This study aims to clarify the role of Iraqi satellite channels in spreading negative values ​​among university youth; and the tendency of this segment to simulate the descending behaviors and pseudo-peculiar concepts of our society, which are displayed through the screens of these channels, based on the relevant media literature such as scientific references and the results of previous studies and research.

The study followed the survey methodology to examine the public based on the questionnaire as a research tool, which was distributed to a sample of male and female students of Baghdad University enrolled in the university for the academic year 2011-2012.

In order to achieve the specific objectives of this research

The search (floors and transparent role in the design studios satellite channels) and presented as a study-oriented, and the research aims to identify the role of flooring transparent spaces studios satellite channels and side performative and aesthetic, and formulas design to highlight the role floors transparent spaces studios satellite channels. And highlight the importance of research, particularly in its contribution to the clarification of the concept of the relationship between transparency and performing aesthetic treatments for floors by clarifying its role in the designs of the internal spaces of the studios, as well as his contribution to the founding of the theory of looking at the base of such concepts. To achieve the object

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t

The study area of Baghdad region and nearby areas lies within the central part of the Mesopotamia plain. It covers about 5700 Km2. The remote sensing techniques are used in order to produce possible Land Use – Land Cover (LULC) map for Baghdad region and nearby areas depending on Landsat TM satellite image 2007. The classification procedure which was developed by USGS used and followed with field checking in 2010. Land Use-land cover digital map is created depending on maximum likelihood classifications (ML) of TM image using ERDAS 9.2.The LULC raster image is converted to vector structure, using Arc GIS 9.3 Program in order to create a digital LULC map. This study showed it is possible to produce a digital map of LULC and it can be co

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe

      This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases.  Medical images of different cases of kidney diseases were compared with those of   healthy cases. Four different kidneys disorders, such as stones, tumors (cancer), cysts, and renal fibrosis were considered in additional to healthy tissues. This method helps in differentiating between the healthy and diseased kidney tissues. It can detect tumors in its very early stages, before they grow large enough to be seen by the human eye. The method used for segmentation and texture analysis was the k-means with co-occurrence matrix. The k-means separates the healthy classes and the tumor classes, and the affected

Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Steganography is one of the most popular techniques for data hiding in the different media such as images, audio or video files. This paper introduced the improved technique to hide the secret message using the LSB algorithm inside the RGB true color image by encrypting it using the secret key transformation function. The key is selecting randomly in the GF (2n) with condition it has an inverse value to retrieve the encrypted message. Only two bits are used for the low byte in each pixel (the blue byte) to hide the secret message, since the blue color has a weak effect on human eyes. The message hidden by the suggested algorithm is less vulnerable to be stolen than other similar applications.