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Suresh Babu, M.
- Enterprise Risk Management Integrated framework for Cloud Computing
Abstract Views :115 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Applications, Madanapalle Institute of Technology and Science, Madanapalle, AP, 517325, IN
2 Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapur - 515003, AP, IN
3 Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapur – 515003, AP, IN
1 Department of Computer Applications, Madanapalle Institute of Technology and Science, Madanapalle, AP, 517325, IN
2 Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapur - 515003, AP, IN
3 Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapur – 515003, AP, IN
Source
International Journal of Advanced Networking and Applications, Vol 5, No 3 (2013), Pagination: 1939-1950Abstract
The emergence of cloud computing is a fundamental shift towards new on-demand business models together with new implementation models for the applications portfolio, the infrastructure, and the data, as they are provisioned as virtual services using the cloud. These technological and commercial changes have an impact on current working practices. Businesses need to understand the impact of the new combinations of technology layers, and how they work together. A crucial part of this is analyzing and assessing the risks involved. In the evolution of computing technology, information processing has moved from mainframes to personal computers to server-centric computing to the Web. Today, many organizations are seriously considering adopting cloud computing, the next major milestone in technology and business collaboration. A supercharged version of delivering hosted services over the Internet, cloud computing potentially enables organizations to increase their business model capabilities and their ability to meet computing resource demands while avoiding significant investments in infrastructure, training, personnel, and software. In fall 2010, a Google executive testified before a U.S. congressional subcommittee that more than three million businesses worldwide were customers of its cloud service offerings. As with any new opportunity, cloud computing entails commensurate risks. It brings to organizations a different dimension of collaboration and human interaction, new organizational dependencies, faster resource fulfilment, and new business models.Keywords
Server - Centric Computing, Virtualization, Cloud Computing.- Clustering Approach to Stock Market Prediction
Abstract Views :130 |
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Authors
Affiliations
1 Intel Institute of Science, Anantapur, Andhra Pradesh, IN
2 Department of Computer Science, S.K. University, Anantapur, IN
3 Board of Studies, Department of Computer Science, Sri Krishnadevaraya University, Anantapur, IN
1 Intel Institute of Science, Anantapur, Andhra Pradesh, IN
2 Department of Computer Science, S.K. University, Anantapur, IN
3 Board of Studies, Department of Computer Science, Sri Krishnadevaraya University, Anantapur, IN
Source
International Journal of Advanced Networking and Applications, Vol 3, No 4 (2012), Pagination: 1281-1291Abstract
Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Various clustering algorithms have been developed which results to a good performance on datasets for cluster formation. This paper analyze the major clustering algorithms: K-Means, Hierarchical clustering algorithm and reverse K means and compare the performance of these three major clustering algorithms on the aspect of correctly class wise cluster building ability of algorithm. An effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering) is proposed, to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each subcluster belong to the same class. Then, for each sub cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits.- Information Delivery System Through Bluetooth in Ubiquitous Networks
Abstract Views :112 |
PDF Views:0
Authors
Affiliations
1 INTELL Engineering College, Anantapur-515004, IN
2 INTELL Institute of Science, Anantapur-515004, IN
3 Beasant Institute of Technology & Science, Anantapur-515004, IN
4 Sri Krishnadevaraya University, Anantapur-515053, IN
1 INTELL Engineering College, Anantapur-515004, IN
2 INTELL Institute of Science, Anantapur-515004, IN
3 Beasant Institute of Technology & Science, Anantapur-515004, IN
4 Sri Krishnadevaraya University, Anantapur-515053, IN