Chandra Shekar

Learn More
This paper presents a method of applying text mining techniques and data mining tools for pharmaceutical spam detection from Twitter data. A simple method based on a manually selected list of 65 pharmaceutical discriminating words is used for labeling spam training tweets. Preliminary experimental results show that J48 decision tree classifier has better(More)
Text messages express the state of minds from a large population on earth. From the perspective of decision makers, this collection of messages provides a precious source of information. In this paper, we present the use of Weka data mining tools to extract useful information for classifying sentiment of tweets collected from Twitter. The results of tweet(More)
New wireless technologies such as WiMAX, NFC and ZigBee are rapidly being adopted, along with existing wireless standards such as Bluetooth, Wi-Fi, GSM and other cellular technologies. Bluetooth and Wi-Fi have already become notorious for severe security shortcomings during their relatively brief existence. New vulnerabilities and exploits are reported and(More)
High quality digital video transmission requires efficient and reliable data communication over broadcasting channels as there is a risk of data corruption associated during transmission. The near channel performance of Low Density Parity Check Codes (LDPC) has motivated its use in second generation Digital Video Broadcasting (DVB) standards for mobile,(More)
Many small and medium-sized companies that develop software experience the same problems repeatedly, and have few systems in place to learn from their own mistakes as well as their own successes. Here, we propose a lightweight method to collect experience from completed software projects, and compare the results of this method to more widely applied(More)
—Twitter presents a new forum for spammers to facilitate illegal pharmaceutical scams. We present a classification scheme using decision strategy and data mining techniques taking into account the unbalanced nature of the data set. Four classifiers are used to identify pharmaceutical spam tweets. Classifiers J48 and Random Tree (RT) are generated by Weka(More)