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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shengqiang Chi Tianshu Zhou Weiwei Zhu Xueyao Li Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Computational Methods for Inferring and Reconstructing Gene Regulatory Networks

Computational Methods for Inferring and Reconstructing Gene ...

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作者： Xianglu Zhou Haiyang Jia College of Computer Science and Technology Jilin University College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin University

Gene regulatory network(GRN) describes dynamic and complex gene interactions that determine the functions of ***,understanding of GRNs has played a great role in cancer treatment,drug design,and gene ***,the GRN study suffers from a lack of experiment measurement data and the complexity of the *** approaches have evolved in recent years to promote the study of *** this review,we summarized popular Computational approaches for GRN *** started with traditional bioinformatic methods,such as Bayesian networks and mutual information ***,we introduced how today's hot technology in the computer field-machine learning benefited GRN ***-based approaches and other machine-learning methods are elaborated on in this *** discussed not only the advantages and progression brought by various methods but also the drawbacks and limitations,such as the accuracy and robustness of GRN *** expect to inspire readers for improved GRN study approaches via our introduction to this field.

关键词： Gene Regulatory Network Bioinformatics Machine Learning

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Social Group Query Based on Multi-Fuzzy-Constrained Strong Simulation

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ACM Transactions on knowledge Discovery from data 2021年第3期16卷 1–27页

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) and Hefei University of Technology Macquarie University NSW Australia Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Mininglamp Academy of Sciences Mininglamp Technology

Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interactions between members of the group. Generally, Graph Pattern Matching (GPM) technique is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this article, first, a social group query problem that needs to consider the trust between members of the group is proposed. Then, to solve this problem, a multi-fuzzy-constrained strong simulation matching model is proposed based on multi-constrained simulation, and a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence is proposed. Aiming at the inefficiency of the NTSS algorithm when pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.

关键词： Graph pattern matching contextual social network multi-fuzzy-constrained strong simulation social group query

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The Investigation and Prediction of Voriconazole-Associated Hepatotoxicity under Therapeutic Drug Monitoring*

The Investigation and Prediction of Voriconazole-Associated ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Ma Yu Wang Shuang Ma Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Voriconazole is commonly used as the first-line agent to treat invasive fungal infections (IFIs), but the induction of hepatotoxicity limits its use. To improve the treatment outcomes and minimize toxicity, doctors often administer Therapeutic Drug Monitoring (TDM) to patients receiving voriconazole treatment. Here, we conducted a real-world clinical investigation of voriconazole-treated patients and found significant differences between the TDM (n=318) and non-TDM cohort (n=6,379), and such incidence of hepatotoxicity showed 10.6% in the non-TDM cohort, compared with 21.5% in the TDM cohort. Based on our previous investigation, we presented and compared several machine learning models (including AdaBoost, decision tree, GBDT, logistic regression, neural networks, and random forest) for the early warning of voriconazole-associated hepatoxicity. Through the five-fold cross validation, the logistic model outperformed other models with a mean AUC of 0.7933±0.0934. Our findings offer important insights into the safe and effective application of voriconazole.

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Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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Chinese Journal of Electronics 2020年第6期29卷 1153-1164页

作者： XU Qianyi QIN Guihe SUN Minghui YAN Jie JIANG Huiming ZHANG Zhonghan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Jilin University College of Software Jilin University

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional ***,video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture *** that if the displays are more relevant,the results will more accurate,so ResNext,a kind of compact and efficient neural network,is firstly used as feature extractor,then an improved weighted frame unification method is adopted to obtain the key frame samples,finally the Discriminant correlation analysis(DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch(FEB) on static *** public dataset named Depth based gesture recognition database(DGRD) is used in this paper,but the dataset is a little small and the class distribution is largely imbalance,and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training *** order to conquer the disadvantages of limited dataset,a special loss function scheme combining the soft max loss and dice loss is *** of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

关键词： feature extraction gesture recognition learning (artificial intelligence) neural nets Depth based gesture recognition database Feature embedding branch dynamic data static data key frame samples improved weighted frame unification method feature extractor efficient neural network compact network uncorrelated gesture factors video gestures recognition depth videos automotive interfaces Feature fusion based hand gesture recognition method Weighted frame unification Discriminant correlation analysis (DCA) Feature embedding branch (FEB) Gesture recognition Loss function

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From knowledge Graph Development to Serving Industrial knowledge Automation:A Review 41

From Knowledge Graph Development to Serving Industrial Knowl...

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第41届中国控制会议

作者： Guangxuan Song Dongmei Fu Dawei Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing National Materials Corrosion and Protection Data Center University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

knowledge is essential for the management,scheduling,and operation of all industrial *** automation refers to the automation of knowledge-based work,which is a disruptive technology of the *** in the era of big data,KG inherits the good interpretability of traditional symbolism system approaches and becomes one of the feasible and important directions to realize industrial knowledge *** paper begins with the origin of KGs,introduces the characteristics of KGs,and reviews the state of the art of KG through the cases of KG constructed and applied in general and vertical *** paper also focuses on the research progress of KG applications in industry,by identifying the current challenges of KG in industrial applications,and highlighting the substantial potential of KG to serve knowledge automation.

