POLITICAL PARTIES CANDIDATE NOMINATION PARADIGM USING INTERVAL TYPE 2 FUZZY LOGIC AND FLOWER POLLINATION ALGORITHM

POLITICAL PARTIES CANDIDATE NOMINATION PARADIGM USING INTERVAL TYPE 2 FUZZY LOGIC AND FLOWER POLLINATION ALGORITHM

  • The Complete Research Material is averagely 52 pages long and it is in Ms Word Format, it has 1-5 Chapters.
  • Major Attributes are Abstract, All Chapters, Figures, Appendix, References.
  • Study Level: BTech, BSc, BEng, BA, HND, ND or NCE.
  • Full Access Fee: ₦4,000

Get the complete project » Instant Download Active

CHAPTER ONE

GENERAL INTRODUCTION

1.0       Introduction

1.1.                Background of Study

No global political trend has been more far-reaching and profound like the conduct of elections. An election is the formal process in which people vote to choose a person or group of people to hold an official position or be their political leader or representative in government. Usually, at the time of any election at any constituency, more than one persons are interested in contesting an election as a representative of a political party. A political party is an organization that nominates or presents candidates to stand for election in its name and seeks to have or place representatives (leaders) in government (Wordu, 2011). In such situations party leaders must select candidates to best accomplish their electoral, organizational, and policy goals. In this scenario, it is very tough to decide the most suitable candidate. Candidate selection or rightly put, candidate nomination is the process by which political parties decide who will be on the ballot paper as their recommended candidate (Rose, Gavin M, 2006).

Parties can nominate their candidates in many different ways. In numerous cases, the existing legal framework establishes that political parties should “democratically” elect their candidates, but this concept is very vague, and there are few if any applicable legal provisions. Only in a few cases does legislation lay down the process by which candidates should be nominated, and the nomination process can have a direct impact on the depth and breadth of the democratic process—particularly if a given party’s candidate nomination process is non-transparent. Nevertheless, in almost all cases, the emergence of a candidate as a flag bearer of any party is through party primaries and party nomination.

A party primary election is a type of poll organized before the general elections for the purpose of nominating a party’s candidates for a political office (Keithly, 2012). Candidates of various compositions and calibre are given the room to contest for who will bear the party flag and run for electoral office under the platform. It starts from ward level then to local level to state level and then to federal level. At this stage, the popularity of each of the candidates are weighed and their imperative measured while the party members are also given an opportunity to be part of the nomination process.

However, in Nigeria, the process of nominating candidates using party primaries is and continues to be a very controversial issue among the big political parties. The choice of party elections as the primary method of candidate nomination is majorly to give party members the right to choose who their representatives in the general elections will be and to select the most popular candidate within the party. Obviously, this is usually not the case because, the politics of electoral primaries in Nigeria does not truly reflect the choice of the electorates, but that of a class who are concerned about their personal financial aggrandizement. For instance, in the 2015 elections, the leaders of the People’s Democratic Party (PDP) leaders imposed the then president, President Goodluck Jonathan as on its members as their presidential candidate. Of course, this led to ‘factionalization’ of the party, loss of several key members to opposition parties and its subsequent defeat in the presidential elections.

Again, Party primary elections tend to focus on popularity as the sole criteria for candidate nomination when in many cases, the viability of a candidate may depend on several other decisive factors. For instance, if we assume that in a group of would-be candidates, the candidate having a high popularity will have a very strong chance to win an election. But what will happen, if all the candidates have very high popularity in same constituency or all have very low popularity. Will they all win the election? So, to predict the winning candidate based upon one or two factors or non-favorable factors is not up to mark.

For these reasons, there is need for a better way for parties to nominate their candidates for political positions in the general elections. However, the major hurdles while using most methods of candidate nomination, party primaries inclusive, would be overcoming subjective value judgement, biasness, vagueness, imprecision and complexity as it concerns human behavior. It is shown that analyses of human behavior using pure quantitative methodologies are not likely to have much relevance to social, political and economic real-world problems which involve human beings. People mostly have a tendency to use common linguistic terms to express both themselves and the world that they live in, such as; old, young, very old, hot, cold, slightly cold, etc. It is against this background that this study proposes a Fuzzy System as the best approach to candidate nomination.

Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than classical binary logic. It is a powerful tool to tackle imprecision and uncertainty and was initially introduced to improve robustness and low-cost solutions for real world problems (Kim et al, 2000). Fuzzy logic has proven to be an excellent choice for approximation and forecasting systems, control systems, databases, healthcare, clinical diagnosis, etc. It has been applied in this research project due to its inherent ability to tackle imprecision and uncertainty as it uses an imprecise but very descriptive language to deal with input data more like a human operator.

