Predicting the dynamic modulus of asphalt mixture using hybridized artificial neural network and grey wolf optimizer

2021 ◽  
pp. 1-11
Author(s):  
Emadaldin Mohammadi Golafshani ◽  
Ali Behnood ◽  
Mohammad M. Karimi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Modulus ◽  
Asphalt Mixture ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Artificial Neural

Modal amplitude and phase estimation of multimode near field patterns based on artificial neural network with the help of grey-wolf-optimizer

2021 ◽  
Vol 67 ◽  
pp. 102720
Author(s):  
Naoto Sugawara ◽  
Takeshi Fujisawa ◽  
Kodai Nakamura ◽  
Yusuke Sawada ◽  
Takayoshi Mori ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Field ◽  
Grey Wolf Optimizer ◽  
Phase Estimation ◽  
Grey Wolf ◽  
Field Patterns ◽  
Modal Amplitude ◽  
Artificial Neural

scholarly journals Recognizing Ancient South Indian Language Using Artificial Neural Network Associate With Opposition Based Grey Wolf Optimization Algorithm

2021 ◽  
Author(s):  
A Nareshkumar ◽  
G Geetha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Test Case ◽  
Grey Wolf ◽  
Indian Languages ◽  
Grey Wolf Optimization ◽  
Indian Language ◽  
South Indian ◽  
Artificial Neural

Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that require special tools. This stipulation makes the dispensation period overriding, difficult, and tiresome to calculate. This paper presents a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is apparent from the ANN system that arbitrarily produced weights or neurons linking various layers plays a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results have illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

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