Application of Artificial Neural Networks in Estimating Participation in Elections
This work addresses election forecasting for a specific region, but it is incremental as it applies an existing neural network method to new data without major innovations.
The paper tackled the problem of predicting election participation rates using artificial neural networks, achieving 91% accuracy in forecasting voter turnout in Kohgiloye and Boyerahmad province for Iran's presidential election.
It is approved that artificial neural networks can be considerable effective in anticipating and analyzing flows in which traditional methods and statics are not able to solve. in this article, by using two-layer feedforward network with tan-sigmoid transmission function in input and output layers, we can anticipate participation rate of public in kohgiloye and boyerahmad province in future presidential election of islamic republic of iran with 91% accuracy. the assessment standards of participation such as confusion matrix and roc diagrams have been approved our claims.