Work place: National Institute of Technology /CA, Tiruchirapalli, 620015, INDIA

E-mail: npgopalan@nitt.edu


Research Interests: Distributed Computing, Computing Platform, Data Mining


N.P.Gopalan Professor at Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India. He obtained his Ph.D. from Indian Institute of Science, Bangalore, India. His research interests are in Data Mining, Distributed Computing, Cellular Automata, Theoretical Computer Science, Image Processing and Machine Intelligence.

Author Articles
Key Term Extraction using a Sentence based Weighted TF-IDF Algorithm

By T. Vetriselvi N.P.Gopalan G. Kumaresan

DOI: https://doi.org/10.5815/ijeme.2019.04.02, Pub. Date: 8 Jul. 2019

Keyword ranking with similarity identification is an approach to find the significant Keywords in a corpus using a Variant Term Frequency Inverse Document Frequency (VTF-IDF) algorithm. Some of these may have same similarity and they get reduced to a single term when WordNet is used. The proposed approach that does not require  any test or training set, assigns sentence  based Weightage to the keywords(terms) and it  is found to be  effective. Its suitability is analyzed with several data sets using precision and recall as metrics.

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An Efficient Image Block Encryption for Key Generation using Non-Uniform Cellular Automata

By G. Kumaresan N.P.Gopalan T. Vetriselvi

DOI: https://doi.org/10.5815/ijcnis.2019.02.04, Pub. Date: 8 Feb. 2019

Cryptographic image block encryption schemes play a significant role in information enabled services. This paper proposes an image block encryption scheme based on a novel three stage selection (TSS) method in a public cloud with reversible cellular automata. Due to the openness of public cloud, different attacks are possible over user sensitive information. The TSS method has three stages and they generate a robust master key with user plaintext as input and produces an encrypted block as key to be sent to authenticated users. An analysis of experimental results shows that this new method has a large key space and immune to brute force attacks, statistical cryptanalysis attacks and chosen plaintext attacks. Also, the encrypted image entropy value could be increased to 7.9988 making it ideal for a best image block encryption for key generation.

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A Facial Expression Recognition Model using Support Vector Machines

By Sivaiah Bellamkonda N.P.Gopalan

DOI: https://doi.org/10.5815/ijmsc.2018.04.05, Pub. Date: 8 Nov. 2018

Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

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A Novel Optimization based Algorithm to Hide Sensitive Item-sets through Sanitization Approach

By T.Satyanarayana Murthy N.P.Gopalan Sasidhar Gunturu

DOI: https://doi.org/10.5815/ijmecs.2018.10.06, Pub. Date: 8 Oct. 2018

Association rule hiding an important issue in recent years due to the development of privacy preserving data mining techniques for hiding the association rules. One of the mostly used techniques to hide association rules is the sanitization of the database. In this paper, a novel algorithm MPSO2DT has been proposed based on the Particle Swarm Optimization (PSO) in order to reduce the side effects. The aim is to reduce the side effects such as Sensitive item-set hiding failure, Non-sensitive misses, extra item-set generations and Database dissimilarities along with the reduction of running time and complexities through transaction deletion.

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The Power of Anonymization and Sensitive Knowledge Hiding Using Sanitization Approach

By T.Satyanarayana Murthy N.P.Gopalan Datta Sai Krishna Alla

DOI: https://doi.org/10.5815/ijmecs.2018.09.04, Pub. Date: 8 Sep. 2018

In recent day’s huge rapid growth of corporate industries professional are based on the online marketing. These markets are associated with millions of online transactions which contain the details of the items, number of items, price and additional information like working details, salary information and personal information. The customers associated with these transactions are concerned about privacy issues. This manuscript aims to concentrates more on the additional information about the customer apart from dealing with the items. More analysis helps in knowing the sensitive information about an individual. In this article two algorithms were used, out of which first algorithm has been used to hide the sensitive information about an individual and other proposed algorithm has been used to hide the sensitive transaction information. These algorithms are proposed based on k-Anonymity and association rule hiding techniques. A novel algorithm has been proposed for association rule hiding algorithm to reduce the side effects such as Sensitive item-set hiding failure, Non-sensitive misses, extra item-set generations and Database dissimilarities along with the reduction of running time and complexities through transaction deletion.

