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This article aims at finding the risk factors for hepatitis B virus (HBV) reactivation after the precise radiotherapy in patients with primary liver cancer (PLC). We use sequential forward selection and sequential backward selection to extract features which would be combined into an optimal feature subset, and then establish Bayesian and support vector machine (SVM) classification model. We use sequential...
In this work, we studied the problem of fall detection using signals from tri-axial wearable sensors. In particular, we focused on the comparison of methods to combine signals from multiple tri-axial accelerometers which were attached to different body parts in order to recognize human activities. To improve the detection rate while maintaining a low false alarm rate, previous studies developed detection...
In this work, we studied the problem of fall detection using signals from tri-axial wearable sensors. In particular, we focused on the comparison of methods to combine signals from multiple tri-axial accelerometers which were attached to different body parts in order to recognize human activities. To improve the detection rate while maintaining a low false alarm rate, previous studies developed detection...
The purpose of the study is to ascertain the key feature subsets of hepatitis b virus (HBV) reactivation and establish classification prognosis models of HBV reactivation for primary liver carcinoma (PLC) patients after precise radiotherapy (RT). Genetic Algorithm (GA) is proposed to extract the key feature subsets of HBV reactivation from the initial feature sets of primary liver carcinoma. Bayes...
The proportion of aged population nowadays almost rises acutely in each countries, which resulted from the increasing life expectancy and falling fertility rates. The impairment of mobility is a major health concern for the elderly. While the more decline of daily activities, more dangerous the elderly would be. The design of an image-based surveillance system for tracking elderly's activities in...
This paper presents an automatic event detection system fusing low and mid level features for soccer videos. We first employ an improved approach for Shot Boundary Detection with color and our mean-gradient feature. Then we classify the shots into two view types. We also perform a template-based replay detection for each shot. Play-break sequences are then generated using a rule-based method. We devise...
The rapid development of worldwide networks has changed many challenge problems from video level to big video level for vision based surveillance. An important technique for big video processing is to extract the salient information from the video datasea effectively. As a fundamental function for data analysis such as behavior understanding for social security, object tracking usually plays an essential...
Orange technologies focus on individual behavior analysis, and the core of which is object tracking, especially arbitrary object tracking. One of the popular solution for arbitrary object tracking is tracking by detection. These approaches regard the tracking problem as a detection task, and use the online learning methods to adapt the classifier to various object appearance changes. However, due...
Self-paced brain-computer interface (SP-BCI) has been considered a more practical BCI for users with motor disabilities. Previously, various methods have been proposed to improve the performance of the SP-BCI. However, no studies have been presented to compare the existing methods. In this study, we concentrate on a motor imagery-based two-state SP-BCI and compare the state-of-the-art methods, including...
A defect classification algorithm with bag of visual words approach for thin film transistor liquid crystal display (TFT-LCD) manufacturing is proposed in this paper. Color and SIFT features are introduced to describe defect region. Visual words vocabularies are learnt separated for each features. The two features are separately coded in bag of visual words and combined by multiple chi-square kernel...
In this study, we develop a novel vision-based Kodály musical hand signs recognition system to recognize the gestures of the musical notes. Vision-based gesture recognitions often face the following problems. First, the illumination change can influence the hand detections. Second, the hand tracking will become difficult under the complex background. To overcome the aforementioned problems, we propose...
Due to the large number of protein structures whose functions are unknown, it becomes increasing important to study the structural characteristics of catalytic residues. Here, we use a novel method to calculate the local structural rigidity (LSR) of protein. Based on a dataset of 760 proteins, the results show that catalytic residues have distinct structural properties. They are shown to be extremely...
Many image search engines nowadays still struggle with the semantic gap between low level image features and high level image concepts. Some solutions are proposed to bridge the gap by using surrounding texts of images or by adding tags on images by single user. However, they can only provide obscure or limited information about images. Another problem is that users may not know exactly what they...
Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via...
In this paper, surface electromyographic signal is analyzed by wavelet transform. The feature vectors are built by extracting the singular value of the wavelet coefficients. The multi-class support vector machine classifier is designed by using four kinds of multi-class classification approaches, and completed the eight class surface EMG pattern classification. The SVM classifier is applied to the...
In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability...
A large majority of speaker verification systems are based on frame-level acoustic features, such as Mel frequency cepstral coefficients (MFCCs) which characterize the vocal tract contribution. The most commonly used statistical GMM-UBM classifier models the distribution of MFCCs quite well. Pitch is one of the most important features which characterize speaker-dependent vocal fold vibration rate...
Support vector clustering (SVC) has been successfully applied to solve multi-class classification problems. However, it is usually hard to determine the hyper-parameters of RBF kernel functions. A multiple kernel learning (MKL) algorithm is developed to solve this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously obtained with semidefinite programming. However,...
In recent years, Support Vector Machine is used in many application areas and has shown dramatic achievement. In this paper, we apply it to a text-independent speaker verification task using the NIST 2001 Speaker Recognition database. Starting from a baseline based on Gaussian mixture models, we use the state-of-the-art GMM supervector and SVM to improve the performance. We alter several kernels and...
A new method called curvefaces was firstly presented for face recognition, which is based on curvelet transform. Curvelet is the latest multiscale geometric analysis tool. Contrast to wavelet transform, curvelet transform directly takes edges as the basic representation elements and is anisotropic with strong direction. It is a multiresolution, band pass and directional function analysis method which...
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