Dr. Velmurugan Thambusamy
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Dr. Velmurugan Thambusamy

Associate Professor
Dwaraka Doss Goverdhan Doss Vaishnav College, India

Highest Degree
Ph.D. in Computer Science from University of Madras, Chennai, India

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Biography

Dr. T. Velmurugan is working as an Associate Professor in the PG and Research Department of Computer Science and Applications, D.G.Vaishnav College, Chennai-600106, India. Also, he is the Head of the Department of Computer Applications (BCA). He holds a Ph.D. degree in Computer Science from the University of Madras. He has 27 years of teaching experience. He has guided more than 300 M.Phil., Research Scholars in the field of Computer Science. He guided 9 Ph.D. scholars and currently guiding 8 Ph.D. scholars in the same field. He has published more than 100 articles indexed in SCOPUS and SCI such as Applied Soft Computing, Journal of Computer Science and etc. He elected and served as a Senate Member from Academic Council, University of Madras. He was an invited speaker and keynote speaker for many international conferences around the world. He served as a nominated Senate Member in the Middle East University, Dubai, UAE for a period of three years. He is a member in Board of studies for many autonomous institutions and Universities like Periyar University, Salem, and SCSVMV University, India. He hosted a lot of programmes in Doordharsan Television about recent topics in Information Technology. He was an Organizing Secretary of International Conference on Computing and Intelligence Systems (ICCIS 2015) and an international workshop on Applications of IoT. In addition, he was a resource person for various national workshops entitled "Scientific Research Article Writing and Journal Publications". Also, he hosted 22 webinars during this COVID 19 pandemic period. He is an editorial board member of 5 International Journals. He also a reviewer in many peer reviewed journals like Elsevier, Springer, IOSPress Journals etc. Further, he is a visiting faculty for M.Phil. course for various universities throughout India. His H index is 16 and i10 index is 20. His area of specialisation includes Data Mining, Artificial Intelligence, Machine Learning, Network Security, Big Data Analytics, Data Science and etc. He hosted more than 30 webinars during this lockdown period.

Area of Interest:

Computer Sciences
100%
Artificial Intelligence
62%
Image Processing
90%
Network Security
75%
Data Mining
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
80
Abstracts
40

