- Label Prediction in Social Networks using NLP
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Mar 15 - Present
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Trying to predict the gender, political preferences and religious views of Users(Nodes) on Social Networks like Facebook. Initially used only network features and techniques like Gibbs Sampling for prediction. Started looking at textual Features to improve the prediction accuracy. For instance, by using Facebook wall posts of users and their connections, their gender can be predicted with 92% accuracy.
Project Guide: Dr. Dan Goldwasser
Assistant Professor, Department of Statistics, Purdue University
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- Quantitative Analysis of words and categories in Multiclass Regression
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Dec 13 - Present
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Trying to apply a joint high dimensional Bayesian Variable and Covariance Selection model to the multiclass textual classification. The word features are the variables and hence, variable selection problem corresponds to finding words that are good predictors overall and for specific categories. The covariance selection gives information about dependencies between the multiple categories.
Project Guide: Dr. Anindya Bhadra
Assistant Professor, Department of Computer Science, Purdue University
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- Empirical Analysis of Personal Email Network
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Nov 13
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Constructed and analyzed three different types of ego networks obtained from Gmail consisting of about \textit{seven and half years} of emails. Applied clustering and community detection algorithms to detect communities based of my email communications and compared them with communities detected from my facebook friendship network. Interestingly, I could recover a good number of them.
[Project Link] [Report]
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- TREC - Knowledge Base Acceleration Track
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May 13 -- Aug 13
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Had to filter documents related to entities (140 Wikipedia and 20 Twitter) that are worthy of citation in their profiles. The challenges we \textit{two fold}, \textit{one} the data was huge around 6.5 TB of compressed data consisting of social data, news articles etc. and \textit{two}, the entities had very few training examples, in the order of 10. Built a model similar to one-vs-all classifier and F1 measure was close to 0.6.
Project Guide: Dr. Luo Si
Associate Professor, Department of Computer Science, Purdue University
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- Supervised LDA for
Masquerader Detection
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Feb 13 -- Apr 13
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Extended a work of the PhD Thesis of Malek Ben Salem, that builds user-profiles based on search behaviour with a predefined taxonomy of applications and processes to detect masquerader attacks and intrusion
detection. Built a novel method by using a variation of LDA to build the taxonomy automatically . Also
showed that by using the latent classes obtained from the model as feature, we could build classifiers that give the same performance as those that used all the
feature, essentially a huge feature space reduction.
Project Guide: Dr. Seregy Kishner
Associate Professor, Department of Computer Science, Purdue University
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- Indiana Social Search
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May 12 - Present
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Built a website in PhP Indiana Social Search, to I crawl and classify news articles and tweets from Google News and Twitter respectively, into predened categories. This system is going to be integrated with the famous INDURE project. I am also working on extracting trend from the articles classify to make a trend cloud of the popular happenings in the state of Indiana and extracting mean ingful summaries for the crawled news articles. The LINK to the website.
Project Guide: Dr. Luo Si
Associate Professor, Department of Computer Science, Purdue University
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- Sampling and Analysis of Social Network Activity Graphs
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Sep 11 - Dec 11
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Mining Information from social networks gives valuable information about user activity and interaction. Constructed social network activity graphs of senders and receivers from the Purdue email data. Sampled data over two day window spans and computed various graph properties like the average degree, density etc. for these windows and the aggregate graph. Compared and contrasted email user activity with those of friendship networks like facebook.
Project Guide: Dr. Jennifer Neville and Dr. Ramana Rao Kompella
Assistant Professors, Department of Computer Science, Purdue University
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- Data Mining in Non-Binary Data Sets
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July 08 - April 2010
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Binary dataset representation gives information about an item being present or not in the search space, but does not provide any information about the strength of its presence which can be more effective in drawing association rules close to real life situations. Hence, we developed an algorithm for mining frequent itemsets and association rules from non-binary search space. As an extension, we generated weighted association rules. Further, we developed clustering algorithms for non-binary search space.
Project Guide: Dr. Anirban Sarkar and Dr. Narayan C. Debanath
Asstistant Professor, Deptartment of Computer Application, NIT Durgapur and Professor, Deptartment of CS, Winona State University, USA
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- Data Mining in Mobile Networking
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Jul 09 – Apr 10
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Data from Mobile Networks was analysed for predicting user movement, customer recommendation, business forecast and analysis. Predicting the user movement is an issue of major concern in mobile communication for better handoff mechanism and ensuring quality of service. Grouped User Profile based on Cells matrices and hierarchically clustered them. Also grouped frequent cells together based on user movement. Built a framework in java for performing necessary computations.
Project Guide: Mr. Parag Kumar Guhathakurtha
Assistant Professor, Department of Computer Science and Engineering, NIT Durgapur
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- Compression and Encryption for Secure Communication
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Jul 09 - Nov 09
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The ever increasing internet traffic constantly urges the need for enhancing communication security. So, we developed an algorithm for performing encryption and lossless compression at the same time in order to increase bandwidth utilization and to secure data transmission. We essentially converted the message into a bi-tuple using mapping techniques and encoded only one elements of the tuple.
Project Guide: Mr. Prasenjit Chowdhury and Mr. Jaydeep Howlader
Asstistant Professor, Department of Computer Application and Asstistant Professor, Department of Information Technology, NIT Durgapur
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- Semantic Analysis in Query Log Data
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May 09 - July 09
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Mining for semantic information from search engine query logs bears great potential for both the optimization of search engines and bootstrapping Semantic Web applications. Further, the formalization of log data into Logsonomies retains semantics information. Therefore we analysed and semantically characterized query term relatedness by grounding it to WordNet and compared it to prior results of Folksonomies.
Project Guide: Dr. Gerd Stumme and Dr. Andreas Hotho
Professor and Senior Researcher, Department of EE/CS, University of Kassel, Germany
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- MULET : A Multilanguage Encryption Technique
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Mar 09 - Oct 09
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The use of a multilingual approach in cryptography was not prevalent. So we focused on encryption of plain text over a range of languages supported by Unicode. We used mapping techniques to make the algorithm fast, efficient and easier to implement. Further, the replacement strategy used ensures better security. We believe this will facilitate the localization of Cryptographic Software tools.
Project Guide: Mr. Prasenjit Chowdhury
Assistant Professor, Department of Computer Application, NIT Durgapur
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- Document Clustering using Lexical Chains
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Dec 09 – Jan 10
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Lexical chains can be used to group documents together based on a common idea contained in the documents. Quality of clustering was improved by considering hypernyms, hyponyms etc. to build synsets and consequently lexical chains. We also addressed a situation where a document has a set of lexical chains common with one document and another set of lexical chains common with another document and so on. A Hierarchy of clusters can best depict such situation. Cliques can obtain such hierarchies from documents considered as nodes of a graph.
Project Guide: Dr. B. Ravindran
Associate Professor, Department of CSE, Indian Institute of Technology, Madras
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- Formal verification of softwares using Spin Model Checker
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Mar 08 – Feb 09
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Was involved in research on a project for Formal verification of Software. The Spin Model Checker is used to verify the integrity of software. Extracted the state transition diagrams and used the language Promela to get the properties verified. This may be extended to verify application in Web 2.0 and verification of network protocols.
Project Guide: Mr. Prasenjit Chowdhury
Assistant Professor, Department of Computer Application, NIT Durgapur
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