scholarly journals Estimation of Parameters of Parathyroid Glands Using Particle Swarm Optimization and Multivariate Generalized Gaussian Function Mixture

2019 ◽  
Vol 9 (21) ◽  
pp. 4511 ◽  
Author(s):  
Maria H. Listewnik ◽  
Hanna Piwowarska-Bilska ◽  
Krzysztof Safranow ◽  
Jacek Iwanowski ◽  
Maria Laszczyńska ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Photon Emission ◽  
Gaussian Function ◽  
Particle Swarm ◽  
Position Estimation ◽  
Parathyroid Glands ◽  
Emission Computed Tomography ◽  
Swarm Optimization ◽  
Large Patient ◽  
Neighborhood Topology

The paper introduces a fitting method for Single-Photon Emission Computed Tomography (SPECT) images of parathyroid glands using generalized Gaussian function for quantitative assessment of preoperative parathyroid SPECT/CT scintigraphy results in a large patient cohort. Parathyroid glands are very small for SPECT acquisition and the overlapping of 3D distributions was observed. The application of multivariate generalized Gaussian function mixture allows modeling, but results depend on the optimization algorithm. Particle Swarm Optimization (PSO) with global best, ring, and random neighborhood topologies were compared. The obtained results show benefits of random neighborhood topology that gives a smaller error for 3D position and the position estimation was improved by about 3 % voxel size, but the most important is the reduction of processing time to a few minutes, compared to a few hours in relation to the random walk algorithm. Moreover, the frequency of obtaining low MSE values was more than two times higher for this topology. The presented method based on random neighborhood topology allows quantifying activity in a specific voxel in a short time and could be applied it in clinical practice.

Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

2013 ◽  
Vol 333-335 ◽  
pp. 1384-1387
Author(s):  
Jin Jie Yao ◽  
Xiang Ju ◽  
Li Ming Wang ◽  
Jin Xiao Pan ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Position Estimation ◽  
Target Localization ◽  
Prediction Ability ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.

scholarly journals Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1239
Author(s):  
Fatih Ecer ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
Amir Mosavi
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Population Size ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Gaussian Function ◽  
Particle Swarm ◽  
Series Data ◽  
Output Function ◽  
Swarm Optimization

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

Dynamic Small World Network Topology for Particle Swarm Optimization

2016 ◽  
Vol 30 (09) ◽  
pp. 1660009 ◽  
Author(s):  
Qingxue Liu ◽  
Barend Jacobus van Wyk ◽  
Shengzhi Du ◽  
Yanxia Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Small World ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Small World Network ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Dynamic Topology ◽  
Neighborhood Topology

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

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