In this research, we use fuzzy nonparametric methods based on some smoothing techniques, were applied to real data on the Iraqi stock market especially the data about Baghdad company for soft drinks for the year (2016) for the period (1/1/2016-31/12/2016) .A sample of (148) observations was obtained in order to construct a model of the relationship between the stock prices (Low, high, modal) and the traded value by comparing the results of the criterion (G.O.F.) for three techniques , we note that the lowest value for this criterion was for the K-Nearest Neighbor at Gaussian function .

The research aimed to study the role that the media play in shaping the public knowledge of human rights issues among the people of Kirkuk, which will be the focus of the study. The research was conducted by applying a survey panel to a random sample of the city's audience. The research dealt with the theoretical aspect of a theory that relied on the media, and the loans provided by the theory, on the basis of which the research was conducted and the research problem was determined based on a major question: What is the role that the mass media play in developing the knowledge of members of the public on human rights and the relationship between the intensity of view in that, as well as the identification of the effect of two variables G

Violence is a very serious phenomenon affecting the upbringing and culture kids, are playing the most violent forms of development of these Phenomenon, the research aims to determine the role of the media in reducing violence, and conducted a field study on parents Children by 200 form, the results revealed the importance of the media in raising awareness of the risks of violence on the child  Especially television, which hugely increased Show features, as well as the role of parents in guiding the child to buy Useful because the game to play negative impact of violence on children's health, and the media play an important role in raising awareness of these Risks through educational programs and television commercials, and o

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r