Multi-Modal AI for Multi-Label Retinal Disease Prediction Using OCT and Fundus Images: A Hybrid Approach by Amina Zedadra, Mahmoud Yassine Salah-Salah, Ouarda Zedadra, Antonio Guerrieri

“…The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. …”
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Fusion of Multimodal Spatio-Temporal Features and 3D Deformable Convolution Based on Sign Language Recognition in Sensor Networks by Qian Zhou, Hui Li, Weizhi Meng, Hua Dai, Tianyu Zhou, Guineng Zheng

“…In this paper, we firstly propose a Multi-Stream Spatio-Temporal Graph Convolutional Network (MSGCN) that relies on three modules: a decoupling graph convolutional network, a self-emphasizing temporal convolutional network, and a spatio-temporal joint attention module. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

Subjects: “…Multi-graph convolutional network…”
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MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction by Zengwei Xing, Shaoyou Yu, Shuzu Liao, Peng Wang, Bo Liao

Subjects: “…Graph convolutional networks…”
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DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering by Dongfen Ye, Jianqiang Zhou, Gang Huang

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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

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Research on Chinese Semantic Relation Extraction in Marine Engine Rooms Based on Multi-Feature Fusion by Xicai Liu, Zhengquan Wang, Fubo Wang

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Multi-Task Prediction Method Based on GGCN for Object Centric Event Logs by Li Ke, Fang Huan, Xu Yifei, Shao Chifeng

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Hypergraph Convolution Network Classification for Hyperspectral and LiDAR Data by Lei Wang, Shiwen Deng

“…Although deep learning methods based on convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs) have demonstrated promising results in fusing complementary multi-source data, existing methodologies demonstrate limited efficacy in capturing the intricate higher-order spatial–spectral dependencies among pixels. …”
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Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

“…In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. …”
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A spatio-temporal fusion-based approach for multi-dimensional classification of Parkinson’s disease progression using multi-modal dataset by Vinay Kukreja, Vandana Ahuja, Modafar Ati, Hariharan Shanmugasundaram, Murugaperumal Krishnamoorthy, Rishabh Sharma, Abhishek Bhattacherjee

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Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…In this paper, we propose a Multi Scale Spatial-Temporal Recurrent Graph Network (MSSTRG), focusing on local temporal, multi-scale temporal and dynamic spatial correlation. …”
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Graph-Based Feature Crossing to Enhance Recommender Systems by Congyu Cai, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou, Yuanguo Lin

“…Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. …”
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Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting by Vandana Singh, Sudip Kumar Sahana, Vandana Bhattacharjee

“…The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics. …”
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Cloud Computing Resource Scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network by Haichuan Sun, Liang Gu, Chenni Dong, Xin Ma, Zeyu Liu, Zhenxi Li

“…Based on graph convolutional neural networks, analyze the target load of cloud platforms, construct multi hop data transmission paths one by one, and perform deep level information load balancing; Establish a multiplexing information transmission model, correct the initial weights of graph convolutional neural networks, combine reverse transmission calculation methods, integrate and balance cloud computing resources, and confirm the optimal resource scheduling plan; Integrating class convolution and human-machine interaction attention mechanism, the value of the previous time series neural unit is transferred to the current neural unit, and the classification output sequence of knowledge graph relational data feature fragments is analyzed. …”
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An efficient graph attention framework enhances bladder cancer prediction by Taghreed S. Ibrahim, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

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Knowledge graph construction and talent competency prediction for human resource management by Bowen Yang, Zhixuan Shen

“…To address these challenges, we propose a hybrid model that integrates Graph Convolutional Networks (GCN), Reinforcement Learning (RL), and Deep Collaborative Filtering (DCF). …”
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VE-GCN: A Geography-Aware Approach for Polyline Simplification in Cartographic Generalization by Siqiong Chen, Anna Hu, Yongyang Xu, Haitao Wang, Zhong Xie

“…Polyline simplification is a critical process in cartographic generalization, but the existing methods often fall short in considering the overall geographic morphology or local edge and vertex information of polylines. To enhance the graph convolutional structure for capturing crucial geographic element features and simultaneously learning vertex and edge features within map polylines, this study introduces a joint vertex–edge feature graph convolutional network (VE-GCN). …”
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Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

“…Additionally, a multi-head graph attention layer is introduced to capture spatial correlation features among research topics. …”
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Node Classification Based on Kolmogorov-Arnold Networks by YUAN Lining, FENG Wengang, LIU Zhao

Subjects: “…graph convolutional networks; multi-layer perceptron; kolmogorov-arnold networks; contrastive learning; node classification…”
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