{"title":"基于机器学习(kNN-QSPR)方法的折射率数学建模","authors":"Rifkat Davronov, B. Rasulev, F. Adilova","doi":"10.1109/AICT50176.2020.9368648","DOIUrl":null,"url":null,"abstract":"In the present work, we created new software in R system, strictly following to the kNN-QSPR approach, used PaDEL and Dragon Descriptor software for generation of initial set of descriptors and developed the program for automatic scaling. Based on this new software, we propose models of quantitative relationships between refractive indices (RI) and polymer structure. An important conclusion of this study is confirmed by the correct interpretation of the constructed models.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mathematical modeling of refractive index based on machine learning (kNN-QSPR) method\",\"authors\":\"Rifkat Davronov, B. Rasulev, F. Adilova\",\"doi\":\"10.1109/AICT50176.2020.9368648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the present work, we created new software in R system, strictly following to the kNN-QSPR approach, used PaDEL and Dragon Descriptor software for generation of initial set of descriptors and developed the program for automatic scaling. Based on this new software, we propose models of quantitative relationships between refractive indices (RI) and polymer structure. An important conclusion of this study is confirmed by the correct interpretation of the constructed models.\",\"PeriodicalId\":136491,\"journal\":{\"name\":\"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICT50176.2020.9368648\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICT50176.2020.9368648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mathematical modeling of refractive index based on machine learning (kNN-QSPR) method
In the present work, we created new software in R system, strictly following to the kNN-QSPR approach, used PaDEL and Dragon Descriptor software for generation of initial set of descriptors and developed the program for automatic scaling. Based on this new software, we propose models of quantitative relationships between refractive indices (RI) and polymer structure. An important conclusion of this study is confirmed by the correct interpretation of the constructed models.
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