Muthanna Journal of Pure Science (MJPS)

To work in the commercial environment, the company needs to be a major competitor in the business market, which depends mainly on the company's resources. One of the most important resources is the employees. Based on that, the absence of the employees from work leads to deterioration and reduce production in the institutions which leads to heavy losses. There are many reasons why employees are absent from work. Those may include health problems and social occasions. The purpose of this paper was to apply machine learning techniques to predict the absenteeism at work. There are four methods have been used in this research ( neural network(NN) technique ,decision tree (DT) technique, support vector machine (SVM) technique and logistic regression (LR) technique. . decision tree model has the highest accuracy equals to 83.33% with AUC 0.834 and the support vector machine has the lowest accuracy equals to 68.47 % with AUC 0.760.

This paper aims at the modeling the crashes count in Al Muthanna governance using finite mixture model. We use one of the most common MCMC method which is called the Gibbs sampler to implement the Bayesian inference for estimating the model parameters. We perform a simulation study, based on synthetic data, to check the ability of the sampler to find the best estimates of the model. We use the two well-known criteria, which are the AIC and BIC, to determine the best model fitted to the data. Finally, we apply our sampler to model the crashes count in Al Muthanna governance.