1. V. Sugumaran, V. Muralidharan and K.I. Ramachandran, Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing, Mechanical Systems and Signal Processing, Volume 21, Issue 2, February 2007, Pages 930-942. It stood in 16th position of top 25 articles in Mechanical System Signal Processing.
2. V. Sugumaran and K.I. Ramachandran, Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing, Mechanical Systems and Signal Processing, Volume 21, Issue 5, July 2007, Pages 2237-2247. It stood in 5th position of top 25 articles in Mechanical System Signal Processing
3. Sugumaran, V.; Sabareesh, GR; Ramachandran, KI; Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine, Expert Systems with Applications,34, No. 4, pp. 3090-3098, 2008, Elsevier.
4. Indira, V.; Vasanthakumari, R.; Sugumaran, V.; Minimum sample size determination of vibration signals in machine learning approach to fault diagnosis using power analysis, Expert Systems With Applications,37, No. 12, pp. 8650-8658, 2010, Elsevier.
5. Kumar, R.A.; Sugumaran, V.; Gowda, BHL; Sohn, CH; Decision tree: A very useful tool in analysing flow-induced vibration data, Mechanical Systems and Signal Processing22, No. 1, pp. 202-216, 2008, Elsevier.
6. Radhika, S.; Sabareesh, GR; Jagadanand, G.; Sugumaran, V.; Precise wavelet for current signature in 3Ï• IM, Expert Systems with Applications37, No. 1, pp. 450-455, 2010, Elsevier.
7. Sugumaran, V.; Ramachandran, KI; Wavelet Selection Using Decision Tree for Fault Diagnosis of Roller Bearings, International Journal of Applied Engineering Research4, No. 2, pp. 201-225, 2009.
8. Sugumaran, V.; Ajith Kumar, R.; Gowda, BHL; Sohn, CH; Safety analysis on a vibrating prismatic body: A data-mining approach, Expert Systems with Applications36, No. 3, pp. 6605-6612, 2009, Elsevier.
9. Elangovan, M.; Ramachandran, KI; Sugumaran, V.; Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features, Expert Systems with Applications37, No. 3, pp. 2059-2065, 2010, Elsevier.
10. Elangovan, M.; Sugumaran, V.; Ramachandran, KI; Ravikumar, S.; Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool, Expert Systems with Applications, No. , pp. , 2011, Elsevier.
11. Devasenapati, S.B.; Sugumaran, V.; Ramachandran, KI; Misfire identification in a four-stroke four-cylinder petrol engine using decision tree, Expert Systems With Applications37, No. 3, pp. 2150-2160, 2010, Elsevier.
12. V. Indira, R. Vasanthakumari, N.R.Sakthi vel. Sugumaran, V..; A Method for Minimum Sample Size Calculation for a Classification Problem in Fault Diagnosis of Centrifugal Pump, International Journal of Statistics and Systems (IJSS)5, No. 2, pp. 183-202, 2010, Research India Publication.
13. V. Muralidharan, V. Sugumaran, S. Bharath kumar hedge; Fault Diagnosis of Centrifugal Pump Using Haar Wavelet Transform, International Journal of Applied Engineering Research5, No. 13, pp. 2307-2316, 2010, Research India Publications.
14. V. Muralidharan, V. Sugumaran, P.Shanmugam, K.Sivanathan.; Artificial Neural Network based Classification for Monoblock Centrifugal Pump using Wavelet Analysis, International Journal of Mechanical Engineering & Technology1, No. 1, pp. 28-37, 2010, IAEME.
15. Manju B R, A.R. Rajan, Sugumaran, V.; Wavelet Design for Fault Diagnosis of Roller Bearings using Continuous Wavelet Transforms, International Journal of Mechanical Engineering and Technology2, No. 2, pp. 70-84, 2011, IAEME.
16. Babu Devesenapati. S.; KI, Ramachandran.V.Sugumaran.; Misfire Detection in a Spark Ignition Engine using Support Vector Machines,International Journal of Computer Applications IJCA5, No. 6, pp. 25-29, 2010, Foundation of Computer Science FCS.
17. Balamuruga Mohan Raj.G, Sugumaran, V..; Prediction of Work Piece Hardness using Artificial Neural Network, International Journal of Design and Manufacturing Technology,1, No. 1, pp. 29-44, 2010, IAEME.
18. Sugumaran, V., G.B.Mohan raj.; Analysis of Tool Life Data in Linear Regression Analysis Method and Least Meadian Square Method, International Journal of Engineering Research and Technology, 3, No. 3, pp. 109-118, 2010, Research India publications.
