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Middleware sharing is one of the important resource sharing approaches which enables sharing of costs across a large pool of users. However, the shared Java middleware server easily causes interference on performance between concurrent user requests. A key requirement to an effective performance isolation is the knowledge of the resource consumption of the various kinds of use requests classified...
An epitope activates B cells to amplify and induce antibodies which can neutralize the foreign molecules, particles and pathogens. It also plays a crucial role in developing synthetic peptides for vaccination. Identification of epitopes using biological screening approaches is time consuming and high cost. Therefore, bioinformatics approaches are developed to enhance the speed of identifying the epitopes...
Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine...
This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine....
B-cell epitopes play an important role for developing synthetic peptide vaccines and inducing antibody responses. Applying biological experiments for epitope identification is time consuming and demands a lot of experimental resources. Nevertheless, it is important yet challenging task for designing a computer-aided B-cell linear epitope prediction system with high precision rates. In this paper,...
In this paper, we propose a clustering linear discriminant analysis algorithm (CLDA) to accurately decode hand movement directions from a small number of training trials for magnetoencephalography-based brain computer interfaces (BCIs). CLDA first applies a spectral clustering algorithm to automatically partition the BCI features into several groups where the within-group correlation is maximized...
Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens:...
Reduced Support Vector Machine (RSVM) was proposed as an alternate of the standard SVM. Motivated by resolving the difficulty on handling large data sets using SVM, it pre-extracts a subset of data as `support vectors' and solves a smaller optimization problem. But it selects `support vectors' randomly from the training set, and this will affect the result. A new method called reduced support vector...
Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
We have recently found that the computation time of homology-based subcellular localization can be substantially reduced by aligning profiles up to the cleavage site positions of signal peptides, mitochondrial targeting peptides, and chloro-plast transit peptides [1]. While the method can reduce the profile alignment time by as much as 20 folds, it cannot reduce the computation time spent on creating...
A novel blind separation algorithm based on double compression for single-channel JPEG permuted image was proposed in this paper. Firstly, the permuted image was compressed again, and then the primary compression factors were estimated by calculating the correlation coefficients of image blocks in pre and post recompression. Secondly, a `mapping space' was constructed based on the primary compression...
Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension...
The reduced support vector machine (RSVM) was proposed to overcome the computational difficulties as well as to reduce the model complexity in generating a nonlinear separating surface for a massive data set. However, it selects `support vectors' randomly from the training set, this will effect the result. To overcome this shortcoming of RSVM, an improved RSVM algorithm is presented in this paper...
Local stereo matching methods still play an important part as they are simple and fast. Some local methods perform well and even better than most global methods. But they usually achieve accuracy at the expense of speed. Simple local methods are fast, but exhibit systematic errors. In this paper, we focus on the invalid regions of traditional window-based matching and present a new solution to improve...
Coronary heart disease (CHD) remains the single leading cause of death of adults worldwide, but the traditional related factors can not explain the whole situations. Unstable angina (UA) is a type of CHD. The aim of this study was to establish clinical diagnose pattern for UA with blood stasis syndrome. Twenty-two biological parameters were detected on seven hundreds and seventy-six unstable angina...
DV-Hop algorithm is one of the important range-free localization algorithms. Three improved methods are put forward in this paper to settle the larger location error of the classic DV-Hop localization algorithm. First, the average hop-size of each anchor node is calculated by adopting least squares method. Second, the distance between unknown nodes and anchor nodes is refined by means of the average...
The functions of proteins are closely related to their subcellular locations. In the post-proteomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means. This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by using the information provided...
We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant. Our approach can also be used for designing...
In many models of customer relationship management (CRM) analysis, RFM model is widely accepted. RMF model is an important tool to weigh customer value and customer profitability. To address this issue, this paper closely combines the rough set theory with neural network and uses rough set theory to process the random sample data from dataset. Then the data is projected from high-dimensional to low-dimensional,...
In wireless sensor networks, energy efficiency is an important standard for evaluating the effectiveness of the time synchronization protocols. Hence, energy should be conserved as much as possible provided that algorithm can achieve a certain accuracy. In this paper, we propose an energy efficient time synchronization algorithm in which each layer of the network can be synchronized merely by receiving...
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