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As a widely used medium platform, Micro-blog influence research is a hotspot. The community micro-blog, which is used as an effective tool by social managers in virtual community, has developed rapidly in recent years. As the basis of government micro-blog system in China, the community blog influence has great importance to guide the public popular feelings and guarantee the safety of the virtual...
In this paper, we developed the system for recognizing the orchid species by using the images of flower. We used MSRM (Maximal Similarity based on Region Merging) method for segmenting the flower object from the background and extracting the shape feature such as the distance from the edge to the centroid point of the flower, aspect ratio, roundness, moment invariant, fractal dimension and also extract...
In conventional text categorization algorithms, documents are symbolized as “bag of words” (BOW) with the fact that documents are supposed to be independent from each other. While this approach simplifies the models, it ignores the semantic information between terms of each document. In this study, we develop a novel method to measure semantic similarity based on higher-order dependencies between...
Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is difficult. According to the relative bin sub-image based studies, high dimensionality is affecting the system...
In order to overcome the shortcomings in traditional anomaly intrusion detection methods, such as low detection rate and high false alarm rate, this paper proposes an intrusion detection method based on wavelet kernel Least Square Support Vector Machine (LS-SVM). As a new machine learning method, SVM has been used in Intrusion Detection System (IDS) recently and achieved certain effects. While the...
Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion...
In this paper, we propose a novel method of anomaly detection in wireless sensor networks (WSN) based on S Transform. It makes use of S transform for feature extraction. We extract only the significant components of the time-series data. Earlier wavelets based approach that extracts features from the time-series data has been applied for detecting anomalies in combination with various classifiers...
This study proposes a clustering-based Wi-Fi fingerprinting localization algorithm. The proposed algorithm first presents a novel support vector machine based clustering approach, namely SVM-C, which uses the margin between two canonical hyperplanes for classification instead of using the Euclidean distance between two centroids of reference locations. After creating the clusters of fingerprints by...
The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition,...
Jamu is made from natural materials such as roots, leaves, timber and fruits. Jamu has many variations of formula. The composition of Jamu formula is usually based on empirical data or personal experiences. Thus, the classification for the efficacy of Jamu based on its compositions of plants still remains an interesting task. The purpose of this research is to develop a classification system for Jamu...
The music genre classification method includes two stages: feature extraction and classifier design. In this paper, we present how the characteristic of traditional Thai music affects the accuracy of music genre classification. With regards to our experiments, we find that each feature has the capability to be classified differently. The experiments are conducted on 180 excerpt of traditional Thai...
This study investigated the classification of multiclass motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using independent component analysis (ICA), principle component analysis (PCA) and support vector machine (SVM) techniques. The dataset used is available on BCI competition IV that contains EEG signals for 9 subjects who performed left hand, right hand, foot and...
The research reported in biomedical articles often involves large numbers of investigators at different institutions. To properly credit these investigators, an article's authors frequently name them together in some part of the article. These Investigator Names (IN) now constitute a required field in the MEDLINE® citation for the article. The automated extraction of these names is implemented in...
Estimation of steel consumption is an essential part of the implementation of quota design. Increasing accuracy of this estimation will not only help control the design phase, but also back up project decision and make it possible to conduct procurement plan earlier, which will shorten project life cycle. This paper adopts Support Vector Machine method among the numerous methods to apply it to this...
A new modelling method of image jacobian estimation is presented for uncalibrated visual servoing of robots, in which a kernel recursive least squares (KRLS) technique is used for non-linear mapping between target image features and robot joint angles, and an image jacobian expression is derived from the KRLS algorithm with gaussian kernel. The simulations of robot visual servoing with eye-in-hand...
This paper designs SVM radar emitter classification and identification methods based on the AP clustering. Using AP clustering algorithm to optimize the data set obtains a high-quality, small-sample training set of SVM classifier. Experimental results show that compared with the traditional SVM classifiers, the hybrid classifier has higher classification accuracy and furthermore Radar emitter classification...
In this paper, we propose a passive copy move image forgery detection method using a steerable pyramid transform (SPT) and Local Binary Pattern (LBP). SPT is applied on a grayscale version or one of the YCbCr channels of an image. LBP is applied to describe the texture in each SPT subband. Then the support vector machine (SVM) uses the LBP feature extracted from SPT sub-bands in classifying images...
Relevant Vector Machine (RVM) and Support Vector Machine (SVM) are two relatively new methods that enable us to utilize a few experimental sample points to construct an explicit metamodel. They have been extensively employed in both classification and regression problems. However, their performance in uncertainty analysis is rarely studied. The focus of this paper is to compare the two metamodeling...
This paper presents a weighted support vector machine (WSVM) based on association rules for two-class classification problems. The basic idea of the WSVM is to assign different weights to different data points to minimize impacts of outliers. In this paper, we apply association rules to generate weights to prevent bias to the majority class for imbalanced binary classification problems. Experimental...
Support Vector Machine (SVM) has slow training speed to deal with large amount of data in ECT system. In view of these shortcomings, an improved serial calculation-parallel transmission mode that based on SVM which is adapted to realize on hardware is proposed, and the mode on FPGA is realized using hardware description language. The work process of system and overall structure are presented. The...
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