Sri R
Graduate Student at Central Michigan University, Actively seeking for full time oppurtunities
LATEST PROJECTS
Project | 01
Project | Twitter Data Analysis for User Level Tweets Segregation based on their Locations
There are many number of researches that are presently going on the twitter data. I have summarized the related work on the basis of the user’s perception of analyzing data. Social networking sites, such as Facebook, Twitter, and LinkedIn, providing powerful opportunities in the field of social science research. The data which is been extracted from the social media is useful to better understand the social behavior and relationships. I had implemented text mining techniques to segregate the tweets from top contributors by using most frequently used keywords or hashtags in different domains which includes sports, media, entertainment, politics…etc.
Project | 02
Project | Authentication of Graphical Passwords using Strength Indicator
Authentication is an activity of linking an independent
or an individual process on the basis of user name and password which basically consists of characters, numbers, alpha numeric values, special characters etc. Everyone of us, use the simple textual passwords which can be easily guessed by the attacker.
As mentioned in many of the previous
researches textual passwords are very easy for hackers to understand. In this paper, I conducted a comprehensive survey of the existing
graphical password techniques. The password strength indicator exists in the textual password domain and I had provided a novel mechanism to compute graphical password strength indicator
using image segmentation.
Project | 03
Project | A Scalable Two-phase Top-down specialization approach for data anonymization using MapReduce on cloud
In this paper, we have proposed a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with this approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.