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Anfis Based Model for Predicting a Credible Candidate in Elections

Omomule Taiwo Gabriel*

Department of Computer Science, Adekunle Ajasin University, Akungba-Akoko, Ondo, Nigeria.

Periodicity:December - February'2020
DOI : https://doi.org/10.26634/jcom.7.4.17072

Abstract

The effect of poor governance in developing countries can lead to problems as to who is best to vote in elections. This is further affirmed by various political, social, economic and religious segregation, cultural bias and corruption. In quest for good governance and elections within the government without bias, there is a need to consider candidate's credibility. Therefore, this paper proposes a model for predicting credible candidates contesting in an electoral process. Moreover, the rationale of the paper is to assess candidate's credibility and the potential to win elections. Data are extracted from the opinion of elite Nigerians on Twitter and it was used to train the proposed model and to determine a reliable output from the opinions of personal credibility criteria of political leaders in Nigeria. In the proposed system, six (6) political candidates are sampled and the most credible candidate is selected. The data collected are subjected to Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for training and the Gaussian Membership Function (GMF) technique is adopted to calculate the membership grade for each input parameter implemented on MATLAB R2018a. The results obtained are the Average Testing Error (AVE) of 0.17 and the prediction accuracy of 92.85%, respectively. Comparative analysis of the current results shows the prediction of the electoral systems with significant contributions.

Keywords

Electoral System, Election Prediction, Credibility, Candidates, ANFIS, Votes.

How to Cite this Article?

Gabriel, O. T. (2020). Anfis Based Model for Predicting a Credible Candidate in Elections. i-manager's Journal on Computer Science, 7(4), 34-45. https://doi.org/10.26634/jcom.7.4.17072

References

[1]. Ajayi, O. O., Aderele, T. B., Elemese, T. O.,& Adetuyi, T. E. (2019). Suggesting suitable economic and political leaders using Fuzzy-Analytic Hierarchy Process (FAHP). International Journal of Computer Trends and Technology, 67(3), 85-94.

[2]. Alam, M. M., MezbahUddin, M., & Shoma, S. N. (2015). Election result prediction system using Hidden Markov Model (HMM). International Journal of Computer Applications, 129(3), 1-4.

[3]. Burnap, P., Gibson, R., Sloan, L., Southern, R., & Williams, M. (2016). 140 characters to victory?: using Twitter to predict the UK 2015 general election. Electoral Studies, 41, 230-233. https://doi.org/10.1016/j.elect stud.2015 .11.017

[4]. Isma'ila, Y., & Othman, Z. (2015). Challenges of electoral processes in Nigeria's quest for democratic governance in the fourth republic. Research on Humanities and Social Sciences, 5(22), 1-10.

[5]. Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. https://doi.org/ 10.1109/21.256541

[6]. Karmore, P., & Butey, P. (2016). Neuro-fuzzy based testing approach for model driven embedded system. International Journal of Advanced Research in Computer Science and Software Engineering, 6(6), 2049-2056.

[7]. Khashman, Z., & Khashman, A. (2016). Anticipation of political party voting using artificial intelligence. Procedia Computer Science, 102, 611-616. https:// doi.org/10.1016/j.procs.2016.09.450

[8]. Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H. (2014). Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information Processing in Agriculture, 1(1), 14-22. https://doi.org/10.1016/j.inpa. 201 4.04.001

[9]. Kulkarni, S., Nikumbh, P. J., Anuradha, G., & Nikam, S. (2012). S-ANFIS: Sentiment aware adaptive networkbased fuzzy inference system for predicting sales performance using blogs/reviews. International Journal of Multi disciplinary in Cryptology and Information Security, 1(2), 22-32.

[10]. Kwaghga, B. (2012). Good governance and credible elections in Nigeria. Journal of Social Science and Public Policy, 4, 65-72.

[11]. Lawal, T., & Olukayode, O. V. (2012). Law and electoral politics in Nigeria: Analysis of the congruence. JL Pol'y & Globalization, 6, 14-17.

[12]. Lofti, E., & Karimi, M. R., (2014). OPEC oil price prediction using ANFIS. Journal of Mathematics and Computer Science, 10, 286-296.

[13]. Mattes, K., Spezio, M., Kim, H., Todorov, A., Adolphs, R., & Alvarez, R. M. (2010). Predicting election outcomes from positive and negative trait assessments of candidate images. Political Psychology, 31(1), 41- 58. https://doi.org/10.1111/j.1467-9221.2009.00745.x

[14]. McDonald, R., & Mao, X. (2015). Forecasting the 2015 General Election with Internet Big Data: An Application of the TRUST Framework, Working Papers, Business School-Economics, University of Glasgow.

[15]. Mesfin, B. (2008). Democracy, elections & political parties: A conceptual overview with special emphasis on Africa. Institute for Security Studies Papers, 1-12.

[16]. Nnamani, D. O. (2014). Electoral process and challenges of good governance in the Nigerian state (1999-2011). Journal of Good Governance and Sustainable Development in Africa (JGGSDA), 2(3), 78- 99.

[17]. Nwagboso, C. I., (2011). Elections and electoral tribunal in Nigeria. African Research Review: An International Multi-Disciplinary Journal, 5(2), 42-55.

[18]. Nyukorong, R., & Quisenberry, W. (2016). Character traits of effective executives : A phenomenological study of CEOs in Ghana. European Scientific Journal, 12(20), 69-90. https://doi.org/ 10.19044/esj.2016.v12n20p69

[19]. Oyadiran, S., & Olorungbemi, S. T. (2015). Police and challenges of conducting credible elections in Nigeria; An examination of the 2007 presidential election. Global Journal of Arts, Humanities and Social Sciences, 3(6), 57-85.

[20]. Şahin, M., & Erol, R. (2017). A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computational Applications, 22(4), 1-12. https:// doi.org/10.3390/mca2 2040043

[21]. Salunkhe, P.,& Deshmukh, S. (2017). Twitter based election prediction and analysis. International Research Journal of Engineering and Technology (IRJET), 4(10), 539-544.

[22]. Singh, H., Singh, G., & Bhatia, N. (2012). Election results prediction system based on fuzzy logic. International Journal of Computer Applications, 53(9),30-37.

[23]. Uzedhe, G. O., & Okhaifoh, J. O. (2016). A technological framework for transparent e-voting solution in the Nigerian electoral system. Nigerian Journal of Technology, 35(3), 627-636. https://doi.org/ 10.4314/ njt.v35i3.22

[24]. Van Zuydam, S. (2014, April). Credibility as a source of political capital: exploring political leaders' performance from a credibility perspective. In ECPR Joint Sessions 2014, Workshop 15: Political Capital and the Dynamics of Leadership: Exploring the Leadership Capital Index (pp.1-24).

[25]. Wojtasik, W. (2013). Functions of elections in democratic systems. Political Preferences, 4, 25-38. https://doi.org/10.6084/m9.gshare.729054

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