19. V. Sugumaran, V. Muralidharan, Bharath Kumar Hegde, Ravi Teja .C.; Intelligent Process Selection for NTM - A Neural Network Approach, INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD)1, No. 1, pp. 84-93, 2010, IAEME.
20. Ravikumar, S.; Ramachandran, KI; Sugumaran, V.; Machine learning approach for automated visual inspection of machine components, Expert Systems With Applications38, No. 4, pp. 3260-3266, 2011, Elsevier.
21. Sugumaran, V.; Ramachandran, KI; Effect of number of features on classification of roller bearing faults using SVM and PSVM, Expert Systems with Applications38, No. 4, pp. 4088-4096, 2011, Elsevier.
22. Sugumaran, V.; Ramachandran, KI; Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning, Expert Systems with Applications38, No. 5, pp. 4901-4907, 2011, Elsevier.
23. V. Muralidharan, V. Sugumaran, G. Pandey..; SVM Based Fault Diagnosis Of Monoblock Centrifugal Pump Using Stationary Wavelet Features, International Journal of Design And Manufacturing Technology (IJDMT)2, No. 1, pp. 41280, 2011, IAEME.
24. Manju B R, V. Sugumaran, K.I.R.; A New Wavelet Feature for Fault Diagnosis of Roller Bearings using Decision Tree, International Journal of Mechanical Engineering & Technology (IJMET)2, No. 2, pp. 70-84, 2011, IAEME.
25. V. Indira, R. Vasanthakumari, Sugumaran, V.; Sample Size Determination For Classification Of EEG Signals Using Power Analysis In Machine Learning Approach, International journal of Advanced Research in Engineering & Technology (IJARET)3, No. 1, pp. 41283, 2012, IAEME Publishers.
26. V. Muralidharan, Hemantha kumar, Sugumaran, V..; Fault Diagnosis Of Monoblock Centrifugal Pump Using Discrete Wavelet Features And J48 Algorithm, International Journal of Mechanical Engineering and Technology (IJMET)3, No. 1, pp. 120 - 126, 2012, IAEME Publishers.
27. V. Muralidharan, V. Sugumaran; A comparative study of Naive bayes and Bayes net classifier for Fault Diagnosis of Monoblock Centrifugal pump using wavelet analysis, Soft Computing, 12, No. 8, 2012, pp. 2023-2029, Elsevier.
28. Manju B R , A R Rajan, Sugumaran, V.; Optimizing the parameters of wavelets for pattern matching Using GA, International Journal of Advanced Research in Engineering and Technology (IJARET)3, No. 1, pp. , 2012, IAEME Publishers.
29. V V Ramalingam, S Ganesh kumar, Sugumaran, V.; Analysis of EEG signals using data mining approach,International Journal of Computer Engineering & Technology (IJCET)3, No. 1, pp. , 2012, IAEME Publishers.
30. Muralidharan, V.; Sugumaran, V.; A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis, Applied Soft Computing, No. , pp.206-212 , 2012, Elsevier.
31. Muralidharan, V.; Sugumaran, V.; Feature Extraction using Wavelets and Classification through Decision Tree Algorithm for Fault Diagnosis of Mono-Block Centrifugal Pump, Measurement,46, No.1, pp.353-359, 2012, Elsevier.
32. Sakthivel, NR; Sugumaran, V.; Babudevasenapati, S.; Vibration based fault diagnosis of monoblock centrifugal pump using decision tree, Expert Systems with Applications37, No. 6, pp. 4040-4049, 2010, Elsevier.
33. Sakthivel, NR; Sugumaran, V.; Nair, B.B.; Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for fault classification of monoblock centrifugal pump, International Journal of Data Analysis Techniques and Strategies2, No. 1, pp. 38-61, 2010, Inderscience (SRM).
34. Sakthivel, NR; Sugumaran, V.; Nair, B.B.; Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump, Mechanical systems and signal processing24, No. 6, pp. 1887-1906, 2010, Elsevier, (SRM).
35. Saimurugan, M.; Ramachandran, KI; Sugumaran, V.; Sakthivel, NR; Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine, Expert Systems with Applications38, No. 4, pp. 3819-3826, 2011, Elsevier, (SRM).
