Resumen

CALDERON-SEGURA, Yessica Yazmin; BURLAK, Gennadiy  y  GARCIA PACHECO, José Antonio. Enhancing Electoral Surveys with Artificial Neural Networks. Program. mat. softw. [online]. 2024, vol.16, n.2, pp.49-59.  Epub 17-Sep-2024. ISSN 2007-3283.  https://doi.org/10.30973/progmat/2024.16.2/5.

The objective of this study is to search for the main factors that can influence to predict the results of voting surveys. A system is developed that allows the optimization of Artificial Neural Networks to identify the factors that affect the electoral result, through a computational method that allows the evaluation of the characteristics that influence a successful electoral vote. An Artificial Neural Network with three layers and a back propagation learning algorithm is used. The first phase loads the system by developing a random synthetic database. This will contain the data that will serve as input to the Artificial Neural Network to optimize the most outstanding attributes that affect a vote. The system identifies the inputs to the Artificial Neural Network, and the iterations that can be carried out to optimize its outputs.

Palabras llave : Artificial Neural Network; Conservative; Algorithm.

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