Cross Attentive Multi-Cue Fusion for Skeleton-Based Sign Language Recognition by Ogulcan Ozdemir, Inci M. Baytas, Lale Akarun

“…We demonstrate how the proposed attention-based framework exposes distinct temporal patterns of visual cue representations extracted via Spatio-Temporal Graph Convolutional Network (ST-GCN) and exploits them for learning SL representations more effectively. …”
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Unraveling trends in schistosomiasis: deep learning insights into national control programs in China by Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu

“…METHODS We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. …”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

“…To address this issue, we propose a spatio-temporal interpolation model (CA-STIM) that integrates both external environmental dynamics and the intrinsic spatio-temporal evolution characteristics of island morphology using a convolutional neural network-long short-term memory network (CNN-LSTM) framework with a cross-attention mechanism and a weighted binary cross-entropy loss function. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Our approach leverages <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><mn>1</mn></mrow></semantics></math></inline-formula> convolutional kernels within a convolutional neural network (CNN) solver to approximate derivatives, enabling the discovery of interpretable equations that generalize across temporal–spatial domains. …”
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Explainable AI Meets Synthetic Data: A Deep Learning Framework for Detecting Network Intrusion in NextG Network Infrastructure by Md Junayed Hossain, Khorshed Alam, Md Fahad Monir, Md Mozammal Hoque, Tarem Ahmed

“…To address these challenges, we proposes an innovative NIDS framework tailored for NextG networks that combines Generative Adversarial Networks (GANs) with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. …”
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A Novel Spatio&#x2013;Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

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Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities by Wei Chen, Xinghua Lu, Wei Fan, Xiaochao Wang

“…For the pulse shaping system of the SG-II-up facility, we propose a U-shaped convolutional neural network that integrates multi-scale feature extraction capabilities, an attention mechanism and long short-term memory units, which effectively facilitates real-time denoising of diverse shaping pulses. …”
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A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Hence, this paper proposes a new data-driven method called STA-Conv-LSTM that combines convolutional long short-term memory (Conv-LSTM) and spatio-temporal attention (STA) to predict SSPs. …”
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Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model by Jianan Sun, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…In this paper, in order to solve this challenge, the Bi-graph Graph Convolutional Spatio-Temporal Feature Fusion Network (BGCSTFFN)-based model is introduced to capture complex spatio-temporal correlations. …”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. …”
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Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Motor imagery decoding network with multisubject dynamic transfer by Zhi Li, Mingai Li, Yufei Yang

“…Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. …”
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SMANet: A Model Combining SincNet, Multi-Branch Spatial&#x2014;Temporal CNN, and Attention Mechanism for Motor Imagery BCI by Danjie Wang, Qingguo Wei

“…We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. …”
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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

“…Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. …”
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An automatic modulation recognition network based on multi-channel lightweighting by WAN Jinhua, SHANG Junna, ZHANG Huadi

“…The gated recurrent unit-multi-head self-attention (GRU-MHSA) module multi-head self-attention (MHSA) based on time fading and gated recurrent unit (GRU). to further extract the temporal correlation. Signal features in spatial dimension and time dimension were extracted by the combination of separable convolution module and GRU-MHSA module. …”
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Attention-Based Hypergraph Neural Network: A Personalized Recommendation by Peihua Xu, Maoyuan Zhang

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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Second, our approach employs the dilated causal convolution to extend the receptive field and prolong the prediction time. …”
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Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers by Dingge Liang, Junliang Li, Junping Yin

“…However, situational data provided by tactical wargame systems present significant challenges: high redundancy across consecutive frames and extreme data sparsity, with units occupying only a small fraction of the overall map. Traditional convolutional neural networks (CNNs) struggle to extract meaningful patterns from such data. …”
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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…Therefore, a more widely applicable network is needed for sleep staging.MethodsThis paper introduces MSDC-SSNet, a novel deep learning network for automatic sleep stage classification. …”
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Mamba-STFM: A Mamba-Based Spatiotemporal Fusion Method for Remote Sensing Images by Qiyuan Zhang, Xiaodan Zhang, Chen Quan, Tong Zhao, Wei Huo, Yuanchen Huang

“…However, traditional methods are constrained by linear assumptions; generative adversarial networks suffer from mode collapse; convolutional neural networks struggle to capture global context; and Transformers are hard to scale due to quadratic computational complexity and high memory consumption. …”
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