Although conventional fuzzy logic (type-1 fuzzy logic) systems have achieved great success in many different real-world applications, research has shown that Type-1 FLSs, whose membership functions are type-1 fuzzy sets, are unable to directly handle rule uncertainties. As a result of this, the concept of type-2 fuzzy sets were introduced by Zadeh in 1975 as an extension of the concept of an ordinary fuzzy set, i.e., a type-1 fuzzy set. Type-2 FLSs, in which antecedent or consequent membership functions are type-2 fuzzy sets, can handle rule uncertainties because they have grades of membership that are themselves fuzzy. A type-2 membership grade can be any subset in (0, 1)—the primary membership; and, corresponding to each primary membership, there is a secondary membership (which can also be in (0, 1)) that defines the possibilities for the primary membership. Similar to a type-1 FLS; a type-2 FLS includes fuzzifier, rule base, fuzzy inference engine, and output processor (Wu, 2005). The output processor includes type-reducer and defuzzifier; it generates a type-1 fuzzy set output (from the type-reducer) or a crisp number (from the defuzzifier). A type-2 FLS is again characterized by IF–THEN rules, but its antecedent or consequent sets are now type-2.Type-2 fuzzy logic systems (T2 FLS) have the potential to provide better performance than a type-1 Fuzzy Logic System (Wu and Mendel, 2003).

However, general type-2 fuzzy logic system is complex, ambiguous and very difficult to implement. This is because type-reduction is very intensive. Things simplify a lot when secondary membership functions (MFs) are interval sets (in this case, the secondary memberships are either zero or one), which is the case with interval type-2 fuzzy system. The interval type-2 fuzzy system is a simplified version of the general type-2 fuzzy logic system and is ideal for implementing this project. Gaussian membership function is used to show the degree of participation of each input parameter because Gaussian membership function provides the best result generally (Mete et al, 2012). This project will aid in solving the leadership by helping Political parties field credible candidates during elections. This project seeks to eliminate the devastating effects of nominating ‘poor’ and unsuitable candidates.

1.2.              Statement of the Problem

Candidate nomination is an important issue in the political and leadership process of Nigeria. Most systems that have been used for candidate nomination such as regression models, auto-regression integrated moving average (ARIMA) and the conventional fuzzy logic (type-1 fuzzy inference system) are not efficient due to the fact that,

§  the Linear Regression models have limited modeling ability for nonlinear human behavior

§  the ARIMA models involve the use of complex mathematical modeling

§  the conventional fuzzy logic (type-1 fuzzy logic) has limited capabilities to directly handle data uncertainties especially in situations where it is difficult to determine the exact membership function for a fuzzy set.

1.3.                Aim and Objectives of the Study

This research is aimed at the development of an interval type-2 fuzzy system for Candidate Nomination by Political Parties in Nigeria. Objectives of this study include:

                   I.            Design of a database to handle both structured and unstructured information about the decision variables of the proposed system.

                II.            Design of an interval type-2 fuzzy inference model for Candidate nomination by political parties.

             III.            Optimize the membership functions in (II) above using Flower Pollination algorithm

             IV.            Implement (II) and (III) above

                V.            Evaluate the developed system by comparing it’s results with that of an existing un-optimized system

1.4.                Research Methodology

In order to achieve the set objectives, the following research methodologies are employed:

       I.             Review and study of relevant literatures on fuzzy logic, electoral and political party systems and processes in Nigeria.

    II.            Gathering data through personal interviews with experts and stakeholders in the Nigeria political landscape

 III.            Unified Modeling Language (UML) is employed in the design of the system

 IV.            Object Oriented Design Methodology (OODM) is employed in the development of the system

    V.            Knowledge base design tools are used in the system

 VI.            Interval Type 2 Fuzzy Logic design tools are employed in the design of the system

VII.             Flower pollination algorithm method is applied in the optimization of the membership functions of our system

VIII.             Microsoft Excel is used for data analysis, Oracle’s MySQL is used for Database Implementation, MATLAB Toolbox is used as the modelling environment while Java is used as the interface development language.

1.5.                Scope of the Study

The study is limited major political parties in Nigeria using IT2FL as a predictor using only relevant parameters (6 in this case). The fuzzy logic optimization will only be done on the membership functions.

1.6.           Significance of the Study
This project on completion will:

          I.              Aid political parties select the most suitable candidates for elections in Nigeria

       II.              Help curb the ‘proliferation’ of ‘substandard’ candidates presented to the electorates by political parties

    III.              Aid in solving the leadership crises which has engulfed the country by ensuring that the candidates available for election from the different political parties have what it takes to perform in the different political positions

   IV.              Help political parties maximize their chances of winning the election since the will have selected the candidate that is not just most suitable for the position but also most likely to win the elections from within the party

1.7.           Organization of work

This research project is organized in five chapters. Chapter one gives a general overview and introduction to the work while emphasizing the problem, aims and objectives and research methodology. Chapter two delves deeper into the applications of fuzzy inference systems in the area of candidate nomination. The Candidate nomination process of various political parties as well as the political party system of Nigeria in general are also discussed in detail. Chapter three presents the system design and analysis of the candidate nomination expert system. Chapter four emphasizes on the implementation of the system. The summary, conclusion and recommendation of this work is given in Chapter five.





Share a Comment


You can find more project topics easily, just search

Quick Project Topic Search