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Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

By N.P.Gopalan Sivaiah Bellamkonda

DOI: https://doi.org/10.5815/ijigsp.2018.09.04, Pub. Date: 8 Sep. 2018

Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being.  Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities.  Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier. 

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A Novel Algorithm for Association Rule Hiding

By T.Satyanarayana Murthy N.P.Gopalan

DOI: https://doi.org/10.5815/ijieeb.2018.03.06, Pub. Date: 8 May 2018

Current days privacy concern about an individual, an organization and social media etc. plays a vital role. Online business deals with millions of transactions daily, these transactions may leads to privacy issues. Association rule hiding is a solution to these privacy issue, which focuses on hiding the sensitive information produces from online departmental stores ,face book datasets etc..These techniques are used to identify the sensitive rules and provide the privacy to the sensitive rules, so that results the lost rules and ghost rules. Algorithms developed so far are lack in achieving the better outcomes. This paper propose two novel algorithm that uses the properties from genetic algorithm and water marking algorithm for better way of hiding the sensitive association rules.

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An Analytical Study of Cellular Automata and its Applications in Cryptography

By G. Kumaresan N.P.Gopalan

DOI: https://doi.org/10.5815/ijcnis.2017.12.06, Pub. Date: 8 Dec. 2017

Security and confidentiality are the major concerns in information technology enabled services wherein data security, user authentication, industrial security and message authentication have a great deal of access to the world anywhere, anytime. The implication is: there is a need for efficient methods to secure digital data across different platforms. The concept of cellular automata finds application in the design of efficient methods to secure digital information. It is a recent field of research and its recognition has been on the rise with its high parallel structure and ability to design complex dynamic systems. In this paper, we study the basic concepts of different types of cellular automata and also discuss its applications in cryptography with various examples.

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EduCloud: A Dynamic Three Stage Authentication Framework to Enhance Security in Public Cloud

By G. Kumaresan N.P.Gopalan

DOI: https://doi.org/10.5815/ijem.2017.06.02, Pub. Date: 8 Nov. 2017

Now-a-days, one of the most exciting technology is cloud computing. Accessing dynamically virtualized resources through internet is called as cloud computing. Security and confidentiality are the major concerns in public cloud. Though EduCloud (Educational Cloud) uses public cloud, moving data from one location to another location may lead to risk. Information related to staff, student and management or admin that can be shared in EduCloud, are to be secured in public educational cloud environment. In this scenario, data security is the most critical issue in cloud. But present authentication system available does not provide enough security in public EduCloud. Hence, we propose new authentication framework to enhance security in public educational cloud. The features of various authentication techniques are discussed in this paper and a novel framework is proposed for pubic EduCloud, which provides not only security but also increases the response time. The developed software tool is best suited and provably a secured solution to the public educational cloud environment.

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Task Assignment for Heterogeneous Computing Problems using Improved Iterated Greedy Algorithm

By R.Mohan N.P.Gopalan

DOI: https://doi.org/10.5815/ijcnis.2014.07.07, Pub. Date: 8 Jun. 2014

The problem of task assignment is one of the most fundamental among combinatorial optimization problems. Solving the Task Assignment Problem is very important for many real time and computational scenarios where a lot of small tasks need to be solved by multiple processors simultaneously. A classic problem that confronts computer scientists across the globe pertaining to the effective assignment of tasks to the various processors of the system due to the intractability of the task assignment problem for more than 3 processors. Several Algorithms and methodologies have been proposed to solve the Task Assignment Problem, most of which use Graph Partitioning and Graph Matching Techniques. Significant research has also been carried out in solving the Task Assignment Problem in a parallel environment. Here we propose a modified version of iterated greedy algorithm that capitalizes on the efficacy of the Parallel Processing paradigm, minimizing the various costs along with the duration of convergence. The central notion of the algorithm is to enhance the quality of assignment in every iteration, utilizing the values from the preceding iterations and at the same time assigning these smaller computations to internal processors (i.e. parallel processing) to hasten the computation. On implementation, the algorithm was tested using Message Passing Interface (MPI) and the results show the effectiveness of the said algorithm.

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