Informatics Publishing Limited

T. Muralidharan ¹, S. P. Rajagopalan ²

Affiliations
1 Department of Computer Science and Engineering, Dr. MGR Educational and Research Institute University, Chennai, Tamilnadu, IN
2 Department of Computer Applications, Dr. MGR Educational and Research Institute University, Chennai, Tamilnadu, IN

Abstract

Web sites and transactional databases collect large amounts of time-stamped data related to an organization's suppliers and/or customers over time. Mining these time-stamped data can help business leaders make better decisions by listening to their suppliers or customers via their transactions collected over time. A business can have many suppliers and/or customers and may have a set of transactions associated with each one. However, the size of each set of transactions may be quite large, making it difficult to perform many traditional data-mining tasks. This paper proposes techniques for large-scale reduction of time-stamped data using time series analysis, seasonal decomposition, and automatic time series model selection. After data reduction, traditional data mining techniques can then be applied to the reduced data along with other profile data. This paper demonstrates these techniques SAS® High-Performance Forecasting software.

Keywords

Time Series Analysis, Transactional Mining, Time Stamped Data.

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F. Antony Xavier Bronson ¹, S. P. Rajagopalan ²

Affiliations
1 Department of Computer Science and Engineering, Dr. MGR Educational, and Research Institute University, Chennai, Tamilnadu, IN
2 Department of Computer Applications, Dr. MGR Educational and Research Institute University, Chennai, Tamilnadu, IN

Abstract

Individual wellbeing record (IWR) is a rising pattern in the wellbeing field for the trade and utilization of individual wellbeing data. This record is currently a day's put away in the outsider stockpiling ranges like in the cloud suppliers. To lessen the storage room required and for the expense diminishment, the cloud administration connected. There are loads of security issues related with the capacity of delicate individual wellbeing data in the mists. The security and privacy of individual wellbeing data have challenges when cloud storage and applications utilized. Here the propelled encryption systems like trait based encryption and its varieties are utilized. Secure offering of individual wellbeing record is guaranteed in this framework.

Keywords

Trait Based Encryption (TBE), Cloud Storage, Security, Cloud Information Security, Individual Wellbeing Record (IWR).

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S. Aruna ¹, L. V. Nandakishore ², S. P. Rajagopalan ³

Affiliations
1 Department of Computer Applications, Dr. M.G.R University, Chennai-95, IN
2 Department of Mathematics, Dr. M.G.R University, Chennai-95, IN
3 Dr. M.G.R University, Chennai-95, IN

Abstract

In this paper a classifier model is proposed to detect the different states of breast cancer. GIST-SVM is used to differentiate patients having benign and malignant cancer cells. To improve the accuracy of classification, the optimal size of the training sets are determined. To find the optimal size of the training set, different sizes of training sets are experimented and the one with highest classification rate is selected.

Keywords

Breast Cancer, Gist, Optimum Training Size, Pattern Classification, Support Vector Machines.

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