Online Election Management System Using Machine Learning

The Online Election Management System is a web-based application. The system has a centralized database to keep the details or records of all the voters, candidates and final Results. The System was implemented based on image classification technique and sending the passwords to voters through email, for login. The System uses HTML and CSS for front-end designing and for backend python programming language and python's web framework called Django for authentication and authorization, database management. The major work in the system is image recognition, for image recognition Convolutional Neural Network (CNN) is used. CNN needs less data to train the model and efficiency is more compared with other classification algorithm YOLO. On using this application, the voter need not go to the booth. So, it is user convenient and also it is easy for the admin to manage the voters and candidate information. It is secure because it uses a two-step verification method. First the user has to login using credentials then using image classification, it recognizes the exact voter. The system prepares the dataset at the time of user registration the user credentials by generating the password randomly for the voter and sending the voter credentials to the voter registered mail id. This research work gives a complete solution for an online voting system with higher accuracy and lesser time.