Joint QoS prediction for Web services based on deep fusion of features by Jianxun LIU, Linghang DING, Guosheng KANG, Buqing CAO, Yong XIAO

“…In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First, QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping, and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector.Finally, the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).…”
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Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy by Fahad Mostafa, Hafiz Khan, Fardous Farhana, Md Ariful Haque Miah

“…Performing a 5-fold cross-validation with 100 repetitions, the ensemble of MobileNetV2 and a Graph Convolution Network exhibits a validation accuracy of 82.5%, significantly outperforming MobileNetV2 alone, which shows a 5-fold validation accuracy of 77.4%. …”
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Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). …”
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Community Detection Framework Using Deep Learning in Social Media Analysis by Ao Shen, Kam-Pui Chow

“…The proposed end-to-end community detection framework is the implementation of Graph Convolution Network and can display the social network topology, locate the core members of the community, and show the connections between users. …”
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A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB by Hailong Zhu, Yawen Xie, Wei He, Chao Sun, Kaili Zhu, Guohui Zhou, Ning Ma

“…First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. ResultsThe graph convolutional network model showed the highest prediction accuracy with an area under the receiver operating characteristic curve of 0.831, accuracy of 75%, sensitivity of 52.12%, and specificity of 90.25%. …”
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Lightweighting the prediction process of urban states with parameter sharing and dilated operations by Peixiao Wang, Haolong Yang, Hengcai Zhang, Shifen Cheng, Feng Lu, Zeqiang Chen

“…In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. …”
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Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction by Guangzhong Huang, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, Xing Dong

“…Then, a model combining Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) networks is proposed for bearing degradation stage classification. …”
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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. …”
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Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data by He Bing, Xu Zhifeng, Xu Yangjie, Hu Jinxing, Ma Zhanwu

“…Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.…”
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A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems by Gang Luo, Haoxuan Kuang, Dongran Zhang, Kunxiang Deng, Jun Li

“…Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. …”
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Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method by Yujie Yuan, Yantao Wang, Chun Sing Lai

“…The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. …”
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Study of crystal property prediction based on dual attention mechanism and transfer learning by Yongyin Xu, Wei Deng, Jiaxin Zheng

“…To avoid the step of manual feature engineering when predicting crystal properties, a graph convolutional neural network based on the dual attention mechanism, named DA-CGCNN, is proposed. …”
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RoPT: Route-Planning Model with Transformer by Zuyun Xiong, Yan Wang, Yuxuan Tian, Lijuan Liu, Shunzhi Zhu

“…This model is based on the fusion of Graph Convolutional Networks (GCNs) and a Transformer, which uses GCNs for capturing complex spatial dependencies between the current intersection and the destination in a road network. …”
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Dance Movement Recognition Based on Feature Expression and Attribute Mining by Xianfeng Zhai

“…Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high-performance dance motion recognition is realized. …”
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Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence by Na Song, Jing Yang, Xuemei Fu, Xiangli Yang, Ying Xie, Shiping Wang

“…Subsequently, a scalable graph convolution fusion module combines these features. …”
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Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis by Zehua Yu, Xianwei Zheng, Zhulun Yang, Bowen Lu, Xutao Li, Maxian Fu

“…Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. …”
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Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature by Lifang Fu, Shuai Liu

“…To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). …”
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