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­­Image Segmentation using Gaussian Mixture Model
R. Farnoosh, B. Zarpak ,
Volume 19, Issue 1 (3-2008)
Abstract

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.

  In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numerically method was introduced for finding the maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we were made a sequence of priors, posteriors were made and then converged to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability which can make labeled image. This labeled image shows our segmented image with reduced noises. We presented this method in several experiments.


IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Rahman Farnoosh, Behnam Zarpak,
Volume 19, Issue 1 (3-2008)
Abstract

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.

  In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, we introduce a new numerically method of finding maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we have made a sequence of priors, posteriors and they converge to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability that will make labeled image. This labeled image shows our segmented image with reduced noises. We show this method in several experiments.


Bayesian Estimation of Reliability of the Electronic Components Using Censored Data from Weibull Distribution: Different Prior Distributions
Fernando Antonio Moala,
Volume 25, Issue 4 (10-2014)
Abstract

The Weibull distribution has been widely used in survival and engineering reliability analysis. In life testing experiments is fairly common practice to terminate the experiment before all the items have failed, that means the data are censored. Thus, the main objective of this paper is to estimate the reliability function of the Weibull distribution with uncensored and censored data by using Bayesian estimation. Usually it is assigned prior distributions for the parameters (shape and scale) of the Weibull distribution. Instead, we assign prior distributions for the reliability function for a fixed time, that is, for the parameter of interest. For this, we propose different non-informative prior distributions for the reliability function and select the one that provides more accurate estimates. Some examples are introduced to illustrate the methodology and mainly to investigate the performance of the prior distributions proposed in the paper. The Bayesian analysis is conducted based on Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions

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Improving Task Performance through Virtual Team Communication
Laila Refiana Said, Zainal Arifin, Meldasari Said,
Volume 34, Issue 2 (6-2023)
Abstract
Numerous studies have examined the increasing number of virtual team communication usage, especially during the Covid-19 pandemic. However, little research has been conducted on the factors affecting its effectiveness in improving task performance, seeing the virtual team's rapid development today. Therefore, this study examines the effect of direct and indirect employee preferences and organizational support on task performance through virtual teamwork communication. The research method used was a survey of 156 employees in the fields of education, telecommunications, transportation, and health in Banjarmasin city, who work from home, interact with colleagues who also work from home, and with colleagues who work in the office. The analysis was carried out using path analysis. The results showed that employee preferences and organizational support directly affected task performance. Virtual team communication can mediate the influence of employee preferences and organizational support on task performance. The research implies that virtual team communication that runs well can improve work performance. Therefore, it requires collaborative support, both from individuals and the organization.
 
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