This present paper aim at knowing  the process of evaluating the training program that could be applied  in Maysan Health office  for it significance and importance in field of management and vocational staff  preparations  of high  scientific experience in different fields of  Health.  The society of research includes staffs  working in Maysan Health Office  , of specialists ,  dentists, pharmacists, laboratories, nursing  and administrators. Their number is 100 employees, the researcher has designed questionnaire by depending on "Kirkpatrick"  for assessing  the training . The researcher has used   thorough survey and has entailed 90 questionnaire,

The present study aimed to identify the exact location and its relation to cognitive
method (risk_caution) to university students. The sample consisted of (300) students who
were chosen randomly and equally. The study results indicated that students possess an
internal exact location and they also use risk cognitive method. The study also indicated that
there is a prophesies which is an exact location for others to reach to caution cognitive
method. Depending on these results, the study recommended to benefit from the results of the
study variables and from the measurement of the exact location and method of cognitive
(risk_caution) to identify male and female students and especially by consulting units at
college

In this research was to use the method of classic dynamic programming (CDP) and the method of fuzzy dynamic programming (FDP) to controlling the inventory in N periods and only one substance ,in order to minimize the total cost and determining the required quantity in warehouse rusafa principal of the ministry of commerce . A comparison was made between the two techniques، We found that the value of fuzzy total cost is less than that the value of classic total cost

This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe