Oriental Journal of Computer Science and Technology

Kiran Kumar Reddi ¹, M. V. Basaveswara Rao ²

Affiliations
1 Department of Computer Science, Krishna University, Machilipatnam, IN
2 Department of Pharmaceutical Chemistry, Krishna University, Machilipatnam, IN

Abstract

Bioinformatics, an area that has evolved in response to this deluge of information, can be viewed as the use of computational methods to handle biological data. It is an interdisciplinary field involving biology, computer science, mathematics and statistics to analyze biological sequence data, genome content & arrangement, and to predict the function and structure of macromolecules. Soft computing is a consortium of methodologies that work synergistically and provide, in one form or another, flexible information processing capabilities for handling real life ambiguous situations. Its aim, unlike conventional (hard) computing, is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness, low solution cost, and close resemblance with human like decision-making. The paper will focus on soft computing paradigm in bioinformatics with particular emphasis on research.

Keywords

Bioinformatics, Soft Computing Paradigm, Ant Colony Optimization, Bioinformatics Algorithms, Tabu Search, Support Vector Machines.

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K. Venkat Rao ¹, T. Jyothirmayi ², M. V. Basaveswara Rao ³

Affiliations
1 Department of Computer Science & Systems Engineering, College of Engineering, Andhra University, Visakhapatnam - 530 003, IN
2 Department of Computer Science, GITAM University, Visakhapatnam - 530 013, IN
3 Sadineni Chowdaraiah College of Atrs and Science, Maddirala, Chilakaluripet - 522 611, IN

Abstract

This paper aims at constructing the customer data warehouse which adopts an improved ID3 decision tree algorithm to implement data mining in order to predict the risk class of the customer. The obtained results are compared with experimental results in order to verify the validity and accuracy of the developed model.

Keywords

Decision Tree, ID3, Classification, Association Rules.

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