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Giant Ovarian Tumor Presenting as an Incarcerated Umbilical Hernia: A Case Report
Zulfikar Karabulut,Ozgur Aydin,Erdal Onur,Nilufer Yigit Celik,Gokhan Moray 대한의학회 2009 Journal of Korean medical science Vol.24 No.3
We report a rare case of a giant ovarian tumor presenting as an incarcerated umbilical hernia. A 61-yr-old woman was admitted to the hospital with severe abdominal pain, an umbilical mass, nausea and vomiting. On examination, a large, irreducible umbilical hernia was found. The woman underwent an urgent operation for a possible strangulated hernia. A large, multilocular tumor was found. The tumor was excised, and a total abdominal hysterectomy and bilateral salphingo-oophorectomy were performed. The woman was discharged 6 days after her admission. This is the first report of incarcerated umbilical hernia containing a giant ovarian tumor within the sac.
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A comparative assessment of bagging ensemble models for modeling concrete slump flow
Hacer Yumurtacı Aydogmus,Halil Ibrahim Erdal,Onur Karakurt,Yusuf S. Turkan,Hamit Erdal 사단법인 한국계산역학회 2015 Computers and Concrete, An International Journal Vol.16 No.5
In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.
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