P.Srinivasa Rao

Work place: Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam

E-mail: peri.srinivasarao@yahoo.com


Research Interests: Data Structures and Algorithms, Data Mining, Image Processing, Computer Architecture and Organization


Dr. Peri. Srinivasa Rao is presently working as Professor in the Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam. He got his Ph.D degree from Indian Institute of Technology, Kharagpur in Computer Science in 1987. He published several research papers and delivered invited lectures at various conferences, seminars and workshops. He guided a number of students for their Ph.D and M.Tech degrees in Computer Science and Engineering and Information Technology. His current research interests are Image Processing, Communication networks, Data Mining and Computer Morphology.

Author Articles
A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes

By N.V.S. Lakshmipathi Raju M.N. Seetaramanath P.Srinivasa Rao

DOI: https://doi.org/10.5815/ijitcs.2019.04.03, Pub. Date: 8 Apr. 2019

Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate. Existing KC and KCi models concentrate on multiple categorical sensitive attributes. Both these models have their own merits and demerits. This paper proposes a new method named as novel KCi - slice model, to enhance the existing KCi approach with better utility levels and required privacy levels. The proposed model uses two rounds to publish the data. Anatomization approach is used to separate the sensitive attributes and quasi attributes. The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables. The second round generates   multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes. It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables. Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.

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Performance Evaluation of Image Segmentation Method based on Doubly Truncated Generalized Laplace Mixture Model and Hierarchical Clustering

By T.Jyothirmayi K Srinivasa Rao P.Srinivasa Rao Ch.Satyanarayana

DOI: https://doi.org/10.5815/ijigsp.2017.01.06, Pub. Date: 8 Jan. 2017

The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and Hierarchical clustering (DTGLMM-H) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-H algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been done through various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies for various different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.

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