Qualification: 
M.Tech
Email: 
c_arunkumar@cb.amrita.edu

Arun Kumar C. currently serves as Assistant Professor at the Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore Campus. His areas of research include Bioinformatics and Machine Learning. In addition to the academic activities, to name some of the activities carried out under his leadership,

  • Vice Chairman - Council of Wardens, Hostels of Amrita Vishwa Vidyapeetham, Coimbatore
  • Co-Convenor - ANOKHA
  • State level IETE student Forums coordinator
  • Reviewer board for Elsevier and Springer journals
  • Secretary General of VIT Alumni Association which has 50000 members for 2014-16
  • Charter President, VIT Alumni Association Coimbatore Chapter
  • Recipient of the Most Active Domestic Chapter Award of VIT Alumni Association for 2013-2014 during my tenure as President
  • Member – Technical Program Commitee, GS Multi International Conference on Science and Technology,November 2014, Dubai,UAE
  • Member – Machine Intelligence Research Labs, USA.
  • Chief Coordinator – IEEE All India Student Congress, 3-6 October 2013
  • Chief Coordinator – IEEE Face to Face Meet, April 2013
  • Program Director – Infosys Campus Connect during 2007-2008
  • Executive Secretary for the International Conference on Cloud Computing, Smart Grid and Green I.T 2009
  • Has coordinated at the department level for the Institution’s NAAC accreditation and the institution obtained the highest “A” grade in 2014.
  • Former Member of the Rotary club of Pollachi and has also received an award for offering the best of services to the club during 2007-08
  • Co-Convenor for the AICTE sponsored National seminar on Mobile and Network Security 2007

He is responsible for starting “Institution of Electronics and Telecommunication Engineers”(IETE) student forums in all engineering colleges and polytechnics in Tamil Nadu under the jurisdiction of IETE Coimbatore center which expands its wings to more than 12 districts in Tamil Nadu. He has also inaugurated student forums and delivered technical and non-technical lectures in more than 50 engineering colleges in Tamil Nadu including some of the premier institutions like Vellore Institute of Technology, PSG college of Technology, Coimbatore Institute of Technology and so on. He also assisted the Amrita Vishwa Vidyapeetham in conducting a one day tutorial in the area of Network Security in association with Symantec Corporation at IIT Madras during May 2010 as a part of IFIP-Networking 2010. He has also proctored and mentored the IEEE Extreme programming contest, a 24 hour online coding contest during October 2011 in which his team received the All India Ranking of 8 among 1500 teams that took part in the contest across the globe at the same time. IEEE Chapter of Amrita School of Engineering, Coimbatore won the Third prize in the IEEE Region 10(Asia Pacific Region) in the website design competition under his leadership. He was also appointed as the Organizing Secretary for the International Conference on Distributed Computing and Networking 2014 which is one of the top 10 conferences in the world to be hosted at Amrita in 2014. He is a life member of IETE and Machine Intelligence Research Labs, USA. He has published more than 15 papers in leading national and international conferences and journals.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

A. Chinnaswamy and Ramakrishna, S., “Hybrid Information Gain Based Fuzzy Roughset Feature Selection in Cancer Microarray Data”, in Proceedings of IEEE International Conference on Innovations in Power and Advanced Computing Technologies, Vellore Institute of Technology, Vellore, India, 2017.

2016

Conference Paper

A. Chinnaswamy and Ramakrishnan, S., “A hybrid approach to feature selection using correlation coefficient and fuzzy rough quick reduct algorithm applied to cancer microarray data”, in 10th International Conference on Intelligent Systems and Control (ISCO), 2016 , Karpagam College of Engineering, Coimbatore, India, 2016, vol. 15, pp. 199-210.[Abstract]


In this study, we applied a novel method by using correlation coefficient filter for dimensionality reduction followed by fuzzy rough quick reduct algorithm for feature selection. The classification performance was evaluated using the gene subsets obtained from correlation based filter and our proposed method. Later we compared the results with other traditional classifier techniques. After suitable experimental analysis, it has been found that our proposed method has a two-fold advantage namely selection of much lesser number of genes compared to correlation coefficient and improved classifier accuracy in majority of the cases. This approach also reduces the number of misclassifications that might occur in other approaches.

