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Sclerosing angiomatoid nodular transformation of the spleen in children:a two-case report of laparoscopic total or partial splenectomy and a literature review
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作者 Salahoudine Idrissa Pierre-Yves Rabattu +2 位作者 Eva Sole Cruz Yohann Robert Christian Piolat 《World Journal of Pediatric Surgery》 2020年第4期9-12,共4页
Sclerosing angiomatoid nodular transfor-mation(SANT)is a rare splenic lesion first described by Martel et al in 2004.^(1)As reported,SANT is a benign vascular lesion of the red pulp of the spleen,presenting under the ... Sclerosing angiomatoid nodular transfor-mation(SANT)is a rare splenic lesion first described by Martel et al in 2004.^(1)As reported,SANT is a benign vascular lesion of the red pulp of the spleen,presenting under the microscope as angiomatoid nodules in a fibrosclerotic stroma. 展开更多
关键词 SPLEEN ANGIOMA TRANSFORMATION
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Predictive modeling for wine authenticity using a machine learning approach
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作者 Nattane Luíza da Costa Leonardo A.Valentin +1 位作者 Inar Alves Castro Rommel Melgaço Barbos 《Artificial Intelligence in Agriculture》 2021年第1期157-162,共6页
The purpose of this paper is to classify wines from 4 different countries in South America.Each class of wines is formed by samples considered by experts as representatives of the following commercial categories:“Arg... The purpose of this paper is to classify wines from 4 different countries in South America.Each class of wines is formed by samples considered by experts as representatives of the following commercial categories:“Argentinean Malbec(AM)”,“Brazilian Merlot(BM)”,“Uruguayan Tannat(UT)”and“Chilean Carménère(CC)”.The 83 samples collected were analyzed according to their composition of volatiles,semi-volatiles and phenolic compounds.We built a decision system for classification based on support vector machines(SVM),along with Correlation-based Feature selection(CFS),and RandomForest Importance(RFI),whichmeasures the relative importance of the input variables.First,we use CFS to select a subset of variables among 190 chemical compounds.Thirteen chemicals were selected as correlated to the category and uncorrelated with each other.Afterwards,these chemical compounds were organized according to the importance ranking given by the RFI and classified with SVM.The study clearly indicated that SVMin combination with feature selection methodswas able to identify the most important chemicals to classify the wine samples.Among the compounds identified in the wine samples,the variable subset defined by the feature selection methods,which were catechin,gallic,octanoic acid,myricetin,caffeic,isobutanol,resveratrol,kaempferol,and ORAC,were able to achieve an accuracy of 93.97%in classifying the commercial categories. 展开更多
关键词 Wine classification Support vector machines Feature selection South American wines Machine learning
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