Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

2019 ◽  
Vol 36 (1) ◽  
pp. 259-275 ◽  
Author(s):  
Shu Gong ◽  
Wei Gao ◽  
Francis Abza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Tumor ◽  
Optimization Algorithm ◽  
Tumor Diagnosis ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Brain Tumor Diagnosis ◽  
Artificial Neural

scholarly journals An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 48428-48437 ◽  
Author(s):  
Fatemeh Safara ◽  
Amin Salih Mohammed ◽  
Moayad Yousif Potrus ◽  
Saqib Ali ◽  
Quan Thanh Tho ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Detection Method ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Artificial Neural ◽  
Gender Detection

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

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