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In this paper, multilayer perceptron neural networks (MLP neural networks) were used to predict the monkey skull's acoustic parameters (density and sound velocity) for transcranial focused ultrasound (tcFUS) therapy based on the computed tomography (CT) of the monkey skull. Levenberg-Marquardt algorithm was applied for the training of the neural networks with 168 input learning materials. The predicted...
Much research has been done on named entity recognition such as whether the name is a person, company or place, and valuable contributions have been made. However, there has been little research on country recognition of people's names and places. In this paper, we develop a classification technique for social multimedia to automatically classify countries for person or place. This technique will...
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative...
In this paper, a new approach is proposed for feature reduction using a GA-Rough hybrid approach on Bio-medical data. The given set of bio-medical data is pre-processed with the min-max normalization method. Then the subsequent evaluation on each feature with respect to the output class is carried out utilizing the information gain-based approach using the entropy-based discretization. Features with...
Chinese segmentation is an important issue in Chinese text processing. The traditional segmentation methods those depend on an existing dictionary suffer the drawbacks when encounter unknown words. The paper proposed a segmenting algorithm for Chinese based on extracting local context information. It added the context information of the testing text into the local PPM statistical model so as to guide...
Support vector machines (SVM) can overcome the disadvantage of traditional anomaly detection, which need large sample data and have great effect in real-time detection, but has the disadvantage of slow training velocity. Least squares support vector machines (LS-SVM) can overcome the disadvantage of slow training velocity, but makes the solution lose sparsity and robustness. So a weighted LS-SVM (WLS-SVM)...
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