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2015

Conference Paper

A. Chinnaswamy and Srinivasan, R., “Hybrid feature selection using correlation coefficient and particle swarm optimization on microarray gene expression data”, in Proceedings of the 6th International Conference in Bioinspired Computing and Applications, Advances in Intelligent Systems and Computing, ToC H Institute of Science and Technology, Kochi, India, 2015.[Abstract]


Diagnosis of cancer is one of the most emerging clinical applications in microarray gene expression data. However, cancer classification on microarray gene expression data still remains a difficult problem. The main reason for this is the significantly large number of genes present relatively compared to the number of available training samples. In this paper, we propose a hybrid feature selection approach that combines the correlation coefficient with particle swarm optimization. The process of feature selection and classification is performed on three multi-class datasets namely Lymphoma, MLL and SRBCT. After the process of feature selection is performed, the selected genes are subjected to Extreme Learning Machines Classifier. Experimental results show that the proposed hybrid approach reduces the number of effective levels of gene expression and obtains higher classification accuracy and uses fewer features compared to the same experiment performed using the traditional tree-based classifiers like J48, random forest, random trees, decision stump and genetic algorithm as well.

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2014

Conference Paper

A. Chinnaswamy and Ramakrishnan, S., “Binary Classification of cancer microarray gene expression data using extreme learning machines”, in IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2014 , 2014.[Abstract]


This paper presents the usage of Extreme Learning Machines for cancer microarray gene expression data. Extreme Learning Machines overcomes the problems of overfitting, local minima and improper training rate that are most common in traditional algorithms. We have evaluated the binary classification performance of Extreme Learning Machines on five bench marked datasets of cancer microarray gene expression data namely ALL/AML, CNS, Lung Cancer, Ovarian Cancer and Prostate Cancer. Feature Extraction has been performed using Correlation Coefficient prior to classification. The results indicate that ELM produces comparable or better results compared to the traditional classification methods like Naïve Bayes, Bagging, Random Forest and Decision Table.

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2013

Conference Paper

A. Chinnaswamy and Ramakrishna, S., “Analysis of Classification Techniques for Microarray Cancer data using Support Vector Machines”, in Proceedings of International Conference on Bio-Signals, Images and Instrumentation, SSN College of Engineering, Chennai, India, 2013.

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

A. Chinnaswamy and Ramakrishnan, S., “A comparative study of hybrid feature selection methods using correlation coefficient for microarray data”, Journal of Network and Innovative Computing, vol. 4, no. 1, pp. 164–174, 2016.[Abstract]


Feature selection is a key challenge before the process of classification could be performed. The classification accuracy would increase by using a good feature selection method and also at the same time reduces the cost and time involved in the computation. In this study, we applied hybrid methods by using Correlation Based Feature Selection combined with different search algorithms. The classification performance was evaluated using fuzzy rough neural network classifier on the selected gene subsets. The experimental results reveal that majority of the hybrid method selects very few gene subsets and produces much better classification accuracy. The results are validated using traditional approaches like Precision, Recall, F-Score and Region of Characteristic.

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2016

Journal Article

A. Chinnaswamy and Ramakrishnan, S., “Modified Fuzzy Rough Quick Reduct Algorithm for Feature Selection in Cancer Microarray Data”, Asian Journal of Information Technology, vol. 15, no. 2, pp. 199–210, 2016.[Abstract]


This study proposes a novel method that employs correlation based filter for dimensionality reduction followed by fuzzy rough quick reduct for feature selection on a particle swarm optimization search space. The first phase removed the redundant genes using correlation coefficient filter on a particle swarm optimization search space. The second phase produced a fuzzy rough quick reduct that would be used for classification. The genes obtained after feature selection are subjected to classification using traditional classifiers. It has been determined that the proposed method contributes to reduction in the total number of genes and improvement in the classifier accuracy compared to gene selection and classification using correlation coefficient and traditional fuzzy rough quick reduct algorithm. This approach also reduces the number of misclassifications that might occur in other approaches.

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2015

Journal Article

A. Chinnaswamy and .Ramakrishnan, S., “A Comparative Study of different Classifiers on Microarray Cancer Gene Expression Data”, Australian Journal of Basic and Applied Sciences, no. 27, pp. 145-151, 2015.

2014

Journal Article

A. Chinnaswamy and Ramakrishnan, S., “Two Step Feature Extraction Method for Microarray Cancer Data using Support Vector Machines”, International Journal of Computer Applications, vol. 85, no. 8, pp. 34-42, 2014.[Abstract]


Diagnosis of cancer is one of the most emerging clinical applications in microarray gene expression data. However, cancer classification on microarray gene expression data still remains a difficult problem. The main reason for this is the significantly large number of genes present relatively compared to the number of available training samples. In this paper, a novel approach to feature extraction combining the statistical t-test and absolute scoring is proposed for achieving better classification rate. Suitable classification approaches using the linear Support Vector Machines, the Proximal Support Vector Machines and the Newton Support Vector Machines is also discussed. A comparative analysis on the different techniques for feature extraction is also presented. Microarray cancer data based on Adenoma and Carcinoma with 7086 and 7457 genes of 4 and 18 patients respectively is used for this study. Increase in the classification rate of the proposed new method is clearly demonstrated in the results.

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Faculty Research Interest: 
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