach by Marreddy Naga Sabari, Deepak Ch

“…This research proposes a novel deep learning framework using a convolutional long short-term memory (LSTM) network to detect tremor anomalies in PD patients. …”
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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning by Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao

“…Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. …”
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Deep reinforced cognitive analytics algorithm (DRCAM): An advanced method to early detection of cognitive skill impairment using deep learning and reinforcement learning by Sunita Patil, Dr. Swetta Kukreja

“…Long-term cognitive state predictions are made by a Temporal Convolution Network (TCN). The MMT model achieves a classification accuracy of 90–92 %. …”
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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. …”
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