Work

Election Voting Trend Prediction System

Year: 2024

Type: article

Abstract: This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

Source: Asian Journal of Research in Computer Science

DOI: 10.9734/ajrcos/2024/v17i10508

Authors: I. C. Emeto, D.C. Elenwo, U.W. Anthony, J.C Shawulu, C.O Ajayi, A.A Galadima

Institutions: Federal University of Technology Owerri, Federal College of Fisheries and Marine Technology, University of Port Harcourt

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4403157194

FWCI:

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