Future trends for University distance learning systems involve integration of the mobile devices with the support of cloud computing services. The multimedia content that is delivered to the student's mobile devices provides an efficient learning environment. The main benefit, elaborated in this paper, is to use the processing power of the mobile cloud computing (MCC) environment for adapting the multimedia content to the context-aware network conditions of the mobile user. In this paper we also improve the QoE (Quality of Experience) metrics as a multi-dimensional construct of user perceptions and behaviors that is in direct relation to the QoL (Quality of Learning) in the process of distance learning.

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Cloud computing consists of hardware and software resources, available on the Internet as a set of services for users. This technology aims to provide stable, reliable and encapsulated dynamic information and communication environment for end users to be able to simultaneously access shared resources that are available anywhere and at any time. The major benefit of cloud computing is used to improve the perception of quality for the client requests. Commonly in the communications industry, the term Quality of Experience (QoE) is used as a measure for the user perception of service from the user's point of view. In this research, we propose a classification of cloud-based services based on objective and subjective characteristics for perception of quality. The main contribution in this paper is a novel approach based on Bayesian modeling for efficient assessment of QoE perception for cloud-based services considering the level of interactivity, service complexity, usage domain, and multimedia-intensity.

1 Abstract-Today we see various small automated devices that are used to regulate the energy consumption in households. Most of these devices work autonomously on a specific set of appliances or only for specific necessities, such as heating or cooling. The proposed smart energy solutions are focused on gathering datasets of the historical data for energy consumption and then propose different algorithms for analysing these datasets.In this paper, we investigate the possibility of using a new Smart-ECO model, consisting of a smart device, which is running its own smart algorithm. We will conduct analysis on real-time household dataset, in order to optimize the electricity consumption per appliance. The proposed model is based on the custom preferences and behavioural habits of the people that live in the household and the inter-dependency of the appliances that are active at the moment.Index Terms-Smart homes, regression analysis, artificial bee's colony algorithm, habitual average.

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Abstract. With the rapid growth in technology, there is a huge proliferation of data in cyberspace for its efficient management and minimizing the proliferation issues. Distributed file system plays a crucial role in the management of cloud storage which is distributed among the various servers. Many times some of these servers get overloaded for handling the client requests and others re-main idle. Huge number of client requests on a particular storage server may in-crease the load of the servers and will lead to slow down of that server or dis-card the client requests if not attended timely. This scenario degrades the over-all systems performance and increases the response time. In this paper, we have proposed an approach that balances the load of storage servers and effectively utilizes the server capabilities and resources. From our experimental results and performance comparison of proposed algorithm with least loaded algorithm we can conclude that our approach balances the load, efficiently utilize the server capabilities and leverage the overall system performance.

Abstract. When two-dimensional medical images are subject to fractal analysis, one of the methods used is to detect the contour of objects in the binary images and later to estimate the fractal dimension of the extracted contour. This scalar characteristic of the medical image should help in discrimination between normal and abnormal tissues. In this paper we expose the factors that affect the reliability of such examinations and put the fractal dimension in question as a valid criterion for description, classification and recognition in medical diagnosing.