关键词： knowledge knowledge Graph(KG) knowledge Automation Symbolism Industry

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Predicting atmospheric corrosion of low-alloy steels based on environmental features extracted by improved autoencoder

Predicting atmospheric corrosion of low-alloy steels based o...

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第35届中国控制与决策会议

作者： Tao Yang Guanghua Zhao Jialin Wu Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing School of Automation and Electrical Engineering University of Science and Technology Beijing

Material corrosion can cause substantial economic losses and pose serious *** way to reduce those damages is to predict available data before catastrophic ***,because of the small sample size of the corrosion data,the generalization of the corrosion prediction models remains a *** study adopts an autoencoder to determine the intrinsic features of the natural atmospheric environment to which the materials were *** training process of the autoencoder was designed to ensure that the features were consistent between natural atmospheric environmental data and the averaged environmental data within small-sample corrosion *** addition,the k-nearest method was used to find a set of extracted features for the corrosion rate ***,the amount of corrosion data *** this study,a neural network was adopted for augmented corrosion data to predict corrosion *** experimental results showed that the proposed method not only had a satisfactory training accuracy but also a strong generalization ability.

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CiRLExplainer: Causality-Inspired Explainer for Graph Neural Networks via Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 9970-9984页

作者： Wenya Hu Jia Wu Quan Qian School of Computer Engineering and Science Shanghai University Shanghai China School of Computing Macquarie University Sydney NSW Australia School of Computer Engineering and Science the Center of Materials Informatics and Data Science Materials Genome Institute the Key Laboratory of Silicate Cultural Relics Conservation Ministry of Education and Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

In this article, we propose a new graph neural network (GNN) explainability model, CiRLExplainer, which elucidates GNN predictions from a causal attribution perspective. Initially, a causal graph is constructed to analyze the causal relationships between the graph structure and GNN predicted values, identifying node attributes as confounding factors between the two. Subsequently, a backdoor adjustment strategy is employed to circumvent these confounders. Additionally, since the edges within the graph structure are not independent, reinforcement learning is incorporated. Through a sequential selection process, each step evaluates the combined effects of an edge and the previous structure to generate an explanatory subgraph. Specifically, a policy network predicts the probability of each candidate edge being selected and adds a new edge through sampling. The causal effect of this action is quantified as a reward, reflecting the interactivity among edges. By maximizing the policy gradient during training, the reward stream of the edge sequence is optimized. The CiRLExplainer is versatile and can be applied to any GNN model. A series of experiments was conducted, including accuracy (ACC) analysis of the explanation results, visualization of the explanatory subgraph, and ablation studies considering node attributes as confounding factors. The experimental results demonstrate that our model not only outperforms current state-of-the-art explanation techniques, but also provides precise semantic explanations from a causal perspective. Additionally, the experiments validate the rationale for considering node attributes as confounding factors, thereby enhancing the explanatory power and ACC of the model. Notably, across different datasets, our explainer achieved improvements over the best baseline models in the ACC-area under the curve (AUC) metrics by 5.89%, 5.69%, and 4.87%, respectively.

关键词： Predictive models Graph neural networks Reinforcement learning Accuracy Training Prediction algorithms Analytical models Sustainable development Focusing data science

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Wild Mushroom Image Segmentation Algorithm Based on Improved DeepLabV3+

Wild Mushroom Image Segmentation Algorithm Based on Improved...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Fangrong Zhu Benliang Xie Kaixuan Zhao Lin Qin Tingchi Wen College of Big Data and Information Engineering Guizhou University Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China The Engineering and Research Center of Southwest Bio-Pharmaceutical Resource Guiyang China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Wild mushrooms in China are diverse and widely distributed, particularly in southwestern regions like Yunnan and Guizhou, where many inedible species pose a risk of poisoning upon accidental ingestion. To mitigate these incidents, researchers are increasingly focused on species recognition. However, the natural growth environments of wild mushrooms introduce significant background disturbance, complicating accurate identification. Consequently, separating the background from the image is crucial for enhancing recognition accuracy. To solve the above problems, this paper takes the common wild mushrooms in Guizhou province as the research object and proposes a deep learning network based on the improved DeepLabV3+ for the background segmentation task of wild mushroom images. Firstly, the network Xception backbone feature extraction network of the original DeepLabV3+ is replaced with MobileNetV2 network, which makes the network more lightweight and suitable for deployment into mobile devices. Secondly, the Feature Pyramid Networks feature fusion branch is proposed to realize the fusion of different levels of feature maps, so that the network can extract richer wild mushroom features and improve the wild mushroom image segmentation accuracy. And then, a multi-layer cascade superposition is used to replace the original Atrous Spatial Pyramid Pooling module with Dense Atrous Spatial Pyramid Pooling as an enhanced feature extraction network to increase the perceptual field of the network and obtain the multi-scale information of the wild mushroom image. Finally, the ResNet-18 network was used to identify the segmented images. Experimental results show that the improved DeepLabV3+ has better segmentation performance. The average segmentation accuracy of the algorithm is 96.81%, the mean Intersection over Union is 92.36%, and the average accuracy after ResNet-18 recognition reaches 93.8%. In the comparison experiments, our segmentation algorithm has a higher segmentation acc

关键词： Deep learning Computers Image segmentation Accuracy Image recognition Fuses Feature extraction Mobile handsets data mining Artificial intelligence

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