36. Sakthivel, NR; Nair, B.B.; Sugumaran, V.; Rai, R.S.; Decision support system using artificial immune recognition system for fault classification of centrifugal pump, International Journal of Data Analysis Techniques and Strategies3, No. 1, pp. 66-84, 2011, Inderscience, (SRM).
37. Sakthivel, NR; Indira, V.; Nair, B.B.; Sugumaran, V.; Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump, International Journal of Granular Computing, Rough Sets and Intelligent Systems2, No. 1, pp. 23-36, 2011, Inderscience, (SRM).
38. Sakthivel, NR; Nair, B.B.; Sugumaran, V.; Soft computing approach to fault diagnosis of centrifugal pump, Applied Soft Computing, Vol. 12, Issue 5, pp.1574–1581, 2012, Elsevier (VIT).
39. Muralidharan, V.; Sugumaran, V.; Sakthivel, NR; Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump, International Journal of Data Analysis Techniques and Strategies3, No. 2, pp. 159-177, 2011, Inderscience, (SRM).
40. Sakthivel, NR; Nair, B.B.; Sugumaran, V.; Roy, R.S.; Application of standalone system and hybrid system for fault diagnosis of centrifugal pump using time domain signals and statistical features, International Journal of Data Mining, Modelling and Management4, No. 1, pp. 74-104, 2012, Inderscience, (SRM).
41. Indira, V.; Vasanthakumari, R.; Sakthivel, NR; Sugumaran, V.; Determination of sample size using power analysis and optimum bin size of histogram features, International Journal of Data Analysis Techniques and Strategies3, No. 1, pp. 21-41, 2011, Inderscience, (SRM).
42. Indira, V.; Vasanthakumari, R.; Sakthivel, NR; Sugumaran, V.; A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump, Expert Systems with Applications38, No. 6, pp. 7708-7717, 2011, Elsevier, (SRM).
43. Muralidharan, V., Sugumaran V..; Selection of Discrete Wavelets for Fault Diagnosis of Monoblock Centrifugal Pump using the J48 Algorithm, Applied Artificial Intelligence, 27, Issue 1, Jan 2013, pp.1-19, (VIT).
44. Muralidharan, V., Sugumaran, V.; Fault Diagnosis of Centrifugal Pump using Wavelet Features - a Fuzzy Based Approach, International journal of computational system engineering, Vol. 1, No.3, pp.175-183, 2013, Inderscience Publishers (VIT).
45. Sakthivel, NR; Sugumaran, V.; Nair, B.B.; Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump, Applied Soft Computing12, No. 1, pp. 196-203, 2012, Elsevier, (VIT).
46. Amarnath M, Sugumaran V, H.; Exploiting Sound Signals for Fault Diagnosis of Bearings Using Decision Tree, Measurement, 46, No.3 , pp.1250-1256, 2012, Elsevier, (VIT).
47. Hemantha Kumar, T. A. Ranjit Kumar, M. Amarnath, Sugumaran, V.; Fault diagnosis of antifriction bearings through sound signals using support vector machine, Journal of Vibroengineering14, No. 4, pp. , 2012.
48. V. Sugumaran, C. Anand, K.I.R.; Study of Classification Efficiency of Discrete Wavelet Features using Support Vector Machines in Fault Diagnosis of Roller Bearings, Journal of Wavelet Theory and Applications, No. , pp. , Research India Publications.
49. Hemantha Kumar, T. A. Ranjit Kumar, M. Amarnath, Sugumaran, V.; Fault Diagnosis of Bearings through Vibration Signal Using Bayes Classifiers, International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 6, No. 1, 2014.
50. G. Balamuruga mohan raj, Sugumaran, V.; Regression and Least Median Square Analysis to Predict the Surface Roughness in Boring Operation, International Journal of Logistics and Supply Chain Management (IJLSCM), No. , pp. , 2012.
51. V. Muralidharan, Sugumaran, V.; Effect of Cascaded Classifiers and Wavelet Features for Fault Diagnosis of Monoblock Centrifugal Pump, International Journal of Operations Systems and Human Resource Management (IJOSHRM), No. , pp. , 2012.
52. V. Sugumaran, Deepak Jain, M. Amarnath and Hemantha kumar, Fault Diagnosis Of Helical Gear Box Using Decision Tree Through Vibration Signals, International journal of performability engineering, 9, No. 2, pp. 221-234, 2013.
53. R. Jegadeeshwaran, V. Sugumaran, Method and Apparatus for Fault Diagnosis of Automobile Brake System Using Vibration Signals, Recent Patents on Signal Processing, 3, No. 2, pp. 2-11, 2013, Bentham Science Publishers.
