Optimización de las exportaciones de banano ecuatorianas mediante un clasificador basado en redes neuronales.

Abstract

Exports are an essential part of the country's growth today, where innovation and process automation with the use of data analysis is an effective tool to improve the classification of products by identifying the key variables that directly influence them. However, the understanding and use of artificial intelligence concepts such as artificial neural networks for classification remains a challenge because it is a complex process that is regularly being developed and innovated. Despite these challenges, it is essential for companies to adapt their processes to this new era of digitalisation in order to develop a competitive advantage. The classification of products and the identification of the variables that affect their export are essential, as they allow companies to adopt new strategies for the production of products. This research focuses on the development of an artificial neural network model for the classification of Ecuadorian bananas for export, in which it is hoped to achieve a minimum error in the prediction of the classification of banana types. The objective is to determine which variables directly affect banana exports. The application of artificial neural networks for classification is justified because this sector is one of the main industries in the economy of our country.