Abstract

The increasing demand for secure and efficient voting systems has led to the exploration of online voting solutions. Traditional voting methods are often vulnerable to fraud, inefficiencies, and logistical challenges. This paper presents an online voting system that leverages machine learning techniques to enhance security, accuracy, and accessibility. The system employs facial recognition for voter authentication, anomaly detection to prevent fraudulent activities, and natural language processing (NLP) for user interaction. Experimental results indicate that the proposed model provides a reliable, tamper-resistant, and user-friendly voting experience.