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Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity...
WIA-PA (Wireless Networks for Industrial Automation-Process Automation) standard has become one of the three major IEC (International Electrotechnical Commission) standards for industrial wireless networks technology, and multi-channel frequency hopping mechanism is significant for the performance of WIA-PA networks. Existing AFH (adaptive frequency hopping) mechanism for WIA-PA networks is based...
With the advances in high-throughput technology, a large number of protein interactions data have been burgeoning in recent years, which makes it possible for considering dynamic properties of protein interaction networks(PINs) instead of static properties. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we proposed a new model-based scheme for the construction...
According to the characteristics of infrared images of electrical equipment, a new image segmentation method based on wavelet transformation and fuzzy clustering is proposed in this paper. Because image segmentation based on the fuzzy C mean (FCM) clustering algorithm is easy to be affected by the initial clustering center and the clustering number, which often leads to the convergence of results...
Emerging big data applications comprise rich multi-faceted workflows with both compute-intensive and data-intensive tasks, and intricate communication patterns. While MapReduce is an effective model for data-intensive tasks, the MPI programming model may be better suited for extracting high-performance for compute-intensive tasks. Researchers have recognized this need to employ specialized models...
As an important property of complex networks the research of community structure has never stopped. Recently people found some nodes may belong to several communities, so more and more people try to present algorithms to detect the overlapping communities in network. In addition, for a large scale complex network, a algorithm with lower time complexity and higher classification accuracy is required...
With the development of high-throughput technique, protein-protein interactions (PPIs) are increasing fast and available conveniently, which make it possible to identify protein complexes in PPI networkf 1–7]. Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks[8–9], which requires protein complex detection methods can identify both...
The vast amount of genes and proteins that participate in biological networks imposes the need for determination of protein complexes within the network in order to reduce the complexity, while these complexes will be the first step in deciphering the composite genetic or cellular interactions of the overall network.
There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the...
Infrastructure-as-a-Service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use least resources to deliver best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to place virtual machines closely to minimize network latency...
Fuzzy clustering algorithms have been successfully applied to POLSAR classification, but not to POLInSAR. In this paper, a Fuzzy C Means (FCM) clustering algorithm integrating the complementary physical information and statistical property contained in both polarimetric and interferometric data, is used for POLInSAR classification. At first, the area dominated by volume scattering is extracted from...
The traditional Fuzzy C-Means (FCM) Clustering Algorithm is widely used in Data Mining technology at present. It always adopts Euclidean Distance to measure the dissimilarity between objects. Accordingly the clusters with convex shapes could be generally discovered. But it is difficult to discover the clusters with irregular shapes, and also is more sensitive to the existence of noise and isolated...
It's a hotspot to expend the research on support vector machine from a two-class issue to a multi-class one. Among all kinds of methods, Bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting then others, and it works out the impartibility problem. So it is a good method. The dissertation systematically researches and analyses Bintree...
An interconnect-driven layout-aware multiple scan tree synthesis methodology is proposed in this paper. Multiple scan trees greatly reduce test data volume and test application time. However, previous researches on scan tree synthesis rarely considered routing length issues, and hence create scan trees with long routing paths. The proposed algorithm effectively considers both test compression rate...
Dense subgraphs of protein interaction networks are believed to be potential protein complexes and play an important role in analyzing cellular organization and predicting functions of proteins. In this paper, we present a new algorithm LD-Miner for mining l-dense subgraphs in protein interaction networks. We apply algorithm LD-Miner to the protein interaction network of Saccharomyces cerevisiae collected...
Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm takes a long time to partition a large dataset. In addition, in current fuzzy cluster algorithms it is difficult to determine the cluster centers. This paper proposes a modified FCM algorithm for MR (magnetic resonance) brain images segmentation. This method fetches in...
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