Prakash J. Kulkarni

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Existing tag based social media search engines present search results as a ranked list of images. But, they fail to identify visual, textual and geographical concepts present in query results. In this paper, we present an approach for automatic generation of visual, textual and geographical concept preserving summary of social image search results. For user(More)
Explosive and quick growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and sophisticated tools to help the Web user to find the desired information. Finding desired information on the Web has become a critical ingredient of everyday personal, educational, and business life. Thus, there is a demand for more(More)
One-versus-all (OVA) classification is one of the multiclass classification problems as well as it is a binary classifier. On the basis of this, we propose a network intrusion detecting system for the security of computers and networks. In this paper, we present a new learning algorithm for detection of a network intrusion using one versus all decision tree(More)
We address to the problem of Privacy Preserving Back Propagation Algorithm for a Vertically Partitioned Dataset. To enhance cooperation's in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an(More)
Intrusion detection systems have become a key component in ensuring the safety of systems and networks. This paper introduces the probabilistic approach called Conditional Random Fields (CRF) for detecting network based intrusions. In this paper, we have shown results for the issue of accuracy using CRFs. It is demonstrated that high attack detection(More)
In this paper, we propose a new research problem on active learning from data streams where data volumes grow continuously. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. We propose a classifier-ensemble based active learning framework which selectively labels(More)
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