This study is an attempt to investigate the conceptual metaphor of UP and DOWN commonly used in Iraqi community. Some of the metaphorical expressions in Iraqi colloquial Arabicarewidely used by Iraqi speakers in everyday language. Ithas been analyzed by following the cognitive theory of metaphor (Lakoff& Johnson,1980).The study indicates that the Iraqi speakerexperiences more of the metaphorical expressions of UP and DOWN to referto many of the abstract concepts that shape his/her impression of everyday life situations.

Abstract:

 In its theoretical farm, the research adobet  the subject of human resource management strategies  and the cognitive skills. It comes as result of the rapid development which considers it human resource. as main axis in organization , the research in includes ,connective analysis  , between  human resource management strategies  and cognitive skills which is considers one of the new concept that should  be  studied widly so that the organization can be able recognize it as concept and type and its importance for people in the organization.The study method was descriptive and  analytics , it identified collation of hypothesis which were by statist too

The present paper is a qualitative descriptive study. It aims to examine the macro-cultural schemata addressing the concept of violence against women in Iraq from a cognitive linguistic point of view. To meet this objective, a number of Iraqi social caricatures have been selected from two popular and active Iraqi caricaturists, Odeh Al-Fahdawi and Nasser Ibrahim. The selection and the analysis of data have been achieved following the validity and reliability procedures and the ethical considerations. To meet this objective, Sharifian’s Model (2011) of Cultural Schemata has been adopted in data analysis. The study has concluded that the macro-cultural schemata regarding the concept of violence in the selected panels reveals that violenc

The study aims to identify the cognitive bias and the level of emotional thinking among university students, besides, identifying the significant differences between male and female students regarding those two variables, and determine if there is a correlation between cognitive bias and emotional thinking. To this end, two scales were adopted to collect needed data: cognitive bias scale designed by (Al-any, 2015), composed of (14) items, and emotional thinking scale designed by (Abdu Allah, 2017), consisted of (27) items. These two scales were administered to (140) students composed the study sample. They were chosen from four different colleges at Al-mustansiriyah University for the academic year (2017-1018). The findings revealed that

The aim of the current research is to verify the effect of the cognitive modeling strategy on the achievement of the chemistry course for the students of the first intermediate grade. To achieve the objective of the research, the null hypothesis was formulated via cognitive modeling strategy. The results showed that the experimental group's students performed better than the students in the control group. In the light of the results, the researchers concluded: The impact of the cognitive modeling strategy in the achievement of students of first intermediate grade in chemistry.

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are