A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…Remote sensing change detection aims to accurately detect changes in buildings, roads, and other features in a pair of dual-temporal remote sensing images. In recent years, convolutional neural networks have achieved significant results in this task, but they lack the ability to model global features, leading to suboptimal performance in complex scenarios. …”
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Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot by Chang-Sik Son, Won-Seok Kang

“…The model adopts an asymmetric convolutional architecture composed of depthwise and pointwise layers to efficiently capture temporal and modality-specific features while significantly reducing the number of trainable parameters. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEG-based BCIs. …”
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Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…To address this, we propose a lightweight graph convolutional network (GCN) that integrates an improved Ghost module with multi-attention mechanisms, including a global attention mechanism (GAM) and a channel attention mechanism (CAM), to enhance spatial and temporal feature extraction. …”
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MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding by Huangtao Zhan, Xinhui Li, Xun Song, Zhao Lv, Ping Li

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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

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Precise PIV Measurement in Low SNR Environments Using a Multi-Task Convolutional Neural Network by Yichao Wang, Chenxi You, Di Peng, Pengyu Lv, Hongyuan Li

“…This study proposes PIV-RAFT-EN, an enhanced RAFT-based algorithm integrating image denoising, enhancement, and optical flow estimation via a Multi-Task Convolutional Neural Network (MTCNN). Evaluations on synthetic and real-world low-SNR data demonstrate its superior accuracy and efficiency. …”
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iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism by Qian Zhou, Jie Meng, Hao Luo

“…In this study, iPro-CSAF, a convolutional spiking neural network combined with spiking attention mechanism is designed for promoter recognition. …”
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A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction by Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang, Haifeng Song, Lunming Qin

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Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and Convolution Network (MACNet) Based on Surface Electromyography by Guofu Zhang, Banghua Yang, Peng Zan, Dingguo Zhang

“…Methods: This study proposes a multiple attention and convolution network (MACNet) for a three-level assessment of muscle fatigue based on sEMG. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

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ConvGRU: A Lightweight Intrusion Detection System for Vehicle Networks Based on Shallow CNN and GRU by Shaoqiang Wang, Jiahui Cheng, Yizhe Wang, Shutong Li, Lei Kang, Yinfei Dai

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GWSC-SegMamba: Gate Wavelet Spatial Convolution Enhanced State Space Model for Multi-Temporal Agricultural Land Segmentation by Yohanes Fridolin Hestrio, Aprinaldi Jasa Mantau, Wisnu Jatmiko

“…The study of multi-temporal satellite data for agricultural land segmentation faces significant computational challenges when processing extended temporal sequences, particularly due to CNNs’ limited receptive fields and Transformers’ quadratic complexity, since convolutional neural networks are constrained by local receptive fields, whereas Transformers experience quadratic complexity in their self-attention mechanisms. …”
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A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM by Xiaogang Yuan, Jianxin Wan, Dezhi An, Huan Pei

“…A Graph Convolutional Network (GCN) is employed to capture structural dependencies among nodes, while a Long Short-Term Memory (LSTM) network models the temporal dynamics of traffic behavior. …”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. …”
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A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting by Zain Ahmed, Mohsin Jamil, Ashraf Ali Khan

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MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification by Xuegang Xu, Quan Wang, Changyuan Wang, Yaxin Zhang

“…To overcome these shortcomings, we present MCAF-Net, a novel network architecture that employs temporal convolution modules to extract channel-specific features from each input signal and introduces a dynamic gated multi-head cross-channel attention mechanism (MCAF) to effectively model the interdependencies between different physiological channels. …”
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Temporal Segment Method in Sign Word Recognition Using a Pretrained CNN-LSTM Network by Seungju Lee, Irina Polyakova

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User-cooperative dynamic resource allocation for backscatter-aided wireless-powered MEC network by Huaiwen He, Chenghao Zhou, Feng Huang, Hong Shen, Yihong Yang, Shangsong Liang, Xinyuan Jin

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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…Spatiotemporal fusion offers an effective and economical solution to achieve high spatial and temporal resolution simultaneously. This article introduces a new generative adversarial network (GAN) spatiotemporal fusion model based on multiscale convolution and attention mechanism for remote sensing images (MSCAM-GAN), to generate high-resolution fused images. …”
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