The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera

The study aims to identify the relationship between forgiveness and social intelligence among elementary school students. The study employed a descriptive analytical approach, whereby a total of (500) elementary school student were selected randomly regarding the variable of gender and economical status. Two scales were prepared: one to measure the forgiveness depending on Albort’s theory that consist of (20) item, and the other to measure the social intelligence according to Tony’s theory which composed of (20) item as well. The result revealed that 6^th grade students have interested level of the forgiveness and social intelligence, the girl showed significant differences according to the forgiveness variable, the sample

This study aims to reveal the role of one of the artificial intelligence (AI) techniques, “ChatGPT,” in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in b

Spatial Intelligence is a mental ability to understand and solve real-world problems. These visual-spatial representations are fundamental in learning various "STEM" topics, like digital drawing, art presentations, creating graphical representations, 2D designs. Opportunity to interact with real and/or virtual objects. It is a good opportunity in applying new techniques such as the augmenter, which is able to clarify mathematical tables, concepts and generalizations greatly to the visualization, understanding and mastery of concepts mathematically. The purpose of the research is to investigate impact of using AR technology in developing spatial intelligence for secondary school students, Baghdad. The quasi-experimental design was us

It is often needed in demographic research to modern statistical tools are flexible and convenient to keep up with the type of data available in Iraq in terms of the passage of the country far from periods of war and economic sanctions and instability of the security for a period of time . So, This research aims to propose the use of style nonparametric splines as a substitute for some of the compounds of analysis within the model Lee-Carter your appreciation rate for fertility detailed variable response in Iraq than the period (1977 - 2011) , and then predict for the period (2012-2031). This goal was achieved using a style nonparametric decomposition of singular value vehicles using the main deltoid , and then estimate the effect of time-s

The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very

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.    

   The technology of reducing dimensions and choosing variables are very important topics in statistical analysis to multivariate. When two or more of the predictor variables are linked in the complete or incomplete regression relationships, a problem of multicollinearity are occurred which consist of the breach of one basic assumptions of the ordinary least squares method with incorrect estimates results.

 There are several methods proposed to address this problem, including the partial least squares (PLS), used to reduce dimensional regression analysis. By using linear transformations that convert a set of variables associated with a high link to a set of new independent variables and unr