54. V. Muralidharan, V. Sugumaran, Selection of Discrete Wavelets for Fault Diagnosis of Monoblock Centrifugal Pump using the J48 Algorithm, Applied Artificial Intelligence: An International Journal, 27:1, 1-19, 2013.
55. Balamuruga Mohan Raj. G, Sugumaran. V, Developing Gaussian Process Model to Predict the Surface Roughness in Boring Operation, International Journal of Engineering Trends and Technology- Volume4Issue3- pp.219-223, 2013.
56. Abhishek Sharma, V. Sugumaran, S. Babu Devasenapati, Misfire Detection in an IC Engine Using Vibration Signal and Bagging Classifier, International Journal of Engineering Innovation & Research, Volume 2, Issue 4, pp. 386-390, Aug. 2013.
57. R. Jegadeeshwaran, V. Sugumaran, Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features, Measurement, 46, No. 9, 2013, pp. 3247–3260.
58. V. V. Ramalingam, S. Mohan, V. Sugumaran, A Comparison of EMG and EEG Signals for Prostheses Control using Decision Tree, International Journal of Research in Computer Applications & Information Technology, Vol. 1, Issue 1, pp. 01-08, 2013.
59. Kale Ajinkya Pravin, V. Sugumaran, Roller Bearing Fault Diagnosis by Decision Tree Algorithms with Statistical Feature, International Journal of Research in Mechanical Engineering, Volume 1, Issue 1, July-September, pp. 01-09, 2013.
60. Muralidharan, V; Sugumaran, V; Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of mono-block centrifugal pump, Measurement, 46, 9, 3057-3063, 2013, Elsevier.
61. M. Amarnath, Deepak Jain, V. Sugumaran and Hemantha Kumar, Fault Diagnosis of Helical Gear Box Using Decision Tree and Best-First Tree, International Journal of Research in Mechanical Engineering, Volume 1, Issue 1, July-September, pp. 22-33, 2013.
62. Anish Bahri, V.Sugumaran, S. Babu Devasenapati; Misfire Detection in IC Engine using Kstar Algorithm, International Journal of Research in Mechanical Engineering,1,1,103 - 110,2013,IASTER
63. Siddhant Sahu, V. Sugumaran; Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings, International Journal of Research in Mechanical Engineering,1,1,55-63,2013,IASTER
64. B. Rebecca Jeya Vadhanam, S. Mohan, V. Sugumaran; A Classification Performance on Multiple Frames of Advertisement and Non Advertisement Videos using C4.5 Algorithm, International Journal of Engineering & Technology Research,1,1,139 - 145,2013,IASTER
65. Vedant, V. Sugumaran, M. Amarnath and Hemantha Kumar; Fault Diagnosis of Helical Gear Box Using Sound Signal using Naïve Bayes and Bayes Net, International Journal of Engineering & Technology Research,1,1,98 - 105,2013,IASTER
66. M. Amarnath, Deepak Jain, V. Sugumaran and Hemantha Kumar; Fault Diagnosis of Helical Gear Box Using Decision Tree and Best-First Tree, International Journal of Research in Mechanical Engineering,1,1,22-33,2013,IASTER.
67. Piyush Chaskar, V. Sugumaran, Voice Command Wheelchair Navigation using K Star Classifier, International Journal of Research in Mechanical Engineering, Vol. 1, Issue 2, 73 – 79, IASTER.
68. Hemantha Kumar, T.A. Ranjit Kumar, M. Amarnath and V. Sugumaran, Fault diagnosis of bearings through vibration signal using Bayes classifiers, Int. J. Computer Aided Engineering and Technology, Vol. 6, No. 1, 2014.
69. Sanidhya Painuli, V. Sugumaran, M. Elangovan, Tool condition monitoring using k-star Algorithm, Expert Systems With Applications, Volume 41, Issue 6, May 2014, Pages 2638-2643.
70. A Sharma, V Sugumaran, S Babu Devasenapati, Misfire detection in an IC engine using vibration signal and decision tree algorithms, Measurements, Elsevier Journal, 2014, Volume 50, April 2014, Pages 370-380.
71. Sakthi vel,Binoy B Nair, M Elangovan, V. Sugumaran, S Saravana Murugan, Comparison of Dimensionality Reduction Techniques for the Fault Diagnosis of Mono block Centrifugal Pump Using Vibration Signals, Engineering Science and Technology: an International Journal.
