Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection by Meng Jiao, Changyue Song, Xiaochen Xian, Shihao Yang, Feng Liu

“…This framework utilizes three 1D convolutional neural network (CNN) blocks to extract features from R-R intervals and R-peak amplitudes using segments of varying lengths. …”
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An intrusion detection method based on depthwise separable convolution and attention mechanism by Zhifei ZHANG, Feng LIU, Yiyang GE, Shuo LI, Yu ZHANG, Ke XIONG

“…In order to improve the accuracy of multi-classification in network intrusion detection, an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-term memory networks, the spatial and temporal features of network traffic data can be better extracted.A mixed-domain attention mechanism was introduced to enhance the detection performance.To solve the problem of low detection rate in some samples, a data balance strategy based on the combination of the variational auto-encoder (VAE) the generative adversarial network (GAN) and was designed, which can effectively cope with imbalanced datasets and improve the adaptability of the proposed detection method.The experimental results show that the proposed method is able to achieve 99.80%, 99.32%, and 83.87% accuracy on the CICIDS-2017, NSL-KDD and UNSW-NB15 datasets, which is improved by 0.6%, 0.5%, and 2.3%, respectively.…”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

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TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting by Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang

“…To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. …”
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GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer by Xueshen Cui, Jikai Zhang, Yihao He, Zhixing Wang, Wentao Zhao

“…This paper proposes an innovative Spatio-Temporal Graph Convolutional Network: GCN-Former, which aims to enhance model performance in skeleton-based action recognition tasks. …”
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Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network by Yilin Hu, Junling Ran, Rui Qiao, Jiayang Xu, Congming Tan, Liangliang Hu, Yin Tian

“…We employed a graph convolutional neural network model to identify individuals with ADHD. …”
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GTN-GCN: Real-Time Traffic Forecasting Using Graph Convolutional Network and Transformer by Sadia Naj Jinia, Sumaiya Binte Azad, Rima Akter, Taivan Reza Dipto, Md. Khaliluzzaman

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PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport by Eunkyeong Lee, Hosik Choi, Do-Gyeong Kim

“…In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. …”
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MHCAGAT: A Meta Hybrid Convolution Attention Network for Urban Traffic Flow Prediction by Yu Zhan, Suzi Iryanti Fadilah, Azizul Rahman Mohd Shariff

“…To address these issues, a novel traffic prediction model is proposed, Meta Hybrid Convolution Attention Graph Attention Network (MHCAGAT). …”
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Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen

“…Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. …”
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Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks by Masoud Geravanchizadeh, Amir Shaygan Asl, Sebelan Danishvar

“…This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). …”
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2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction by Cheng Fang, Li Wang

“…Our proposed model employs a two-stage architecture. Initially, temporal characteristics of traffic flow between OD pairs are captured using a 1D convolution neural network (1D-CNNs). …”
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PhyICNet: Physics-informed interactive learning convolutional recurrent network for spatiotemporal dynamics by Ruohan Cao, Jin Su, Jinqian Feng, Qin Guo

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Bearing Life Prediction Method Based on Parallel Multichannel Recurrent Convolutional Neural Network by Jianmin Zhou, Sen Gao, Jiahui Li, Wenhao Xiong

“…To extract the time-series characteristics of the original bearing signals and predict the remaining useful life (RUL) more effectively, a parallel multichannel recurrent convolutional neural network (PMCRCNN) is proposed for the prediction of RUL. …”
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Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi, Yoshifumi Okada

“…To address these limitations, this research proposes a convolutional neural network (CNN) model for arrhythmia classification that incorporates two specialized modules. …”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. …”
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Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery by Seth Goodman, Ariel BenYishay, Daniel Runfola

“…Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. …”
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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

“…Therefore, in this paper, we propose a novel approach to skeleton-based action recognition named Multi-stage Adaptive Graph Convolution Network (MSA-GCN). It consists of two modules: Multi-stage Adaptive Graph Convolution (MSA-GC) and Temporal Multi-Scale Transformer (TMST). …”
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A Lightweight Forward–Backward Independent Temporal-Aware Causal Network for Speech Emotion Recognition by Sijia Fei, Qiang Feng, Fei Gao

“…Furthermore, the backward temporal-aware module uses dilated causal convolutions to learn backward information and weighted fusion of multi-level temporal features to enhance the perception of backward emotion changes. …”
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ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection by Bin Li, Jie Li, Mingyu Jia

“…In this paper, we propose ADFCNN-BiLSTM, a novel deep neural network for network intrusion detection. ADFCNN-BiLSTM uses deformable convolution and an attention mechanism to adaptively extract the spatial features of network traffic data, and it pays attention to the important features from both channel and spatial perspectives. …”
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