Sridhar Mandapati

Work place: Dept. of Computer Applications, R.V.R & J.C College of Engineering, Guntur, India



Research Interests: Image Compression, Image Manipulation, Information Security, Image Processing, Data Mining


Sridhar Mandapati obtained his masters in Computer Applications from S.V University, Tirupathi. He is currently working as Associate Professor in the Department of Computer Applications at R.V.R. & J.C College of Engineering, Guntur. He has 14 years of teaching experience. At present he is pursuing Ph.D. from Acharya Nagarjuna University, Guntur. He has seven internal publications and attended several national and international conferences. His areas of research interest include Data Mining, Information Security and Image Processing.

Author Articles
Classification via Clustering for Anonym zed Data

By Sridhar Mandapati Raveendra Babu Bhogapathi M.V.P.C.Sekhara Rao

DOI:, Pub. Date: 8 Feb. 2014

Due to the exponential growth of hardware technology particularly in the field of electronic data storage media and processing such data, has raised serious issues related in order to protect the individual privacy like ethical, philosophical and legal. Data mining techniques are employed to ensure the privacy. Privacy Preserving Data Mining (PPDM) techniques aim at protecting the sensitive data and mining results. In this study, the different Clustering techniques via classification with and without anonym zed data using mining tool WEKA is presented. The aim of this study is to investigate the performance of different clustering methods for the diabetic data set and to compare the efficiency of privacy preserving mining. The accuracy of classification via clustering is evaluated using K-means, Expectation-Maximization (EM) and Density based clustering methods.

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A Hybrid Algorithm for Privacy Preserving in Data Mining

By Sridhar Mandapati Raveendra Babu Bhogapathi Ratna Babu Chekka

DOI:, Pub. Date: 8 Jul. 2013

With the proliferation of information available in the internet and databases, the privacy-preserving data mining is extensively used to maintain the privacy of the underlying data. Various methods of the state art are available in the literature for privacy-preserving. Evolutionary Algorithms (EAs) provide effective solutions for various real-world optimization problems. Evolutionary Algorithms are efficiently employed in business practice. In privacy-preserving domain, the existing EA solutions are restricted to specific problems such as cost function evaluation. In this work, it is proposed to implement a Hybrid Evolutionary Algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Both GA and PSO in the proposed system work with the same population. In the proposed framework, k-anonymity is accomplished by generalization of the original dataset. The hybrid optimization is used to search for optimal generalized feature set.

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