Nowadays, publishing data publically is an important for many purposes especially for scientific research. Publishing this data in its raw form make it vulnerable to privacy attacks. Therefore, there is a need to apply suitable privacy preserving techniques on the published data. K-anonymity and L-diversity are well known techniques for data privacy preserving. These techniques cannot face the similarity attack on the data privacy because they did consider the semantic relation between the sensitive attributes of the data. In this paper, a semantic anonymization approach is proposed. This approach is based on the Domain based of semantic rules and the data owner rules to overcome the similarity attacks. The approach is enhanced privacy preserving techniques to prevent similarity attack and have been implemented and tested. The results shows that the semantic anonymization increase the privacy level and decreases the data utility.

Virtualization is widely adopted by the data centers, in order to fulfill the high demand for resources and for their proper utilization. For system management in these virtualized data centers virtual machine live migration acts as a key method. It provides significant benefit of load-balancing without service disruption. Along with the various benefits virtual machine live migration also imposes performance overhead in terms of computation, space and bandwidth used. This paper analyzes the widely used precopy method for virtual machine live migration and proposes the two fold optimization of precopy method for virtual machine live migration. In the first phase, the proposed two fold precopy method reduces the amount of data sent in first iteration of precopy method. Second phase restricts sending of similar data iteratively in each subsequent iterations of precopy method by identifying frequently updated pages and keeps it till the last stop and copy iteration. In this way it reduces total migration time and total amount of data transferred. The proposed two fold precopy method is compared with precopy method and simulation results show the performance improvement of a virtual machine live migration in terms of total migration time and total amount of data transferred.

Twitter is extensively used in the Arab World in the last few years. It showed positive impact on both students and teachers in academic institutions. In this paper we introduce Taibah University as a case study to examine Twitter uses and benefits in education environments. Deans, Students and Teachers accounts on Twitter will be examined to discover the general uses of Twitter and the type of information which is commuting between students and teachers. Results showed that twitter is used extensively in Taibah University for after class room discussions, and for the teachers to share class notes and lectures, in addition it showed highly agreement on the importance of twitter in Taibah University from both students and faculty members.

Metaheuristic methods are capable of solving a wide range of combinatorial problems competently. Genetic algorithm (GA) is a metaheuristic search based optimization algorithm that can be used to generate optimal Covering Arrays (CAs) and Mixed Covering Arrays (MCAs) for pair-wise testing. Our focus in the work presented in this paper is on the strategies of performing mutation in GA to enhance the overall performance of GA in terms of solution quality and computational time (number of generations). This is achieved by applying a greedy approach to perform mutation at a position that minimizes the loss of existing distinct pairs in the parent CA/MCA and ensures that the generated offspring is of good quality. Experiments are conducted on several benchmark problems to evaluate the performance of the proposed greedy based GA with respect to the existing state-of-the-art algorithms. Our evaluation shows that the proposed algorithm outperforms its GA counterpart by generating better quality MCA in lesser number of generations. Also the proposed approach yields better/comparable results compared to the existing state-of-the-art algorithms for generating CAs and MCAs.

Mobile app reviews are gaining importance as a crowd source to improve the quality of mobile apps. Mobile app review systems are providing a platform for users to share their experiences and to support in decision making for a certain app. Developers, on the other side, are utilizing the review system to get real-life user experience as a source of improving their apps. This paper has analyzed existing review system and proposed few recommendations for the current review system to improve the quality of app reviews. The proposed review system can help for collection and analysis of user reviews to make it more meaningful with less intensive data mining techniques. The proposed system can help the end users to get an overview of mobile apps. The recommendations in this paper are derived from the existing literature related to app reviews and will help to improve the current review systems for better app reviews from users as well as developers perspective.

Computational paradigm has been revolving round cloud computing and its offshoots for some time and till we see a breakout resulting in a breakthrough technology driven by advances in microelectronics and material technology. Till we experience a radically efficient technology for computation it is worth juxtaposing the virtues of cloud computing and market’s longing for offering cost and quality arbitrage to the marketplace. Integration of cloud computing in enterprises has the potential to influence the way business gets carried out by them in the market place. Different reports show that demand for cloud computing products and processes is in an upward growth trajectory. This paper identified the characteristics, drivers and constraints of cloud computing which influence its adaptation and integration in enterprises. We are also examining India specific opportunities and threats of cloud computing tools and cloud driven practices in the context of fierce competition among enterprises to remain competitive in the marketplace by reducing software licensing fees, cost of capital to acquire digital systems and cost of maintenances.
New directions in cloud computing are analyzed by using Gartner strategic technologies and trend in research publications. Paper focuses on exploring the research issues which are categorized into technical and business in nature for understanding the evolving fortunes of cloud computing. Number of papers published in IEEE is an indication of the popularity and relevance of the continued research initiatives happening in the area. It is also noticed that that very few researchers are attempting to understand the possibility of remodeling business processes leveraging the new found computational paradigm.

In recent years, with an increasing number of requests, energy, power and temperature have been important keys in embedded systems, which decrease the lifetime of both CPUs and hard disks. The energy consumption is an important issue in computer systems, particularly real-time embedded systems. The frequency and the Revolutions Per Minute are major factors in the reduction of energy consumption in both processors and hard disk drives. Therefore, the main goal of this paper is to present a scheduling mechanism for a real time periodic task that can save more energy. This mechanism is based on increasing, as much as possible, the execution time of the CPU and/or the Read/Write time of the hard disk without passing the task deadline. This will be done by dynamically changing the CPU frequency and/or the RPM of hard disk. Our experimental results demonstrate that the proposed algorithm manages to lower energy consumption by an average of 25% and to reduce the number of missed tasks by 80%.

Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

Vehicular Ad hoc Network (VANET) is formed to share information on a temporary basis between the vehicular nodes in a wireless medium. The routing information is used to discover the vehicles and the route has to be maintained to transfer the data. It may lead to link failure or breakage in the communication, if there is high network mobility and constrained topology arrangement. In case of failure, it may cause loss of data and delay in the network. So, to avoid breakage, the routing information is stored in traditional protocols like CBAODV and CS-CBAODV. Instead of storing routing information in client side, the idea of storing data in the remote side is taken into consideration to backup permanently. This remote server manages the data in client server which is to be delivered to vehicular node. After analyzing related protocols and simulators, a new reactive based routing protocol is designed in this research work and it is named as RS-CBAODV is used to handle routing information between vehicular node, Client side node (RST i.e. Road Side Terminals) and also in Remote side storage node. To analysis the performance of the proposed protocol, MOVE and NS2 simulator is used to compare both CS-CBAODV and RS-CBAODV protocols.

The use of fuzzy logic in disease diagnosis is very common and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. Most of the research proposed different systems for the diabetes diagnosis. But their accuracy of prediction is not accurate. So, the proposed system presents promising approach for accurately predicting the diabetes by considering the different parameters which are helpful in the diagnosis of diabetes. The proposed fuzzy verdict mechanism takes the information collected from the patients as inputs in the form of datasets. System considers both rules and physicians knowledge to provide the prediction rate of diabetes. Evaluation shows the approach results in better accuracy as compared to other prediction approaches.