| Autor: |
Yessica Yazmin Calderon-Segura, Gennadiy Burlak, José Antonio García Pacheco |
| Jazyk: |
English<br />Spanish; Castilian |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Programación Matemática y Software, Vol 16, Iss 2 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2007-3283 |
| DOI: |
10.30973/progmat/2024.16.2/5 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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