Informatics Publishing Limited

R. Nivedha ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur – 613 401, Tamil Nadu, IN

Abstract

A social media is a mediator for communication among people. It allows user to exchange information in a useful way. Twitter is one of the most popular social networking services, where the user can post and read the tweet messages. The tweet messages are helpful for biomedical, research and health care fields. The data are extracted from the Twitter. The Twitter data cannot classify directly since it has noisy information. This noisy information is removed by preprocessing. The plain text is classified into health and non-health data using CART algorithm. The performance of classification is analyzed using precision, error rate and accuracy. The result is compared with the Naïve Bayesian and the proposed method yields high performance result than the Naïve Bayesian. It performs well with the large data set and it is simple and effective. It yields high classification accuracy and the resulting data could be used for further mining.

Keywords

CART, Classification, Decision Tree, Machine Learning, Twitter

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R. Nivedha

Abstract

This paper presents a Ubiquo of data collection from the system server to mobile. Here, we proposed an approach for accessing the network efficiently in an organization and collect data through a mobile. The common problem is that, the data that are leaked might be found in the third parties hand (in the net). Data leakage is one of the biggest challenges in front of the industries & different institutes. In order toH improvise the possibilities of identifying the leakages, the data are distributed strategies among the agents. In some cases, to increase the chances of identifying the data leakages and the guilty party could be done by inserting some fake data.

Keywords

Mobile computing, Data extraction, data leakage detection, Web minning, Fake records, leakage model, data privacy, guilty agent, data distributor

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