Random Articles
Reseach Article
Election Results Prediction System based on Fuzzy Logic
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 53 - Number 9 |
| Year of Publication: 2012 |
| Authors: Harmanjit Singh, Gurdev Singh, Nitin Bhatia |
10.5120/8450-2245
|
Harmanjit Singh, Gurdev Singh, Nitin Bhatia . Election Results Prediction System based on Fuzzy Logic. International Journal of Computer Applications. 53, 9 ( September 2012), 30-37. DOI=10.5120/8450-2245
Abstract
Election is very popular word in the universe. As per the definition, Election is a process to select suitable from a group of candidates. But the process and properties may vary for different sectors. It has different types. Like local election, legislative election, parliamentary election, presidential election, senate election or election in a small group / union. As election is very famous same way election prediction is again not a new keyword. It has same long life as election. But still there is a challengeable task to predict accurate result. The use of fuzzy logic in social science to evaluate the prediction is the core part of this paper. A toolbox from MATLAB software named fuzzy logic toolbox is used for this purpose. Some input arguments are being considered which scaled using linguistic variables to predict the chances of winning along with chances of losing of a candidate.
References
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control. 8, 338-353.
- Homaifar, A. and McCormick, E. (May 1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Systems. 3 (2).
- Mendel, J. M. (March 1995). Fuzzy logic systems for engineering: a tutorial. In Proceedings of the IEEE. Vol. 83. No. 3.
- Yao, Y. Y. (1996). Two views of the theory of rough sets in?nite universes. International Journal of Approximation Reasoning. 15, 291-317.
- Yao, Y. Y. (1998). A comparative study of fuzzy sets and rough sets. Information Sciences. 109 (1-4), 227-242.
- Hayward, G. and Davidson, V. (2003). Fuzzy logic applications. Analyst. 128, 1304–1306.
- Wang, W. and Bridges, S. M. (March 2000). Genetic algorithm optimization of membership functions for mining fuzzy association rules. In Proceedings of The International Joint Conference on Information Systems, Fuzzy Theory and Technology Conference.
- Abraham, A. (2005). Adaptation of fuzzy inference system using neural learning. Fuzzy System Engineering: Theory and Practice. N. Nedjah, Ed. et al. Berlin, Germany: Springer-Verlag, 3, 53–83.
- Shafiq, M. Z. , Farooq, M. and Khayam, S. A. (2008). A comparative study of fuzzy inference systems. Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection. EvoCOMNET, LNCS.
- Zhiyi, F. (March 2004). A fuzzy inference system for synthetic evaluation of compost maturity and stability. Masters of Engineering thesis, University of Regina, Saskatchewan.
- Kumar, S. , Bhatia, N. and Kapoor, N. (Feb 2011). Fuzzy logic based tool for loan risk prediction. In Proceedings of International Conference on Communication and Computing Technologies (ICCCT-2011), 180-183.
- Kumar, S. , Bhatia, N. and Kapoor, N. (2011). Software risk analysis using fuzzy logic. International Journal of Computer Information Systems, 2 (2), 7-12.
- Singh Gursharan, Bhatia N. and Singh Sawtantar (2011). Fuzzy logic based cricket player performance evaluator. IJCA Special Issue on "Artificial Intelligence Techniques - Novel Approaches & Practical Applications" AIT.
- M. J. Smithson, G. Oden. (1999). Fuzzy set theory and application in psychology, in D. Dubios & H. Prade (Eds), International Handbook of Fuzzy Sets and Possibility Theory, Vol. 5, Kluwer, Amsterdam.
- J. Russell, M. Bullock, (1986). Fuzzy Concepts and the perception of emotion in facial expression, Social-Cognition, Vol. 4, 309-341.
- V. Dimitrov,(1997). Use of Fuzzy Logic when dealing with Social Complexity, Complexity International, Vol. 04.
- T. Yahashita, (1998). On a support system for human decision making by the combination of fuzzy reasoning and fuzzy structural modeling, Fuzzy Set and System, Vol. 8, 257-263
- Luis Teran, (2011). A Fuzzy based advisor for election and creation of political communities, Information System Research Group. Vol. 3,
- J. E Campbell, J. C. Garand (Eds. ) (2000). Before the Vote: Forecasting American National Election, Sage Publication, Inc.
- J. E Campbell, (1992) Forecasting the presidential vote in the states, American Journal of Political Science 36 386-407
- J. E Campbell, K. A. Wink, (1990) Trial-hear forecasts of the presidential vote, American Politics Quarterly 18, 251-269
- M. S Lewis-Beck, T. W. Rice, (1984) forecasting presidential election: a comparison of native models, politics behavior 6, 9-21
- M. S Lewis-Beck, T. W. Rice, (1984). Forecasting U. S House Election, Legislative Studies Quarterly 9, 475-486
- M. S Lewis-Beck, T. W. Rice, (1992). Forecasting Elections, Congressional Quarterly Press, Washington DC.
- R. Fair, (1988). The effects of economic events on votes for president : Update, Political Behavior 10, 168-179
- G. F Royes, R. C Bastos, (2001). Fuzzy Sets in Political Science. In :IFSA World Congress and 20th NAFISPS International Conference, Vol. 2, 935-940
- Riley, J. (2005). Evolving fuzzy rules for goal-scoring behaviour in a robot soccer environment, PhD Thesis, RMIT University: Melbourne, Australia.
- Yanik, P. , Ford, G. and McDaniel, W. (2010). An introduction and literature review of fuzzy logic applications for robot motion planning. In Proceedings of ASEE Southeast Section Conference.
Index Terms
Keywords