Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization

2020 ◽  
pp. 3-10
Author(s):  
Saumendra Kumar Mohapatra ◽  
Sipra Sahoo ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Artificial Neural

Grey Wolf Optimization-Based Artificial Neural Network for Classification of Kidney Images

2018 ◽  
Vol 27 (14) ◽  
pp. 1850231 ◽  
Author(s):  
Paladugu Raju ◽  
Veera Malleswara Rao ◽  
Bhima Prabhakara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kidney Diseases ◽  
Feature Subset ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.

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.

scholarly journals AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION

2021 ◽  
Vol 20 (Number 2) ◽  
pp. 213-248
Author(s):  
Narender Kumar ◽  
Dharmender Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
New Variant ◽  
Parkinson’S Diseases ◽  
Artificial Neural

Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Scandinavian test of artificial neural network for classification of myocardial perfusion images

2000 ◽  
Vol 20 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Lindahl ◽  
Toft ◽  
Hesse ◽  
Palmer ◽  
Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document