Prediction Length of Stay with Neural Network Trained by Particle Swarm Optimization

2017 ◽  
Vol 2 (2) ◽  
pp. 21-38
Author(s):  
Azadeh Oliyaei ◽  
Zahra Aghababaee
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Length Of Stay ◽  
Missing Values ◽  
Negative Impact ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Real World Data ◽  
Swarm Optimization ◽  
Percent Accuracy

This article describes how the prediction of the length of stay demonstrates the severity of the disease as well as the practice patterns of hospitals. Also, it helps the hospital resources management provide better services for inpatients and increase inpatients' satisfaction. In this article, an efficient model based on neural network algorithms is trained by a stochastic optimization technique called particle swarm optimization is proposed to predict the length of stay for coronary artery diseases. Real world data is used to generate the model. According to the number of missing values, some policies are considered. Since the outlier data has negative impact on the prediction model, it would be eliminated. The parameters of the proposed model are adjusted by Taguchi method. The applied algorithm evaluation result on the test data indicates that the model has the capability to predict the length of stay with 90 percent accuracy.

scholarly journals Particle Swarm Optimization to Obtain Weights in Neural Network

MATEMATIKA ◽  
2019 ◽  
Vol 35 (3) ◽  
Author(s):  
Budi Warsito ◽  
Hasbi Yasin ◽  
Alan Prahutama
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Hidden Layer

This research discusses the use of a class of heuristic optimization to obtain the weights in neural network model for time series prediction. In this case, Feed Forward Neural Network (FFNN) was chosen as the class of network architecture. The heuristic algorithm determined to obtain the weights in network was Particle Swarm Optimization (PSO). It is a non-gradient optimization technique. This method was used for optimizing the connection weights of network. The lags used as the input were selected based on the strong relationship with the current. The eight architectures were conducted to improve the accuracy of the neural network model. In each architecture, we repeated the running thirty times to get the statistics of mean and variance. The comparison of the performance of various architectures based on the minimum MSE and the stability of the results is presented in this paper. The optimal number of neurons in hidden layer was determined by these criteria. The proposed procedure was applied in air pollution data, i.e. Solid Particulate Matter (SPM). The results showed that the proposed procedure gave promising results in terms of prediction accuracy. A few neurons in hidden layer are strongly recommended in choosing the optimal architecture.

scholarly journals Tuning Artificial Neural Network Controller Using Particle Swarm Optimization Technique for Nonlinear System

2021 ◽  
Author(s):  
Sabrine Slama ◽  
Ayachi Errachdi ◽  
Mohamed Benrejeb
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neural Network Controller ◽  
Network Controller ◽  
Artificial Neural

This chapter proposes an optimization technique of Artificial Neural Network (ANN) controller, of single-input single-output time-varying discrete nonlinear system. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in ANN to avoid any possibilities from local extreme condition. Further, a PSO based neural network controller is also developed to be integrated with the designed system to control a nonlinear systems. The simulation results of an example of nonlinear system demonstrate the effectiveness of the proposed approach using Particle Swarm Optimization approach in terms of reduced oscillations compared to classical neural network optimization method. MATLAB was used as simulation tool.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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