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Research on EHR Storage and Sharing Scheme Based on Consortium Blockchain 基于联盟区块链的电子病历存储与共享方案研究
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3489014
Haohao Li, Jianhua Liu
{"title":"Research on EHR Storage and Sharing Scheme Based on Consortium Blockchain","authors":"Haohao Li, Jianhua Liu","doi":"10.1145/3488933.3489014","DOIUrl":"https://doi.org/10.1145/3488933.3489014","url":null,"abstract":"In order to realize the safe storage and sharing of medical data, this paper proposes a medical data storage model based on blockchain. Firstly, all medical institutions are divided by regions, and multiple medical institutions alliances are generated, and the consortium blockchain is formed respectively. The medical metadata is stored on the blockchain, and the medical data block is stored in InterPlanetary File System (IPFS). In each consortium blockchain, a small number of authoritative organizations are selected to form a main chain, which is responsible for the cross chain query function among the consortium blockchains. In the query process, the proxy re encryption technology is used to realize the user's access to the patient's medical information. The experimental results show that the model ensures the tamper proof and security of medical data, It realizes the safe sharing of medical data and improves the query efficiency of medical data, When 78.5% of the total number of queries in the same alliance, the cross chain scheme is better than the ordinary scheme.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125705558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybridizing Extremely Randomized Trees with Bootstrap Aggregation for Crime Prediction 基于自举聚合的极端随机树犯罪预测
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488972
Sphamandla May, Omowunmi E. Isafiade, Olasupo O. Ajayi
{"title":"Hybridizing Extremely Randomized Trees with Bootstrap Aggregation for Crime Prediction","authors":"Sphamandla May, Omowunmi E. Isafiade, Olasupo O. Ajayi","doi":"10.1145/3488933.3488972","DOIUrl":"https://doi.org/10.1145/3488933.3488972","url":null,"abstract":"The prevalence of crime continues to be a major challenge in communities and societies around the globe. This justifies the relevance of studies on crime prevention. As a preventive strategy, crime prediction can help deter known crimes before they occur. Machine learning algorithms have been vastly applied to predictive tasks, particularly Decision Trees (DT), among others. Despite their good performance, DT suffers from bias and variance problems. While DT has these problems, there are other algorithms, which are variants of DT that are more viable. The two algorithms known to reduce bias and variance are Random Forest (RF) and Extremely Randomized Trees (ERT). In this work, we proposed a hybrid algorithm which utilizes the best attributes from both RF and ERT, which are bootstrap aggregation and random features selection. We then compared our hybrid algorithm with RF and ERT. Obtained results show that our hybrid algorithm performed better in terms of prediction accuracy, and computational complexity.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121768696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Text Classification Method Based on BiGRU-Attention and CNN Hybrid Model 基于BiGRU-Attention和CNN混合模型的文本分类方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3488933.3488970
Jinbao Teng, W. Kong, Yidan Chang, Qiaoxin Tian, Chenyuan Shi, Long Li
{"title":"Text Classification Method Based on BiGRU-Attention and CNN Hybrid Model","authors":"Jinbao Teng, W. Kong, Yidan Chang, Qiaoxin Tian, Chenyuan Shi, Long Li","doi":"10.1145/3488933.3488970","DOIUrl":"https://doi.org/10.1145/3488933.3488970","url":null,"abstract":"Aiming at the problem that traditional Gated Recurrent Unit (GRU) and Convolution Neural Network (CNN) can not reflect the importance of each word in the text when extracting features, a text classification method based on BiGRU Attention and CNN is proposed. Firstly, CNN was used to extract the local information of the text, and then the full-text semantics was integrated. Secondly, BiGRU was used to extract the context features of the text, and attention mechanism was used after BiGRU to extract the attention score of the output information. Finally, the output of BiGRU attention was fused with the output of CNN to realize the effective extraction of text features and focused on the important content words. Experimental results on three public datasets showed that the proposed model was better than GRU, CNN and other models, which can effectively improve the effect of text classification.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115179674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
News-driven financial warning based on label information attention 基于标签信息关注的新闻驱动型财务预警
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3573942.3574010
Zhilin Ren, Long Chen, Xiaohua Huang, Wenjing Wang, Cai Xu, Wei Zhao, Ziyu Guan
{"title":"News-driven financial warning based on label information attention","authors":"Zhilin Ren, Long Chen, Xiaohua Huang, Wenjing Wang, Cai Xu, Wei Zhao, Ziyu Guan","doi":"10.1145/3573942.3574010","DOIUrl":"https://doi.org/10.1145/3573942.3574010","url":null,"abstract":"Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock Label Information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 datasets. CCS CONCEPTS • Computing methodologies • Artificial intelligence • Natural language processing","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
FF-KGAT: Feature Fusion Based Knowledge Graph Attention Network for Recommendation FF-KGAT:基于特征融合的推荐知识图关注网络
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3573942.3574050
Tao Li, Xiaoge Li, Chaodong Wang, Xianliang Li, Shuai Gao, Dan Han
{"title":"FF-KGAT: Feature Fusion Based Knowledge Graph Attention Network for Recommendation","authors":"Tao Li, Xiaoge Li, Chaodong Wang, Xianliang Li, Shuai Gao, Dan Han","doi":"10.1145/3573942.3574050","DOIUrl":"https://doi.org/10.1145/3573942.3574050","url":null,"abstract":"It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention Network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115295388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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