An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features by Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau

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BTCP: Binary Temporal Convolutional Network-Based Data Prefetcher for Low Inference Latency and Storage Overhead by Chang Ho Ryu, Tae Hee Han

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A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment by Arindam Kishor Biswas, Md Shariful Alam Bhuiyan, Md Nazmul Hossain Mir, Ashifur Rahman, M. F. Mridha, Md Rashedul Islam, Yutaka Watanobe

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TSTA-GCN: trend spatio-temporal traffic flow prediction using adaptive graph convolution network by Xinlu Zong, Jiawei Guo, Fucai Liu, Fan Yu

“…A trend spatio-temporal adaptive graph convolution network (TSTA-GCN) model for metro passenger flow prediction is presented in this paper. …”
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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, Muhammad Sheraz, Teong Chee Chuah

Subjects: “…Temporal convolutional networks (TCNs)…”
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LoadSeer: Exploiting Tensor Graph Convolutional Network for Power Load Forecasting With Spatio-Temporal Characteristics by Jiahao Zhang, Bin Yu, Hanbin Lai, Lin Liu, Jinghui Zhou, Fengliang Lou, Yili Ni, Yan Peng, Ziheng Yu

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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. …”
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Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

“…Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. …”
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SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks. by Safaeid Hossain Arib, Rabeya Akter, Sejuti Rahman, Shafin Rahman

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Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram by Yohanssen Pratama, Kazuya Kitano, Takahiro Kushida, Yuki Fujimura, Takuya Funatomi, Yasuhiro Mukaigawa

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Spike-Driven Channel-Temporal Attention Network with Multi-Scale Convolution for Energy-Efficient Bearing Fault Detection by JinGyo Lim, Seong-Eun Kim

“…To address these demanding requirements, we propose the Spike Convolutional Attention Network (SpikeCAN), a novel spike-driven neural architecture tailored explicitly for real-time industrial diagnostics. …”
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Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis by Ling Wu, Boen Li, Kun Guo, Qishan Zhang

“…To address the above issues, this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies (GSTGCN). …”
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A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects by Rui Li, Xuanwen Yang, Jun Lou, Junsong Zhang

“…To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). …”
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Transient Stability Assessment of Power Systems Built upon Attention-Based Spatial–Temporal Graph Convolutional Networks by Yu Nan, Weiping Niu, Yong Chang, Zhenzhen Kong, Huichao Zhao

“…This paper proposes a TSA method built upon an Attention-Based Spatial–Temporal Graph Convolutional Network (ASTGCN) model. …”
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Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model by Keru Xia, Qi Li, Luyuan Han, Zhaohui Ren, Hengfa Luo

“…In order to alleviate the above problems, this study proposes a residual life prediction method of rolling bearings based on a multi-scale improved temporal convolutional network (MITCN) model. It is used to solve problems such as the low accuracy of bearing life prediction and the difficulty of the temporal convolutional network (TCN) model to capture the correlation of each time series. …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sequence-to-sequence (seq-to-seq) classification without using the sliding window technique. …”
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A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition by Farhad Mortezapour Shiri, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon

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Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting by Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang, Geli Ou’er

“…Therefore, a novel grid partition-based dynamic spatial–temporal graph convolutional network was developed in this study to capture correlations within a large-scale traffic network. …”
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A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces by Qiancheng Hao, Wenjing Liu, Wenze Gao, Xianpeng Wang

“…The proposed MSF-CNN model extracts temporal features at two distinct scales. The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. …”
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