Selected Publications

  1. Velmurugan,T. and T. Indhumathy, 2020. Comparative study on prediction of support and resistance levels with k-nearest neighbor and long short-term memory methods. J. Inf. Comput. Sci., 10: 243-252.
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  2. Velmurugan, T. and T. Indhumathy, 2020. Predicting support and resistance indicators for stock market with fibonacci sequence in long short-term memory. J. Comput. Sci., 16: 1428-1438.
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  3. Velmurugan, T. and S. Karthiga, 2020. Security based approach of SHA 384 and SHA 512 algorithms in cloud environment. J. Comput. Sci., 16: 1439-1450.
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  4. Velmurugan, T. and B. Hemalatha, 2020. Mining implicit and explicit rules for customer data using natural language processing and apriori algorithm. Int. J. Adv. Sci. Technol., 29: 3155-3167.
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  5. SriPradha, G., P. Kumaragurudasan and T. Velmurugan, 2020. Factors influencing the impact of technological innovations on localized adult education. J. Crit. Rev., 7: 2601-2606.
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  6. Karthiga S. and T. Velmurugan, 2020. Enhancing security in cloud computing using playfair and ceasar cipher in substitution techniques. International Journal of Innovative Technology and Exploring Engineering 9: 912-920.
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  7. Hemalatha B. and T. Velmurugan, 2020. Impact of customer feedback system using machine learning algorithms for sentiment mining. International Journal of Innovative Technology and Exploring Engineering 9: 1475-1483.
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  8. Velmurugan T. and E. Venkatesan, 2019. A hybrid multifarious clustering algorithm for the analysis of memmogram images. Journal of Computer and Communications 07: 136-151.
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  9. Thambusamy, V. and L. Umasankar, 2019. Prediction of heart disease using name entity recognition based on back propagation and whale optimization algorithms. Int. J. Innovative Technol. Exploring Eng., 8: 437-443.
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  10. Thambusamy V. and N. Srinivasan, 2019. A comprehensive analysis of simulated results for the performance of aodv and tora routing protocols in mobile ad-hoc networks. Journal of Computer Science 15: 582-593.
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  11. Sukassini, M.P. and T. Velmurugan, 2019. Ascertaining abnormal regions in mammogram images using gravitational search local map view technique. Int. J. Innovative Technol. Exploring Eng., 8: 1861-1868.
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  12. Saravananathan K. and T. Velmurugan, 2019. Quality based analysis of clustering algorithms using diabetes data for the prediction of disease. International Journal of Innovative Technology and Exploring Engineering 8: 448-452.
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  13. Rajamani A. and V. Thambusamy, 2019. Preprocessing the groundwater quality data by lsr and qdr techniques. International Journal of Innovative Technology and Exploring Engineering 9: 2636-2644.
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  14. Hemalatha B. and T. Velmurugan, 2019. Direct-indirect association rule mining for online shopping customer data using natural language processing. International Journal of Recent Technology and Engineering 8: 11099-11106.
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  15. Velmurugan, T., 2018. A state of art analysis of telecommunication data by k-means and k-medoids clustering algorithms. J. Comput. Sci. Commun., 6: 190-202.
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  16. Perumal, S. and T. Velmurugan, 2018. Preprocessing by contrast enhancement techniques for medical images. Int. J. Pure Applied Math., 118: 3681-3687.
  17. Perumal, S. and T. Velmurugan, 2018. Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization. Int. J. Eng. Technol., 7: 74-79.
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  18. Naveen A. and T. Velmurugan, 2018. Clustering techniques on brain mri. Indian Journal of Public Health Research & Development 9: 430-435.
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  19. Arunachalam, A.S. and T. Velmurugan, 2018. Measures for predicting success factors of elearning in educational institutions. Int. J.Pure Applied Math., 118: 3673-3678.
  20. Arunachalam A.S. and T. Velmurugan, 2018. Analyzing student performance using evolutionary artificial neural network algorithm. Int. J. Eng. Technol., 7: 67-73.
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  21. Manimaran, J. and T. Velmurugan, 2017. Evaluation of lexicon-and syntax-based negation detection algorithms using clinical text data. Bio-Algorithms Med-Syst., 13: 201-213.
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  22. Mahalakshmi, S. and T. Velmurugan, 2017. Segmentation of mri brain images: A comparative analysis. Int. J. Control Theory Applic., 10: 161-172.
