Search

Nowadays, the scientific problem becomes very complex; therefore, it requires more computing power and storage space. These requirements are very common in an organization while dealing with current technological data and requirements. Based on these basic requirements, need of higher computational resources is an important issue when dealing with current technological methodology. Hence, cloud computing has become the most important computing paradigm of recent world. The cloud computing is an open source and using Internet as network model. Rapid growth in the field of &#34cloud computing&#34 also increases severe security concerns, because security has a constant issue. This study reviews security models of cloud computing.

Cloud computing facilitates extremely scalable serv ices that can be consumed over internet. An importa nt aspect of the cloud services is that user data are stored remotely in unknown machines in which users do not posses or manage. Since the data’s are stored remot ely, we have to keep in mind that sensitive cloud d ata have to be encrypted before they are outsourced to the commercial public cloud, which makes efficient data utilization service. Searchable encryption file ret rieval technique allows users to securely search ov er encrypted data through search word. Ranking the fil es based on relevance scores greatly enhances syste m usability by making it possible relevance ranking i nstead of sending unwanted results and further ensu res the file retrieval accuracy. In this study, we are developing an automated system for both named and unnamed documents based on the clustering algorithms. We implement the ranking and searching algorithm to retrieve top k files. We also provides the mapping and encryption algorithm to protect the information . The resulting design is able to provide efficient ranki ng which will reduce the search time drastically an d reduce the communication overhead. The mapping and encryption algorithms protect document against an outside attackers and prevent an untrusted cloud data provi der from learning data.

In real world data mining is emerging in various er a, one of its most outstanding performance is held in various research such as Big data, multimedia minin g, text mining etc. Each of the researcher proves t heir contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas t he implementation model derives some sort of knowledge from the real time decision making behaviour such a s artificial intelligence and swarm intelligence an d has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of data set and attributes. This knowledge is applied to th e concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textu al documents, text mining is applied. The text mini ng is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM) is defined for effective text clustering. This VMM involves the le arning of conceptual terms; these terms are grouped in Significant Term List (STL). VMM model is appropriate combination of layer 1 arch with Analysis of Bilateral Intelligence (ABI). The frequent update o f conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorit hm is used for learning texual pattern in the Virtual Min ing Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning a lgorithm.

Entertainment in the real world becomes a popular era and it is long lasting for several decades. The major advancement in television as well as in projection leads to highest quality in terms of realistic pictures, images and movies. Various researchers and research forums are under the development of stereoscopic conversion of 2D program material into 3D realistic. Due to less availability of 3D videoscopic displays, we proposed a new methodology to convert 2D array of video sequence into stereoscopic 3D.The proposed methodology focuses on depth array conversion of back grounds from the 2D video and subtracting the active objects in each scenes at every frames, this yields to obtain the depth information from the video which requires heavy computation in real time, which was achieved by Cryengine with AMD QUADCORE A6 CPU, as well with Radeon graphics. Experimental results shows that 3D recognition rate of a single image in the 2D video sequence is up to 78% and display rate of the particular sequence are at 29 frames per second. Experimental results has been included and 3D display rate for the converted sequence is up to 92% accuracy and frame rates be 32 frames per second for FULL HD (1080 p).

A vital issue of routing is how to efficiently stre ngthen energy consumption of the whole network and to avoid the energy-hole which leads to node failure o r node death; this scenario makes the network to wo rk improperly and leads to network death, Here we proves our hypothesis with linearity between routing an d linear principles. Our proposed work effectively ut ilizes the adaption of Optimized particle swarm algorithm (Fast Reactive and fast adaptive algorith m) in order to solve the routing problem in wireles s sensor networks to avoid the energy hole. Our appro ach effectively defines the node leader based on en ergy level and path optimization for node traversing usi ng particle swarm which effectively utilizes the so lution for problem in linearity into routing problem. Our algorithm FFA redefines the particle swarm rules wh ich are reliable in routing strategies and adapts to th e working principle. The optimization in our algori thm is to balance energy level and to withstand for its conte xt. This study proves the accuracy using our algori thm respectively by theoretical survey and analysis wit h simulated results.

Problem statement: Breast cancer is one of the most dangerous disease s that cause innumerable fatal in the female society. Early dete ction is the only way to reduce the mortality. Due to variety of factors sometimes manual reading of mammogram results in misdiagnosis. So that the diagnosis rate varies from 65-85%. Various computer aided detection techniques have been proposed for the past 20 years. Even then the detection rate is still not high. Approach: The proposed method consists of the following steps preprocessing, segm entation, feature extraction and classification. Noise, Artifact and pectoral region are removed in a preprocessing step. Contrast enhancement and Sobel operator with segmentation algorithm is used to segment the mass region. Feature extraction is performed on the segmented image using gray level co-occurrence matrix and local binary pattern method. Extracted features are classified using sup port vector machine. The performance of the proposed system is evaluated using partest method. Results: Proposed algorithm shows 98.8% sensitivity and 97.4% Specificity. Conclusion: The proposed algorithm is fully automatic and will be helpful in assisting the radiologists to detect the malignancy efficiently.