M. Blagojevic, Milos D. Radovic, M. Radovic, N. Filipovic
{"title":"基于神经网络的动脉壁最大剪应力预测方法","authors":"M. Blagojevic, Milos D. Radovic, M. Radovic, N. Filipovic","doi":"10.1109/BIBE.2015.7367713","DOIUrl":null,"url":null,"abstract":"This paper describes the use of artificial neural networks in predicting value and position maximal wall shear stress in aneurysm. For the purpose of neural network training, back propagation algorithm was used. Input data in the network are geometric parameters of aneurysm model. Obtained results indicate the possibility of a successful application of neural networks in the problems of predicting certain parameters of arteries. Future work relates to the creation of a web-based application that allows users to display the results.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural network based approach for predicting maximal wall shear stress in the artery\",\"authors\":\"M. Blagojevic, Milos D. Radovic, M. Radovic, N. Filipovic\",\"doi\":\"10.1109/BIBE.2015.7367713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes the use of artificial neural networks in predicting value and position maximal wall shear stress in aneurysm. For the purpose of neural network training, back propagation algorithm was used. Input data in the network are geometric parameters of aneurysm model. Obtained results indicate the possibility of a successful application of neural networks in the problems of predicting certain parameters of arteries. Future work relates to the creation of a web-based application that allows users to display the results.\",\"PeriodicalId\":422807,\"journal\":{\"name\":\"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2015.7367713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2015.7367713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural network based approach for predicting maximal wall shear stress in the artery
This paper describes the use of artificial neural networks in predicting value and position maximal wall shear stress in aneurysm. For the purpose of neural network training, back propagation algorithm was used. Input data in the network are geometric parameters of aneurysm model. Obtained results indicate the possibility of a successful application of neural networks in the problems of predicting certain parameters of arteries. Future work relates to the creation of a web-based application that allows users to display the results.