BLOCKCHAIN-BASED ONLINE VOTING SYSTEM

Project Abstract

The University of Energy and Natural Resources (UENR) has embarked on an innovative project to implement a blockchain-based electronic voting system designed to establish a more transparent and structured electoral process. This initiative aims to facilitate seamless voter participation through simplified screening and ballot casting processes. Developed utilizing the agile methodology, the project breaks down the overall task into smaller, manageable components, allowing for concurrent development and iterative improvements. This approach ensures that each aspect of the system can be refined based on user feedback and testing, ultimately leading to a more robust final product. A key advantage of this blockchain technology is its ability to safeguard against falsification, manipulation, and electoral fraud. By employing cryptographic techniques inherent in blockchain, the system provides a secure way to authenticate voter identities and record votes, significantly reducing the risk of malicious activities. The efficacy of blockchain in addressing vulnerabilities commonly found in traditional electoral systems is well documented, making it a powerful tool for enhancing voter confidence and participation. By promoting the principles of transparency and security, this initiative seeks not only to revolutionize the voting process at UENR but also to reinforce democratic values within the institution's electoral framework. Overall, the project represents a significant step toward modernizing the electoral landscape at UENR, fostering greater public trust in the democratic process, and encouraging higher voter engagement among the university community.

Research Area

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

Project Main Objective

• To create an electronic voting system for the University of Energy and Natural Resources (UENR) that is more secure and efficient using blockchain technology system.

Academic Year

2024/2025

Date Uploaded

Feb 3, 2026

Group Members

KUGBEH PRINCE, RUBY YANKSON, ABDUL RAHMAN UMAR KWAKYE EDWIN AMOAH, ADDEAFI GYAMBRAH JULIAN

Program Of Study

BSc Information Technology