Automatic Stylized Action Generation in Animation Using Deep Learning by Xiaoyu Su, Hyung-Gi Kim

“…Specifically, our method combines labeled and unlabeled animation data to train stylization models, employing spatiotemporal graph convolutional networks (ST-GCN) and StyleNet modules. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Results We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE) for multi-omics dimensionality reduction and a densely connected Graph Convolutional Network (GCN) for effective subtype classification. …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. …”
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Dual-Stream Spatially Aware Transformer for Remote Sensing Image Captioning by Haifeng Sima, Xiangtao Ding, JianLong Wang, Mingliang Xu

“…Specifically, DSAT introduces a <italic>dual-stream feature interaction</italic> module that extracts grid-level global features and region-level object features, and further enhances their respective spatial dependencies through multibranch convolution and a graph attention network. In addition, we design a spatially aware attention mechanism that encodes relative spatial relationships into the Transformer, allowing the model to better capture object distribution patterns and geometric relationships. …”
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Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds by Rui Zhang, Guanlong Huang, Fengpu Bao, Xin Guo

“…To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. …”
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Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area by Erzong Zheng, Yichen Zhang, Jiquan Zhang, Jiale Zhu, Jiahao Yan, Gexu Liu

“…Coupled with the Graph Convolutional Network (GCN) model, the study calculates the emergency evacuation time for each raster point, providing a comprehensive assessment of the region’s evacuation capacity. …”
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Lightweight Spatial&#x2013;Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification by Yi Liu, Yanjun Zhang, Yu Guo, Yunchao Li

“…Two-dimensional CNNs fail to effec-tively extract spatial and spectral information, and deploying three-dimensional CNNs on microprocessors is challenging as these net-works consume excessive resources. If graph convolutional networks (GCN) are adopted, most networks employ superpixel segmentation for HSI classification. …”
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DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model by Xiangkui Jiang, Binglong Ren, Qing Wu, Wuwei Wang, Hong Li

“…This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. …”
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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information by Kusal Debnath, Pratip Rana, Preetam Ghosh

“…We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). …”
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Morphological Estimation of Primary Branch Inclination Angles in Jujube Trees Based on Improved PointNet++ by Linyuan Shang, Fenfen Yan, Tianxin Teng, Junzhang Pan, Lei Zhou, Chao Xia, Chenlin Li, Mingdeng Shi, Chunjing Si, Rong Niu

“…Subsequently, the Chebyshev graph convolution module (CGCM) is integrated into PointNet++ to enhance its feature extraction capability, and the DBSCAN algorithm is optimized to perform instance segmentation of primary branch point clouds. …”
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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). …”
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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

“…To this end, this article proposes a joint data and knowledge‐driven disturbance localisation method. A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. …”
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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. …”
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SCATrans: semantic cross-attention transformer for drug–drug interaction predication through multimodal biomedical data by Shanwen Zhang, Changqing Yu, Chuanlei Zhang

“…In the model, BioBERT, Doc2Vec and graph convolutional network are utilized to embed the multimodal biomedical data into vector representation, BiGRU is adopted to capture contextual dependencies in both forward and backward directions, Cross-Attention is employed to integrate the extracted features and explicitly model dependencies between them, and a feature-joint classifier is adopted to implement DDI predication (DDIP). …”
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Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. by Hao Li, Linbing Chen

“…To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. …”
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Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations by Yinping Song

“…The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. …”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. …”
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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

“…Abstract The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of datasets. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. …”
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Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer by Mateusz Bielecki, Khadijeh Saednia, Fang-I Lu, Shely Kagan, Danny Vesprini, Katarzyna J. Jerzak, Roberto Salgado, Raffi Karshafian, William T. Tran

“…Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. …”
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