Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts.
Virtualization is a key concept in empowering the "Infrastructure as-a-Service (IaaS)" of cloud-based services. Live migration of virtual machines is the process of moving virtual machine from one physical host to another without interrupting other VMs. Migration of VMs supports the improved efficiency of resource usage and dynamic resource provision capabilities. The performance metrics of live migration are down time and total migration time. In this paper, a technique to reduce the total migration time of standard memory migration using the mirroring of VMs at destination host is proposed. It mainly concentrates on the total migration time and maximum profit of providers. The proposed technique avoids the iterative transmission of memory pages from the source host to destination host. Performance results depict that the proposed algorithm can reduce 36% of the data transmitted during migration and 26% of the total migration time when compared with pre-copy migration technique.
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