This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

The idea of ​​carrying out research on incomplete data came from the circumstances of our dear country and the horrors of war, which resulted in the missing of many important data and in all aspects of economic, natural, health, scientific life, etc.,. The reasons for the missing are different, including what is outside the will of the concerned or be the will of the concerned, which is planned for that because of the cost or risk or because of the lack of possibilities for inspection. The missing data in this study were processed using Principal Component  Analysis and self-organizing map methods using simulation. The variables of child health and variables affecting children's health were taken into account: breastfeed

In the last two decades, arid and semi-arid regions of China suffered rapid changes in the Land Use/Cover Change (LUCC) due to increasing demand on food, resulting from growing population. In the process of this study, we established the land use/cover classification in addition to remote sensing characteristics. This was done by analysis of the dynamics of (LUCC) in Zhengzhou area for the period 1988-2006. Interpretation of a laminar extraction technique was implied in the identification of typical attributes of land use/cover types. A prominent result of the study indicates a gradual development in urbanization giving a gradual reduction in crop field area, due to the progressive economy in Zhengzhou. The results also reflect degradati

Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based dat

Abstract:

Since the railway transport sector is very important in many countries of the world, we have tried through this research to study the production function of this sector and to indicate the level of productivity under which it operates.

It was found through the estimation and analysis of the production function Kub - Duglas that the railway transport sector in Iraq suffers from a decline in the level of productivity, which was reflected in the deterioration of the level of services provided for the transport of passengers and goods. This led to the loss of the sector of importance in supporting the national economy and the reluctance of most passengers an

This study aims to analyze the flow migration of individuals between Iraqi governorates using real anonymized data from Korek Telecom company in Iraq. The purpose of this analysis is to understand the connection structure and the attractiveness of these governorates through examining the flow migration and population densities. Hence, they are classified based on the human migration at a particular period. The mobile phone data of type Call Detailed Records (CDRs) have been observed, which fall in a 6-month period during COVID-19 in the year 2020-2021. So, according to the CDRs nature, the well-known spatiotemporal algorithms: the radiation model and the gravity model were applied to analyze these data, and they are turned out to be comp

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.    

BACKGROUND: Many genetic factors are known to be related to osteoporosis, and currently the role of the glucagon-like peptide-1 receptor (GLP-1R) gene in bone health has been studied intensively. Some variation of this gene, such as rs1042044 and rs6458093, are known to be linked to metabolic diseases and lower bone mineral density, however their specific contribution to osteoporosis remains largely unexplored. Therefore, this study was conducted to investigate the combined genotypic effect of rs1042044 and rs6458093 as a genetic risk factor for osteoporosis in postmenopausal Iraqi women.METHODS: Blood samples from 75 osteoporosis patients and 75 healthy controls, aged 45-85, were collected. DNA was extracted, and a region of GLP-1R