Hi, I am Lam Hong Lee, My LiveDNA is 60.537
 
   
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Dr. Lam Hong Lee
 
Highest Degree: Ph.D. in Computer Science from The University of Nottingham, U.K.
 
Institute: Universiti Tunku Abdul Rahman, Malaysia
 
Area of Interest: Computer Sciences
  •   Artificial Intelligence
  •   Machine Learning
  •   Pattern Recognition
  •   Text Mining
 
URL: http://livedna.org/60.537
 
My SELECTED Publications
1:   Akram, N.A., D. Isa, R. Rajkumar and L.H. Lee, 2014. Active incremental support vector machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers. Ultrasonics, 54: 1534-1544.
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2:   Baharudin, B., L.H. Lee and K. Khan, 2010. A review of machine learning algorithms for text-documents classification. J. Adv. Inform. Technol., 1: 4-20.
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3:   Chia, Y.Y., L.H. Lee, N. Shafiabady and D. Isa, 2015. A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the support vector machine. Applied Energy, 137: 588-602.
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4:   Choo, W.O., L.H. Lee, D. Isa and W.Y. Chue, 2014. Bayesian folder allocation system for electronic text document repositories. J. Applied Sci., 14: 10-17.
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5:   Hartley, M., D. Isa, V.P. Kallimani and L.H. Lee, 2006. A domain knowledge preserving in process engineering using self-organizing concept. Proceedings of the 3rd International Conference on Artificial Intelligence in Engineering and Technology, November 22-24, 2006, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia, pp: 2-7.
6:   Isa, D., L.H. Lee, V.P. Kallimani and R. Prasad, 2008. Polychotomiser for case-based reasoning beyond the traditional bayesian classification approach. Comput. Inform. Sci., 1: 57-68.
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7:   Isa, D., L.H. Lee, V.P. Kallimani and R. Rajkumar, 2008. Text document pre-processing using the bayes formula for classification based on the vector space model. J. Comput. Inform. Sci., 1: 79-90.
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8:   Isa, D., L.H. Lee, V.P. Kallimani and R. Rajkumar, 2008. Text document pre-processing with the bayes formula for classification using the support vector machine. IEEE Trans. Knowledge Data Eng., 20: 1264-1272.
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9:   Isa, D., V.P. Kallimani and L.H. Lee, 2009. Using the self-organizing map for clustering of text documents. Expert Syst. Appl., 36: 9584-9591.
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10:   Lee, L.H. and D. Isa, 2010. Automatically computed document dependent weighting factor facility for Naive Bayes classification. Expert Syst. Applic., 37: 8471-8478.
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11:   Lee, L.H., C.H. Wan, R. Rajkumar and D. Isa, 2012. An enhanced support vector machine classification framework by using Euclidean distance function for text document categorization. Applied Intell., 37: 80-99.
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12:   Lee, L.H., C.H. Wan, T.F. Yong and H.M. Kok, 2010. A review of nearest neighbor-support vector machines hybrid classification models. J. Applied Sci., 10: 1841-1858.
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13:   Lee, L.H., D. Isa, W.O. Choo and W.Y. Chue, 2010. Tournament structure ranking techniques for Bayesian text classification with highly similar categories. J. Applied Sci., 10: 1243-1254.
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14:   Lee, L.H., D. Isa, W.O. Choo and W.Y. Chue, 2012. High relevance keyword extraction facility for Bayesian text classification on different domains of varying characteristic. Expert Syst. Applic., 39: 1147-1155.
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15:   Lee, L.H., R. Rajkumar and D. Isa, 2012. Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach. Applied Intell., 36: 295-307.
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16:   Lee, L.H., R. Rajkumar, L.H. Lo, C.H. Wan and D. Isa, 2013. Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Expert Syst. Appl., 40: 1925-1934.
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17:   Wan, C.H., L.H. Lee, R. Rajkumar and D. Isa, 2012. A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine. Expert Syst. Applic., 39: 11880-11888.
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