ITDS | SPMS

Chatbot To Aid In The Admissions Process Of UENR

Project Description
Supervisor

-Good communication and reliable knowledge are two most crucial aspects of human life. In today's quickly evolving academic scene, universities must manage an increasing volume of inquiries while offering timely, reliable and easily available information to potential students. When it comes to making good communication and providing reliable knowledge with regards to admissions processes, the University of Energy and Natural Resources face some challenges such as timely response to user queries. With this project, the chatbot which will make use of Artificial Intelligence and Natural Language Processing will assists candidates with the admission process, respond to most common queries and reduce the work load on administrative staff. The system which is build using python and NLP algorithms is expected to understand user queries and provide accurate response base on queries made. The project uses an organized approach that includes backend integration, system design, user interface development and requirement collection. In order to ensure seamless interactions for both local and foreign applicants. Special emphasis is made on building an interactive and user-friendly interface. Security and privacy concerns are addressed by data encryption and compliance with best practices for preserving personal information. To guarantee dependable functionality and user-friendliness, the chatbot undergoes rigorous testing. The project's outcomes show a considerable increase in user involvement, a decrease in administrative effort, and a better application experience. Future iterations of the system will encompass voice-based interaction, enhanced machine-learning-powered customization, and language support.

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.

This project aims to develop and implement a user-friendly chatbot that utilizes artificial intelligence (AI) and natural language processing (NLP) to help in the admission process of the University of Energy and Natural Resources.

2023/2024

Nov 18, 2024

AKPADAGO EVANS AWINTOMAH UEB3202321 NIYUO ZUNUOR EVELYN UEB3202520 EMMANUEL QUAYE UEB3211920 YELINGER JOYCE JEBUNI UEB3218720 AMANKONA OFORI GILBERT UEB3222520

BSc Information Technology