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Classification System for Identifying the Chemical Structure Using Support Vector Machine


Affiliations
1 Department of CSE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
     

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In laboratory, each effort is taken only for identifying the unknown chemicals. All the chemicals are having its own characteristics and structure of molecules such as lines, hexagons and pentagons. The chemical database is used to find the detailed information of that molecule. Even though, the current database does not provide the up to date chemical information. To overcome the above identified problem, this paper introduces the kernel based support vector machine for identifying the chemicals using its structure. The SVM's are becoming more popular algorithm for identification of variety of chemicals in chemical applications. Final result shows the chemical identification and performance analysis of this proposed system.

Keywords

Chemistry, Classification, Molecules, Support Vector Machine.
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  • H. Tamura, and N. Yokoya, “Image database systems: A survey,” Pattern Recognition, vol. 17, no. 1, pp. 29-43, December 1984.

  • P. Howarth, and S. Ruger, “Evaluation of texture features for content-based image retrieval,” In: P. Enser, Y. Kompatsiaris, N. E. O’Connor, A. F. Smeaton, and A. W. M. Smeulders, (ed.) Image and Video Retrieval, CIVR 2004, Lecture Notes in Computer Science, vol. 3115, pp. 326-334, Springer, Berlin, Heidelberg, 2004.

  • O. Ivancicu, “Applications of support vector machines in chemistry,” Reviews in Computational Chemistry, vol. 23, pp. 291-400, 2007.

  • J. Park, Y. Choi, A. Min, and W. Huh, “Chemical structure image extraction from scientific literature using Support Vector Machine (SVM),” EECS 545 F07 Project Final Report, 2008.

  • P. Santhi, and K. Deepa, “Modified boosting classification system for human action classification using 3D modified harris corner detector,” Journal of Advances in Chemistry, vol. 12, no. 21, pp. 5307-5315, December 2016.

  • S. Thilagamani, and N. Shanthi, “Object recognition based on image segmentation and clustering,” Journal of Computer Science, vol. 7, no. 11, pp. 1741-1748, 2011.

  • S. Thilagamani, and N. Shanthi, “Gaussian and gabor filter approach for object segmentation,” Journal of Computing and Information Science in Engineering, vol. 14, no. 2, March 2014.


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  • Classification System for Identifying the Chemical Structure Using Support Vector Machine

Abstract Views: 383  |  PDF Views: 2

Authors

P. Santhi
Department of CSE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
K. Deepa
Department of CSE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India

Abstract


In laboratory, each effort is taken only for identifying the unknown chemicals. All the chemicals are having its own characteristics and structure of molecules such as lines, hexagons and pentagons. The chemical database is used to find the detailed information of that molecule. Even though, the current database does not provide the up to date chemical information. To overcome the above identified problem, this paper introduces the kernel based support vector machine for identifying the chemicals using its structure. The SVM's are becoming more popular algorithm for identification of variety of chemicals in chemical applications. Final result shows the chemical identification and performance analysis of this proposed system.

Keywords


Chemistry, Classification, Molecules, Support Vector Machine.

References