Open Access   Article Go Back

"Fuzzy Expert System for Prediction of Indian General Election Results"

Manjiri M. Mastoli1 , R.V. Kulkarni2 , Urmila R. Pol3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 937-943, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.937943

Online published on May 31, 2019

Copyright © Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol, “"Fuzzy Expert System for Prediction of Indian General Election Results",” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.937-943, 2019.

MLA Style Citation: Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol ""Fuzzy Expert System for Prediction of Indian General Election Results"." International Journal of Computer Sciences and Engineering 7.5 (2019): 937-943.

APA Style Citation: Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol, (2019). "Fuzzy Expert System for Prediction of Indian General Election Results". International Journal of Computer Sciences and Engineering, 7(5), 937-943.

BibTex Style Citation:
@article{Mastoli_2019,
author = {Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol},
title = {"Fuzzy Expert System for Prediction of Indian General Election Results"},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {937-943},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4342},
doi = {https://doi.org/10.26438/ijcse/v7i5.937943}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.937943}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4342
TI - "Fuzzy Expert System for Prediction of Indian General Election Results"
T2 - International Journal of Computer Sciences and Engineering
AU - Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 937-943
IS - 5
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
331 312 downloads 125 downloads
  
  
           

Abstract

A fuzzy expert system is a form of artificial intelligence that uses a collection of membership functions as fuzzy logic and rules to reason about data. India is being the largest democracy in the world; therefore rule based fuzzy expert system for prediction of Indian General Election results has applicable importance. For proper growth and development of any country there is need of democratic governance. Election is very famous in same way the prediction of election results is also has great importance. Authors has designed & developed the fuzzy expert system which predicts election results in India. Authors studied the voting behavior of voters of India by survey. System accepts input as behavioral parameter those are linguistic variables considered for prediction of chances of winning candidate or party. Fuzzy logic toolbox from MATLAB is used for designing and development of fuzzy expert system. The Fuzzy Expert System “FuzzyExitPoll” also works like exit poll as well as opinion poll. This research help to predict the election results so respective action should done by that particular party or candidate to win the elections.

Key-Words / Index Term

Election Prediction, Fuzzy Expert System, Membership Function, Linguistic Variable, Fuzzy logic Toolbox

References

[1] Rehana Ali “The working of Election Commission of India
”, Jnanada Prakashan, 2001 - History -10- 239
[2] Barnabas Bede “Mathematics of Fuzzy Sets and Fuzzy Logic
”,Springer,1-5
.[3] GODFREY H. “FUZZY LOGIC WITH MATLAB”, CREATESPACE INDEPENDENT PUBLISHING PLATFORM, 12-NOV-2016 – 1-328
[4] Zadeh, L. A. (1965). Fuzzy sets. Information and Control. 8, 338-353.
[5] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13, 1975, pp. 1-13.
[6]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.
[7]Yue Jiao, Yu-Ru Syau, E. Stanley Lee, Fuzzy adaptive network in presidential elections, Volume 43, Issues 3–4, February 2006, Pages 244-253
[8] 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.
[9] Luis Teran, A Fuzzy-Based Advisor for Elections and the Creation of Political Communities, IEEE, 978-0-9564263-8/3
[10] Harmanjit Singh, Gurdev Singh, Nitin Bhatia, International Journal of Computer Applications (0975 – 8887) Volume 53– No.9, September 2012
[11] Manjiri M. Mastoli, R.V.Kulkarni, A Review: Role of Fuzzy Expert System for Prediction of Election Results. Reviews of Literature, 2013
[12] Masaharu Mizumoto, “Fuzzy Sets and Their Operations”, Information And Control 48, 30-48 (1981)
[13] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science Pub. Co., 1985.
[14]https://docs.google.com/forms/d/e/1FAIpQLSeesMLD0mSGpyzuMOXPZy6Ura_pRHk7XEeTMamemtN2r8lElg/viewform?usp=pp_url
[15] Fuzzy Logic ToolboxTM User’s Guide: MATLAB
[16] S.N. Sivanandam, S. Sumathi, S. N. Deepa “Introduction to Fuzzy Logic using MATLAB”,Springer,128