  23. Karthiga, S. and T. Velmurugan, 2017. A survey on the security issues of software defined networking tool in cloud computing. Int. J. Bus. Intell., 6: 27-33.
  24. Govindasamy, K. and T. Velmurugan, 2017. A study on classification and clustering data mining algorithms based on students academic performance prediction. Int. J. Control Theory Applic., 10: 147-160.
  25. Deepalakshmi, S. and T. Velmurugan, 2017. A clustering and genetic algorithm based feature selection (CLUST-GA-FS) for high dimensional data. Int. J. Control Theory Applic., 10: 63-76.
  26. Arunkumar, R. and T. Velmurugan, 2017. A survey on the analysis of dissolved oxygen level in water using data mining techniques. Int. J. Data Min. Tech. Appl., 6: 43-51.
  27. Anuradha, C., T. Velmurugan and R. Anandavally, 2017. Clustering algorithms in educational data mining: A review. Int. J. Power Control Comput., 708: 47-52.
  28. Venkatesan, E. and T. Velmurugan, 2016. Extraction of cancer affected regions in mammogram images by clustering and classification algorithms. Indian J. Sci. Technol. 10.17485/ijst/2016/v9i30/93851.
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  29. Velmurugan, T. and S. Mahalakshmi, 2016. Efficiency of fuzzy C means algorithm for brain tumor segmentation in MR brain images. Int. J. Eng. Technol., 8: 2979-2989.
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  30. Velmurugan, T. and M.P. Sukassini, 2016. Segmentation of mammogram image using multilevel threshold and gravitational search algorithm. Int. J. Control Theory Applic., 9: 67-75.
  31. Velmurugan, T. and E. Venkatesh, 2016. Effective fuzzy C means algorithm for the segmentation of mammogram images of identifying breast cancer. Int. J. Control Theory Applic., 9: 4647-4660.
  32. Velmurugan, T. and A. Naveen, 2016. Analysing mri brain images using fuzzy c–means algorithm. Int. J. Control Theory Appl., 9: 4661-4675.
  33. Saravananathan, K. and T. Velmurugan, 2016. Analyzing diabetic data using classification algorithms in data mining. Indian J. Sci. Technol. 10.17485/ijst/2016/v9i43/93874.
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  34. Perumal, S. and T. Velmurugan, 2016. Image segmentation based survey on the lung cancer MRI images. Int. J. Data Min. Tech. Applic., 52: 172-177.
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  35. Pazhanivel, M. and T. Velmurugan, 2016. Technology enabled learning to improve student performance: A survey. Int. J. Data Min. Tech. Applic., 5: 50-56.
  36. Padmapriya, B. and T.Velmurugan, 2016. Classification algorithm based analysis of breast cancer data. Int. J. Data Min. Tech. Applic., 5: 43-49.
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  37. Navitha, S. and T. Velmurugan, 2016. Simulation model based performance analysis of DSR and TORA routing protocols in MANET. Int. J. Commun. Networking Syst., 5: 90-95.
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  38. Naveen, A. and T. Velmurugan, 2016. A novel layer based logical approach (LLA) clustering method for performance analysis in medical images. Int. J. Control Theory Applic., 9: 99-109.
  39. Mahalakshmi, S. and T. Velmurugan, 2016. A novel approach to find tumor in MRI brain images using image segmentation techniques. Int. J. Control Theory Appl., 9: 43-55.
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  40. M. Pazhanivel and T. Velmurugan, 2016. Technology enabled learning to improve student performance: a survey. International Journal of Data Mining Techniques and Applications 5: 50-56.
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  41. K. Govindasamy and T. Velmurugan, 2016. A survey on the result based analysis of student performance using data mining techniques. International Journal of Data Mining Techniques and Applications 5: 91-95.
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  42. Govindasamy, K. and T. Velmurugan, 2016. A survey on the result based analysis of student performance using data mining techniques. Int. J. Data Min. Tech. Applic., 5: 91-95.
  43. Dharmarajan, A. and T. Velmurugan, 2016. Efficiency of k-means and k-medoids clustering algorithms using lung cancer dataset. Int. J. Data Min. Tech. Applic., 5: 150-156.
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  44. Deepalakshmi, S. and T. Velmurugan, 2016. Empirical study of feature selection methods for high dimensional data. Indian J. Sci. Technol. 10.17485/ijst/2016/v9i39/90599.
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  45. Arunachalam, A.S. and T. Velmurugan, 2016. A survey on educational data mining tools and techniques. Int. J. Data Min. Tech. Applic., 5: 167-171.
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  46. Anuradha, C. and T.Velmurugan, 2016. Performance evaluation of feature selection algorithms in educational data mining. Int. J. Data Min. Tech. Applic., 5: 131-139.
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  47. Anuradha, C. and T. Velmurugan, 2016. Fast boost decision tree algorithm: A novel classifier for the assessment of student performance in educational data. Ciencia e Tecnica, 31: 139-155.
  48. Venkatesan, E. and T. Velmurugan, 2015. Prediction of tumor in classifying mammogram images by k-means, J48 and CART algorithms. Int. J. Data Mining Tech. Applic., 4: 29-34.
