Cloud Computing is the emerging technology in IT which aims more and more users to be part of it. Cloud computing is a revolution in IT the way resources are utilized and managed. It is an emerging and prosperous field for both academically and industrially. With its wide acceptance today security is a vital concern. Technique running at the back of Cloud computing is virtualization in which virtual machines simultaneously operates and application that controls and managed them is hypervisor. Many models for security of virtualization have been proposed for the protection of resources but still virtualization is being vulnerable to many attacks. Hypervisor forensics is an post approach to investigate and analyze security threats at hypervisor level. This research field will be beneficial for reducing crime rate at network level and improve security. This paper aims to understand some of the proposed model and identify research gap and challenges to provide better awareness of hypervisor forensics. The benefit of this work is that it depicts the stateof-the art in hypervisor forensics.

Abstract-The Lossless data hiding provides the embedding of data in a host image without any loss of data. This research explain a lossless data hiding and image cryptography method based on Choas -Block to image encryption the lossless means if the marked image is considered reliable, the embedding distortion can be totally removed from marked image afterward the embedded data has been extract. This procedure uses features of the pixel difference to embed more data than other randomly partition using Block based Sharpness Index Filtering and refine with single level wavelet decomposition shifting technique to prevent image distortion problems. In this work also manages reversible data hiding based on chaotic technique. In which initially image histogram processes to perceive the pixels which is chosen for hiding each bit of secret data, then by the logistic chaotic map compute an order of hiding each bit stream. Performances differentiate with other exist lossless data hiding plan providing show the superiority of the research. In this proposed research PSNR is found nearly 5.5*103 and existing 4.8*103 at 100 embedding rate which enhance for our existing technique that simulated in MATLAB 2014Ra.

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Solid waste management (SWM) is a crucial management entity in urban cities to handle the waste from its generation to disposal to accomplish a clean environment. The waste management operation mainly encompasses various climatic, demographic, environmental, legislative, technological, and socioeconomic dimensions. The traditional approaches deliver limitations in the process of predict and optimizing such composite non-linear operations. The integration of the internet of things (IoT) and artificial intelligence (AI) methods have progressively gained attention by delivering potential alternatives for resolving the difficulties in SWM. This article presents a review of the significance of the amalgamation of IoT and machine learning (ML) in the SWM to predict waste generation, waste classification, route optimization, estimation of methane emissions, and so forth. The article covers the application of each ML model for the activities, including SWM, compositing, incineration, pyrolysis, gasification, landfill, and anaerobic digestion.Moreover, it is concluded that the decision tree and random forest (DT-RF) algorithm is minor implemented, and artificial neural network (ANN) is implemented majorly in the SWM. The large number of data sets covered in the publication are secured and hidden; it limits replicating the AI models; this is also one key constraint of the non-implementation of AI models in SWM. Scarcity of data, accurate data, rare availability of customized AI models for tackling the activities in SWM are the limitations identified from the previous studies. Implementation of low-power ML processors, edge and fog computing-based devices is the future direction for overcoming SWM limitations.

In present time image data is growing up day by day in a large