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Pap smear test plays an important role for the early diagnosis of cervical cancer in which human cells taken from the cervix of patient are analysed for pre-cancerous changes. The manual analysis of these cells by expert cytologist is labor intensive and time consuming job. The automatic and accurate detection of cervical cells are two critical preprocessing steps for automatic Pap smear image analysis and also for diagnosis of pre-cancerous changes in the uterine cervix. Similarly, the reliable segmentation of abnormal nuclei in cervical cytology is of utmost importance in automation-assisted screening techniques. This paper presents, the existing automated methods for the detection, segmentation and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The majority of cytoplasm segmentation uses K-means algorithm, edge detection method, thresholding approach, graph cut and active contours technique. Most of existing work is done on images of isolated cells, especially for those which are in the Herlev data set. For segmentation of images which contains multiple cells, level set and thresholding techniques have been used. The nucleus segmentation varies as: single-nucleus segmentation, touching-nuclei splitting and multiple-nuclei segmentation. However, many segmentation methods incorporates shape priors, usually enforcing elliptical shapes in order to overcome cell occlusion and noise. The main focus of this paper is comprehensive literature survey of various existing classification and segmentation techniques. The shortcomings and failures of the existing work are also provided for further enhancement and improvement of overall performance and accuracy.

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Cloud computing plays a significant role since its evolution. With its ubiquitous nature, sharing of resources and management of services has never been convenient than ever before. Due to its ability to provide scalability and elasticity infrastructure, many organization utilizes the services, where the workload is shifted in cloud data centers. This data center consumes more power and there is the release of unwanted carbon footprint in the environment. Therefore here lies the need to improve the use of energy and at the same time minimizing power consumption. In this paper, we present a survey on VM placement and migration to achieve energy efficiency in cloud data centers.