Biologically inspired motion detection neural network models evolved using genetic algorithms

2009 ◽  
Author(s):  
Sherif W. Azary ◽  
Peter G. Anderson ◽  
Roger S. Gaborski
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Motion Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Biologically Inspired

scholarly journals Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa

2019 ◽  
Vol 18 (1) ◽  
pp. 54-59
Author(s):  
Irfan Ali ◽  
Lana Sularto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Student Behavior ◽  
Network Models ◽  
Time Data ◽  
Neural Network Models ◽  
Student Graduation ◽  
Artificial Neural ◽  
Artificial Neural Network Models

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.

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