Research on APT groups malware classification based on TCN-GAN. by Daowei Chen, Hongsheng Yan

“…By improving and innovating the extraction methods for image features and disassembled instruction N-gram features of APT malware, and based on the Temporal Convolutional Network (TCN) model, the paper achieves high-accuracy classification and identification of APT malware. …”
Get full text

A hybrid power load forecasting model using BiStacking and TCN-GRU. by Jun Ma, Jishen Peng, Haotong Han, Liye Song, Hao Liu

“…Then, BiStacking is used for preliminary predictions, followed by a temporal convolutional network (TCN) enhanced by a gated recurrent unit (GRU) to produce the final predictions. …”
Get full text

Multidimensional time series classification with multiple attention mechanism by Chen Liu, Zihan Wei, Lixin Zhou, Ying Shao

“…This paper introduces attention mechanisms applied to the temporal dimension, graph attention mechanisms for inter-dimensional relationships within multidimensional data, and attention mechanisms applied between channels post-convolutional calculations. …”
Get full text

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation. by Bhavana Jonnalagadda, Stephen Becker

“…We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. …”
Get full text

A Large‐Scale Analysis of Pockets of Open Cells and Their Radiative Impact by D. Watson‐Parris, S. A. Sutherland, M. W. Christensen, R. Eastman, P. Stier

“…Abstract Pockets of open cells sometimes form within closed‐cell stratocumulus cloud decks but little is known about their statistical properties or prevalence. A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). …”
Get full text

Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss by Zhengpeng Yang, Suyu Yan, Chao Ming, Xiaoming Wang

“…Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). …”
Get full text

A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction by Dawei Wang, Jingwei Guo, Chunyang Zhang

“…This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. …”
Get full text

Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging by Arwa Alzughaibi, Adwan A. Alanazi, Mohammed Alshahrani, Ines Hilali Jaghdam, Abaker A. Hassaballa

“…Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. …”
Get full text

Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…The novel model can depict data decomposition and feature extraction from both cyber and physical domains. First, a sample convolution and interaction network is built to effectively capture temporal dependencies and sudden anomaly features in physical-side data. …”
Get full text

Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction by Caiyun Wang, Jirui Zhang, Jianing Wang, Yida Wu

“…The Attention-Based Convolutional Long Short-Term Memory (AC-LSTM) network is designed to capture nonlinear motion patterns by leveraging temporal attention mechanisms and convolutional layers while also estimating confidence levels via a signal-to-noise ratio (SNR)-based multitask learning approach. …”
Get full text

A hybrid cellular automaton model integrated with 3DCNN and LSTM for simulating land use/cover change by Wei Yang, Yu Zhang, Kun Hou, Xuejing Wang

“…To address this issue, we introduced a hybrid model integrating deep spatiotemporal networks and cellular automata, named DST-CA. This model uses a 3D Convolutional Neural Network (3DCNN) to capture local short-term spatiotemporal features and Long Short-Term Memory (LSTM) to extract long-term chronological featurereferences, thereby more comprehensively capturing the nonlinear spatiotemporal characteristics of LUCC. …”
Get full text

Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism by Qi Chen, Feng Zhang, Yin Wang, Qing Yu, Genfeng Lang, Lixiong Zeng

“…Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. …”
Get full text

A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…To identify the irregularities in the cardiac data pattern, a gated recurrent unit (GRU) classifier and a one-dimensional convolutional neural network (1D-CNN) are introduced. …”
Get full text

Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. …”
Get full text

Action Recognition in Real-World Ambient Assisted Living Environment by Vincent Gbouna Zakka, Zhuangzhuang Dai, Luis J. Manso

“…To address this challenge, this paper introduces the Robust and Efficient Temporal Convolution network (RE-TCN), which comprises three main elements: Adaptive Temporal Weighting (ATW), Depthwise Separable Convolutions (DSC), and data augmentation techniques. …”
Get full text

Multi-Frame Joint Detection Approach for Foreign Object Detection in Large-Volume Parenterals by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…To address these challenges, this paper proposes a multi-frame object detection framework based on spatiotemporal collaborative learning, incorporating three key innovations: a YOLO network optimized with deformable convolution, a differentiable cross-frame association module, and an uncertainty-aware feature fusion and re-identification module. …”
Get full text

NeuroTIS+: An Improved Method for Translation Initiation Site Prediction in Full-Length mRNA Sequence via Primary Structural Information by Wenqiu Xiao, Chao Wei

“…In this paper, under the framework of NeuroTIS, we propose its enhanced version, NeuroTIS+, which allows for more sophisticated codon label dependency modeling via temporal convolution and homogenous feature building through an adaptive grouping strategy. …”
Get full text

Through-wall Human Pose Reconstruction and Action Recognition Using Four-dimensional Imaging Radar by Rui ZHANG, Hanqin GONG, Ruiyuan SONG, Yadong LI, Zhi LU, Dongheng ZHANG, Yang HU, Yan CHEN

“…This network overcomes the limitations of mainstream deep learning libraries that currently lack 4D convolution capabilities, which hinders the effective use of multiframe three-Dimensional (3D) voxel spatiotemporal domain information. …”
Get full text

Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…Compared to traditional CNNs, which rely solely on spatial features, or recurrent neural networks like long short-term memory (LSTM) and gated recurrent units (GRUs), which primarily capture temporal dependencies, the proposed CNN-BiGRU network integrates both spatial and temporal features concurrently. …”
Get full text

Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…The depthwise separable convolutional neural network (DSCNN) minimizes parameter overhead while capturing spatial correlations across distributed grid nodes, followed by convolutional block attention modules (CBAM) that dynamically recalibrate channel and spatial features to amplify discriminative patterns. …”
Get full text