Usar diferentes tipos de redes neuronales para detectar la preparación del cuerpo para la donación de sangre y determinar el valor de cada uno de sus parámetros mediante un algoritmo genético

Autores/as

  • Zahra Jafari Islamic Azad University
  • Asma Mahdavi Yousefi Islamic Azad University
  • Saman Rajabi Seraj Higher Education Institute

DOI:

https://doi.org/10.15649/2346075X.998

Palabras clave:

Perceptron neural network, RBF neural network, blood transfusion, Fisher’s discrimination ratio, genetic algorithm

Resumen

Blood transfusion is of great importance in the medical field, as blood donation centers are responsible for collecting and distributing blood and blood products. Artificial neural network is a data processing system getting ideas from the human brain and designs a data structure that acts like a neuron using programming science and by creating a network between these neurons and combining large amounts of data with smart algorithms and their rapid processing, the network is trained. In this study, data is extracted from the blood transfusion service center and the perceptron neural network, RBF neural network, Fisher’s discrimination ratio and genetic algorithm were examined, and finally the highest possible accuracy from the neural network was achieved.

Biografía del autor/a

Zahra Jafari, Islamic Azad University

Department of Medical Engineering, Faculty of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Asma Mahdavi Yousefi, Islamic Azad University

Department of Medical Engineering, Faculty of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Saman Rajabi, Seraj Higher Education Institute

Assistant Professor of Electrical Engineering Department, Seraj Higher Education Institute, Tabriz, Iran

Referencias

American Red Cross Biomedical Services. Archived from the original on 2009-10-20. Retrieved 2009-10-26.

R. Beale and T. Jackson, Neural Computing (An Introduction), Adam Ililger (1990).
https://doi.org/10.1201/9781420050431

J. Schmidhuber, "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85-117. (2015).
https://doi.org/10.1016/j.neunet.2014.09.003

D.Graupe, principles of artificial neural networks, world scientific publishing co. Pte. LTD., Vol.6, second edition, 2007.
https://doi.org/10.1142/6429

U. Orhan, M. Hekim, M. Ozer, "EEG signals classification using the K-means clustering and a multilayer Perceptron neural network model" Expert Systems with Applications 38 (2011) 13475-13481.
https://doi.org/10.1016/j.eswa.2011.04.149

Lowe, D, "Adaptive radial basis function non-linearities and the problem of generalisation", IEE Conf. on Artificial Intelligence and Neural Networks. 1989.

C. K. I. Williams, D. Barber "Bayesian Classification with Gaussian Process" IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 20, Issue: 12, Dec 1998).
https://doi.org/10.1109/34.735807

Seryasat, Omid Rahmani, and Javad Haddadnia. "Evaluation of a new ensemble learning framework for mass classification in mammograms." Clinical breast cancer 18.3 (2018): e407-e420.
https://doi.org/10.1016/j.clbc.2017.05.009

J.L.R. Filho, P.C. Treleaven, C. Alippi, Genetic algorithm programming environments, IEEE Comput. 27 (1994) 28-43.
https://doi.org/10.1109/2.294850

Descargas

Publicado

2020-12-01

Cómo citar

Jafari, Z., Mahdavi Yousefi, A., & Rajabi, S. (2020). Usar diferentes tipos de redes neuronales para detectar la preparación del cuerpo para la donación de sangre y determinar el valor de cada uno de sus parámetros mediante un algoritmo genético. Innovaciencia, 8(1), 1–10. https://doi.org/10.15649/2346075X.998

Número

Sección

Artículo de investigación científica y tecnológica

Altmetrics

Descargas

Los datos de descargas todavía no están disponibles.