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Building brain networks based on functional Magnetic Resonance Imaging (fMRI) signal is one of the efficient methods to study functional connectivity of human brain. Various methods of constructing brain network will lead to different results. It is wondered which method is reliable. Therefore, it is necessary to set up a synthetical framework of brain network analysis to study the functional connectivity...
Nowadays, owing to the growth of quantity of data, the data mining techniques have been required on web exceedingly for extracting information from the data. Classification of text in data mining is very important and has been a hot issue on the topic. Especially, ontological taxonomy classification is important for more intelligent information reasoning. As it relates to data distribution of classes...
The emergence of computing power and the abundance of data have made it possible to assist human decisions, especially in the stock markets, in which the ability to predict future values would lower the risk of investing. In this paper, we present a new approach for identifying the predictive power of public emotions extracted from various sections of daily news articles on the movements of stock...
Story identification from online user-generated content has recently raised increasing attention. Existing approaches fall into two categories. Approaches in the first category extract stories as cohesive substructures in a graph representing the strength of association between terms. The latter category includes approaches that analyze the temporal evolution of individual terms and identify stories...
Addressing the problem of information overload, automatic multi-document summarization (MDS) has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of MDS systems. In this paper, we proposed a novel unsupervised pattern-enhanced topic model (PETMSum) for the MDS task. PETMSum combining pattern...
A cloud platform website, offering a catalog of services, operates under a freemium business model or a free trial business model, aggressively marketing to customers who have previously visited. In such a cloud platform or service business, accurate identification of high profile customers is central to the success for the business. However, there are several limitations of existing approaches because...
The propose of this research was to classify the English as a foreign language (EFL) learners based on their performance on the reading test. Three levels of reading comprehension are customarily defined: (1) Factual level or Reading the lines, (2) Interpretive level or Reading between the lines, and (3) Evaluative level or Reading beyond the lines. Further analyzing and synthesizing factors underlying...
The association rule from data mining technology was applied into transformer defect analysis so that the frequent pattern, the dependency and the causality between classification and decision attributes could be found based on data of defects. As a result, correlation properties among grid fault elements were seized macroscopically. In this paper which focused on the frequent item mining algorithm...
This paper presents a method for human action recognition from depth sequence. First, we subdivided the normalized motion energy vector into a set of segments, whose corresponding frame indices are used to partition a video. Then each sub-action is represented by three Depth Motion Maps (DMMs) to capture motion cues in three orthogonal projection views. Multi-scale Histogram of Oriented Gradients...
Research is proposed for improving the human-interpretability of topic models; specifically, for topic models of small sequential documents. Experiments are proposed for evaluating the usefulness of topic modeling. The proposed experiments will model the topics of a diverse set of social media content and attempt to correlate the presence of topics related to terror attacks with actual attacks; additionally,...
Support Vector Machine (SVM) [2], is most widely popular learning algorithm used for classification of large dataset. Our project aims to generate a classifier for breast cancer genes microarray by using modified-SVM-RFE algorithm. This breast cancer microarray contains a large number of genes and its expression, so it necessary to reduce the number of genes before applying for classification. So...
Incidents of public security have an ascendant trend in recent years all over the world, and it is more important to understand the correlation of different kinds of public security incidents. With the popularization of the Internet, numerous web messages can provide resources to do that. However, an important challenge is that the web messages are often heterogeneous and unstructured. In this paper,...
Semantic computing is one of the important and indispensable approaches to analyze various kinds of environmental phenomena and its changes in the real world. In this paper, we present “A Seawater-Quality Analysis Semantic-Space in Hawaii-Islands with Multi-Dimensional World Map System” to realize a global and environmental analysis for ocean environment with the multi-dimensional world map system...
In recent years the social network analysis has been increased, in this paper we focus mining the small network formed by social gathering or business meeting. The main objective is to discover the existence of correlation and influence among the group of people by analysing their social affinity and emotions, currently we are interested only in facial emotions. Whereas the approach for emotion detection...
Pairwise prediction-error expansion (pairwise PEE) is an improvement of the conventional PEE and it can provide excellent performance for reversible data hiding (RDH). Unlike PEE in which the prediction-errors are modified individually, the correlation among prediction-errors is exploited in pairwise PEE by jointly modifying each prediction-error pair. In this paper, the idea of pairwise PEE is developed...
The automated matching of mug-shot photographs with sketches drawn using eyewitness descriptions of criminals is a problem that has received much attention in recent years. However, most algorithms have been evaluated either on small datasets or using sketches that closely resemble the corresponding photos. In this paper, a method which extracts Multi-scale Local Binary Pattern (MLBP) descriptors...
The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying...
In this paper, we introduce a new tool, CEREBRA, to visualize the 3D network of human brain, extracted from the fMRI data. The tool aims to analyze the brain connectivity by representing the selected voxels as the nodes of the network. The edge weights among the voxels are estimated by considering the relationships among the voxel time series. The tool enables the researchers to observe the active...
With the development of power monitoring technology, more and more real-time data are accumulated in power system. Due to large amount and high dimension of monitoring data, traditional data analysis methods are often unable to discover the hidden rules and complex relationships between monitoring data and fault reasons. In recent years, data mining techniques have been successfully applied in many...
Aiming at solving the problem of low matching accuracy caused by different imaging mechanism of heterologous image, we propose a novel image registration algorithm based on effective sub-image extraction and bidirectional matching for surf feature points. The algorithm adopts a coarse-to-fine matching strategy. Firstly, we transform the edge image into frequency domain through fast Fourier transform,...
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