72. Abhinav Aggarwal, V. Sugumaran, M. Amarnath, Hemantha Kumar; Fault Diagnosis of Helical Gear Box using Logistic Function and REP Tree, International Journal of Research in Mechanical Engineering, 2, 1, 26-32, 2014, IASTER
73. Hemantha Kumar, M. Amarnath and V. Sugumaran; Fault Diagnosis of Helical Gear Box using Large Margin K-Nearest Neighbors Classifier using Sound Signals, Journal of Vibration Engineering and Technologies, 2014.
74. Jegadeeshwaran, R; Sugumaran, V; Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines, Mechanical Systems and Signal Processing, Vol. 52-53, 2015
75. R. Jegadeeshwaran and V.Sugumaran,; Condition Monitoring of a Hydraulic Brake System Using Sequential Minimal Optimization (SMO) Algorithm, International Journal of Engineering Research & Management, 2014.
76. R. Jegadeeshwaran, V. Sugumaran, Brake Fault Diagnosis using Clonal Selection Classification Algorithm (CSCA) - A Statistical Learning Approach, Engineering Science and Technology, an International Journal.
77. R. Jegadeeshwaran, V. Indira and V. Sugumaran, Determination of minimum sample size for fault diagnosis of automobile hydraulic brake system using Power Analysis, Engineering Science and Technology: an International Journal, 18 (1), 59-69, 2015. (Elsevier journal)
78. R. Jegadeeshwaran, V. Sugumaran, Vibration based fault diagnosis study of an automobile Brake system using k star (k*) algorithm – a statistical approach, Recent patents in signal processing, accepted for publication.
79. V. Muralidharan, V. Sugumaran, V. Indira; Fault Diagnosis of Monoblock Centrifugal pump using SVM, Engineering Science and Technology: an International Journal, 1, 1, 2014, Elsevier.
80. N. Gangadhar, Hemantha Kumar, S. Narendranath, V. Sugumaran, Fault Diagnosis of Single Point Cutting Tool through Vibration Signal Using Decision Tree Algorithm, Procedia Materials Science, Volume 5, 2014, Pages 1434–1441.
81. A. Kannan, V. Sugumaran, Amarnath, K.P. Soman, Fault diagnosis of helical gear box using variational mode decomposition with naïve Bayes and Bayes net classifiers through vibration signals, International Journal of Manufacturing Systems and Design, Accepted on 30.10.14.
82. V. Sabarinathan and V. Sugumaran, Diagnosis of Heart disease using Decision Tree, International Journal of Research in Computer applications and Information Technology, Vol. 2, Issue 6, PP. 74-49, IASTER.
83. Akhil Muralidharan, V. Sugumaran, K.P. Soman, Bearing fault diagnosis using vibration signals by variational mode decomposition and Naïve Bayes classifier, International Journal of Automation, Issue 1, Vol. 1, 1-14, 2015.
84. R. Jegadeeshwaran, V. Sugumaran, A Comparative study of Naïve Bayes Classifier and Bayes net Classifier for fault diagnosis of Automobile hydraulic brake system, International Journal of Decision support systems, Vol. 1, Issue 1, 2015.
85. M. Amarnath, Deepak Jain, V. Sugumaran, R. Hemantha Kumar, Fault Diagnosis of Helical Gear Box Using Naïve Bayes And Bayes Net, International Journal of Decision Support Systems, Vol. 1, Issue 1, PP. 4-17, 2015 Inderscience publisher.
86. V. Muralidharan, N.R. Sakthivel and V.Sugumaran, Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm, Asian Journal of Science and Applied Technology, Vol.3, Issue. 3, pp. 1-4, 2015.
87. R. Jegadeeshwaran V. Sugumaran and K. P. Soman, Vibration Based Fault Diagnosis Of A Hydraulic Brake System Using Variational Mode Decomposition (VMD), SDHM: Structural Durability & Health Monitoring, accepted for publication on 18.2.2015.
88. Shalet K S, Jegadeeshwaran R, Sugumaran V, Elangovan M; Condition Monitoring of Single Point Cutting Tool Using ARMA Features And SVM Classifiers, International Journal of Applied Engineering Research, (IJAER), 2015.
89. Akhil Muralidharan, V. Sugumaran, K.P Soman, M. Amarnath, Fault Diagnosis Of Helical Gear Box Using Variational Mode Decomposition And Random Forest Algorithm, SDHM: Structural Durability & Health Monitoring, Accepted on 19.03.2015.
