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There are so many web data hidden behind so-called deep web and can only be accessed through query interfaces, and the data volume is increasing. We often need to fill forms over alternative interfaces in the same domain to select the best product or service, such as buying a book across various online book websites to choose the most affordable one. Integrating these web interfaces in the same domain...
Traditional classification algorithms often perform well when training and testing data are drawn from the identical distribution. However, in real applications, this condition may be not satisfied. Domain adaptation is an effective approach to deal with this problem. In this paper, we propose an efficient two-stage algorithm for domain adaptation. In the label transfer stage, we utilize training...
This paper presents a fast method for predicting inverter performance and evaluating power devices in electric vehicle drives during longtime driving cycles. The reliability of insulated gate bipolar transistors (IGBTs) is directly influenced by temperature and temperature variation cycles. Based on ANSYS SIMPLORER, a novel method of decoupling the device and inverter simulation is proposed to maintain...
In recent years real-time ultrasound (US) image fusion with pre-acquired 3D dataset has become widely used in both diagnosis and image-guided interventions. The accuracy of a US image fusion system heavily depends on the image registration method. However, the registration procedure of this application is inevitably interfered by possible outliers in the corresponding point pairs. This is either caused...
Mass spectrometry (MS) data has been widely analyzed for the detection of early stage cancers. Its potential for seeking proteomic biomarkers has received a great deal of attention in recent years. In the sparse representation classification (SRC) framework, a testing sample is represented as a sparse linear combination of training samples. The coefficient vector of representation is obtained by a...
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although numerous methods were proposed and achieved promising results in structural class prediction, some problems in using protein-sequence information have impeded the development. In this paper, a combined representation of protein-sequence information is proposed for prediction of protein structural class,...
Ovarian Carcinoma (OvCa) is the most lethal type of gynecological cancer. The studies show that about 90% patients could be saved if they are treated in the early stage. In this study, a novel biomarker selection approach is proposed which combines singular value decomposition (SVD) and Monte Carlo strategy to early OvCa detection. Other than supervised classification methods or differential expression...
Ovarian cancer (OvCa) has become one of the most lethal gynecological cancers in the world. The identification of ovarian cancer linked biomarkers will provide the basis of diagnoses and treatment. In this study, we proposed to combine singular value decomposition (SVD) and Monte Carlo method to analyze the OvCa data and predict the outcomes of samples. A supervised SVD was proposed to weight biomarkers...
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