Intelligent Detection of Heart Disorders

2014 ◽  
pp. 129-158
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Cutoff Frequency ◽  
Noise Removal ◽  
Feedforward Neural Network ◽  
Self Organizing Map ◽  
Swarm Optimization ◽  
Intelligent Detection ◽  
Heart Disorders ◽  
Ecg Noise Removal

In this chapter, the first stage for detecting heart disorders (that is, noise removal) is explained. Two intelligent approaches based on Self Organizing Map (SOM) and Particle swarm Optimization (PSO) are used to train the feedforward neural network for noise removal. The trained ANNs are used to find the cutoff frequency. Then the found cutoff frequency is applied by a bandpass FIR filter for ECG noise removal.

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

scholarly journals Penerapan Particle Swarm Optimization Pada Feedforward Neural Network Untuk Klasifikasi Teks Hadis Bukhari Terjemahan Bahasa Indonesia

2018 ◽  
Vol 2 (4) ◽  
pp. 165
Author(s):  
Muhammad Ghufran ◽  
Adiwijaya Adiwijaya ◽  
Said Al-Faraby
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Islamic Law ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feedforward Neural Network ◽  
General Technique ◽  
Swarm Optimization ◽  
The Public ◽  
Speed Up

Hadith is the second source of Islamic law after Al-Qur'an and used as a guide for Muslims life. there are many hadith which has been narrated, one of them is Bukhari history. This research aims to build a model that can classify Bukhari hadith translation of Indonesian language. This topic is chosen to assist the public in understanding the meaning of the information that contained in the hadith, in the form of advocacy information, prohibitions or just information. The Backpropagation Algorithm (BP) is the general technique that used to train the Feedforward Neural Network (FNN) in classification process cause it has good accuracy for text classification. But, BP has a weakness that is relatively slow to reach convergent and stuck in local minimum. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to speed up convergence and find the minimum global value. The purpose of this test is to see the PSO's ability to train the weight and refraction of FNN. The result of this research on 1000 hadith data show that model PSO-FNN with stemming process get 88.5% accuracy while without stemming process get 88.57% accuracy. Meanwhile, the result of comparative test between PSO-FNN with BP-FNN, the result shows that  PSO-FNN get accuracy equal to 88.57% which is lower 0.93% than BP-FNN which has 89.5% accuracy.

Literature Review

2018 ◽  
pp. 67-80
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Literature Review ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Artificial Neural Network Ann ◽  
Intelligent Methods ◽  
Short Explanation

This chapter introduces different resources about noise in heart signals. It also provides a short explanation about artificial neural network (ANN), particle swarm optimization (PSO), and presents some of the previous studies related to heart signal noise removal, intelligent methods for detection of disorders, and feature extraction.

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