90. Ragul Kumar Sharma, V Sugumaran, H Kumar, M Amarnath, A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal, International Journal of Decision Support Systems 1 (1), 115-129, 2015.
91. Aditya V Rao, V. Sugumaran, K. I. Ramachandran, A Comprehensive Study of Fault Diagnostics of Roller Bearings Using Continuous Wavelet Transform, International Journal of Manufacturing System and Design 1 (1), 1-20, 2015.
92. V.Sugumaran, M. Elangovan, N.R.Sakthivel, S.Saravanamurugan, Binoy.B.Nair, Machine Learning Approach to the Prediction of Surface Roughness using Statistical Features of Vibration Signal Acquired in Turning, Procedia Computer Science 50, 282-288, Elsevier, 2015.
93. V.Sugumaran, R. Jegadeeshwaran, Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach, International Journal of Prognostics and Health Management 6 (1(014)), 10, 2015.
94. V.Sugumaran, R. Jegadeeshwaran, A comparative study of navie Bayes classifier and Bayes net classifier for fault diagnosis of Automobile Hydraulic brake system, Internation Journal of Decision Support System 1 (3), 247-267, 2015.
95. V.Sugumaran, R. Jegadeeshwaran, Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines, Mechanical Systems and Signal Processing 52, 436-446, 2015, Elsevier.
96. V Sugumaran, Aditya V Rao, KI Ramachandran, A Comprehensive Study of Fault Diagnostics of Roller Bearings Using Continuous Wavelet Transform, International Journal of Manufacturing Systems and Design 1 (1), 27-46, 2015.
97. V.Sugumaran, R. Jegadeeshwaran, Fuzzy classifier with automatic rule generation for fault diagnosis of hydraulic brake system using statistical features, International Journal of Fuzzy Computation and Modelling 1 (3), 333-350, 2015.
98. V Sugumaran, V. Sabarinathan, K. Rajesh Khanna, V. J. Sarath Kumar, A Comparative Study on Naïve Bayes and Bayes Net Classifiers for the Heart Disease Prediction System, International Journal of Mechanical Handling and Automation 1 (1), 1-7, 2015.
99. Hemanth Mithun Praveen, V. Sugumaran, Harvesting Vertical Vibration Of Automotive Tyre To Monitor Tyre Pressure Using Applied Machine Learning Technique, International Journal of Applied Engineering Research (IJAER), Accepted for publication on 30 June 2015.
100. Sathish kumar V Sugumaran; remaining life time prediction of bearings through classification using decision tree algorithm , International Journal Of Applied Engineering Research (IJAER) Vol.10, 2015 rip
101. V. Sugumaran and A. Joshuva; speech recognition for humanoid robot, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.57-60 2015 rip scopus indexed
102. Rajesh kanna. K Sabarinathan. V Sarath kumar. V. J and Sugumaran. V; eye state prediction using eeg signal and c4.5 decision tree algorithm, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.167-171 2015 rip scopus indexed
103. V. Sugumaran and A. Arjun; human tracking system, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp. 181-184 2015 rip scopus indexed
104. V. Sugumaran V. Vipin; gesture recognition system for dynamic user interface , International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.185-190 2015 scopus indexed
105. B. Rebecca jeya vadhanam S. Mohan and V. Sugumaran; classification of advertisement and non-advertisement videos with bicc features using csca algorithm , International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.191-196 2015 rip scopus indexed
106. A. Ramu, V. Sugumaran and M. Amarnath; fault diagnosis of gear using histogram features of vibration signals and classifier ensemble, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.234-238 2015 rip scopus indexed
107. V. Sugumaran A. Ramu.; fabrication and actuation of humanoid robot hand, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.318-321 2015 rip scopus indexed
108. V. Sugumaran and R. Jegadeeshwaran; a comparative study of tree family classifiers for fault diagnosis of an automobile hydraulic brake system using statistical features, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.322-327 2015 rip scopus indexed
109. K. Balachandar, V. Sugumaran, R. Jegadeeshwaran and M.Amarnath; fault diagnosis of bearing using sound signals through histogram features and decision tree, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68, pp.476-481 2015 rip scopus indexed
110. Joshuva, V. Sugumaran and M. Amarnath; Selecting Kernel Function Of Support Vector Machine For Fault Diagnosis Of Roller Bearings Using Sound Signals Through Histogram Features, International Journal Of Applied Engineering Research (IJAER) Vol.10, Issue 68 pp.482-487 2015, scopus indexed