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  49. Venkatesan, E. and T. Velmurugan, 2015. Performance analysis of decision tree algorithms for breast cancer classification. Indian J. Sci. Technol., Vol. 8. 10.17485/ijst/2015/v8i1/84646.
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  50. Venkatesan, E. and T. Velmurugan, 2015. Performance analysis of decision tree algorithms for breast cancer classification. Indian J. Sci. Technol. 10.17485/ijst/2015/v8i1/84646.
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  51. Velmurugan, T., 2015. Efficiency of K-means and K-medoids algorithms for clustering arbitrary data points. Int. J. Comput. Techno. Appl., 3: 1758-1764.
  52. Velmurugan T. and S. Navitha, 2015. Simulation model based performance analysis of dsr and tora routing protocols in manet. International Journal of Communication and Networking System 5: 90-95.
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  53. Sukassini, M.P. and T. Velmurugan, 2015. A survey on the analysis of segmentation techniques in mammogram images. Indian J. Sci. Technol., Vol. 8. 10.17485/ijst/2015/v8i22/79105.
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  54. Sekar, G. and T. Velmurugan, 2015. A novel framework CACTO- subject to non-relational data stores. Cienc. Tec., 30: 124-141.
  55. Navitha, S. and T. Velmurugan, 2015. A survey on the simulation models and results of routing protocols in mobile Ad-hoc networks. Int. J. Commun. Networking Syst., 4: 55-61.
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  56. Naveen, A. and T. Velmurugan, 2015. Identification of calcification in MRI brain images by k-means algorithm. Indian J. Sci. Technol., Vol. 8. 10.17485/ijst/2015/v8i29/83379.
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  57. Manimaran, J. and T. Velmurugan, 2015. Analysing the quality of association rules by computing an interestingness measures. Indian J. Sci. Technol., Vol. 8. 10.17485/ijst/2015/v8i15/76693.
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  58. Mahalakshmi, S. and T. Velmurugan, 2015. Detection of brain tumor by particle swarm optimization using image segmentation. Indian J. Sci. Technol., 8: 1-7.
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  59. Mahalakshmi, S. and T. Velmurugan, 2015. Detection of brain tumor by particle swarm optimization using image segmentation. Indian J. Sci. Technol. 10.17485/ijst/2015/v8i22/79092.
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  60. Latha, U. and T. Velmurugan, 2015. Effective approaches of classification algorithms for text mining applications. Int. J. Data Min. Tech. Appl., 4: 103-107.
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  61. Dharmarajan, A. and T. Velmurugan, 2015. Lung cancer data analysis by k-Means and farthest first clustering algorithms. Indian J. Sci. Technol., Vol. 8. 10.17485/ijst/2015/v8i15/73329.
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  62. Anuratha, C. and T. Velmurugan, 2015. A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian J. Sci. Techno., 10.17485/ijst/2015/v8i15/74555.
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  63. Anuradha, C. and T. Velmurugan, 2015. A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian J. Sci. Technol., Vol. 8. .
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  64. Velmurugan, T., 2014. Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Applied Soft Comput., 19: 134-146.
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  65. Velmurugan, T., 2012. Performance comparison between k-means and fuzzy c-means algorithms using arbitrary data points. Wulfenia J., 19: 234-241.
  66. Velmurugan, T., 2012. Efficiencyofk-meansandk-medoidsalgorithmsforclusteringarbitrarydatapoints. Int. J. Comput. Techno. Appl., 3: 1758-1764.
  67. Velmurugan, T. and T. Santhanam, 2011. A survey of partition based clustering algorithms in data mining: An experimental approach. Inform. Technol. J., 10: 478-484.
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  68. Velmurugan, T. and T. Santhanam, 2011. A comparative analysis between K-medoids and fuzzy C-means clustering algorithms for statistically distributed data points. J. Theor. Applied Inf. Technol., 27: 19-30.
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  69. Velmurugan, T. and T. Santhanam, 2010. Performance evaluation of k-means and fuzzy c-means clustering algorithms for statistical distributions of input data points. Eur. J. Scient. Res., 46: 320-330.
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  70. Velmurugan, T. and T. Santhanam, 2010. Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci., 6: 363-368.
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  71. Velmurugan, T. and T. Santhanam, 2010. Clustering mixed data points using fuzzy C-means clustering algorithm for performance analysis. Int. J. Comput. Sci. Eng., 2: 3100-3105.
  72. Velmurugan, T. and T. Santhanam, 1992. A comparative analysis between k-medoids and fuzzy c-means clustering algorithms for statistically distributed data points. J. Theor. Applied Inform. Technol., 27: 19-30.