This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f

The Al Mishraq site has been the subject of many scientific studies for the period before and
after the fire in 2003. Five visits to the site were conducted twice in 2003 for general fact-finding, twice
in 2004, and once in 2005 for detailed sampling and monitoring. Desk-based research and laboratory analysis of soil and water samples results indicate that surface water and groundwater pollution from Al Mishraq site was significant at the time of its operation. The primary pollution source was the superheated water injection process, while the principal receptor is the River Tigris. Now that the plant is idle, this source is absent. Following the June 2003 sulphur fire, initial investigations indicate that short damage to
vegeta

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

Abstract

The current research aims to identify the effect of the proposed strategy in accordance with realistic mathematics on the achievement and mathematical Interrelation of third Intermediate students. Two samples were tested from the middle third grade in a school affiliated with the General Directorate of Baghdad- Rusafa, the first for the academic year (2022-2021). The experimental group is (30) students taught according to the proposed strategy, and the control group is (30) students based on the traditional method. To achieve the research objective, the researchers developed a test for achievement consisting of (30) items and a test of sports interconnection composed of (20) items. The results of the stu

Echinococcosis is a zoonotic disease caused by the larval stage of the tapeworm Echinococcus granulosus. This disease is an important public health and a significant economic issue in Iraq, where the lungs and livers are the popular places of infection. The aim of the current study focused on using the molecular techniques in the detection of an E. granulosus strain that causes cystic echinococcosis to human, sheep and cattle in Thi-Qar province, Iraq. In the current study, thirty isolates of E. granulosus were collected from 10 human hydatid cysts through surgery done at Al-Hussein Imam Teaching Hospital in Thi-Qar province and 10 sheep with 10 cattle hydatid cysts were obtained from the slaughterhouse in Thi-

Objectives: to assess chronic diseases patients’ knowledge toward stroke risk factors and warning signs, besides
determining the relationship between chronic diseases patients’ knowledge and their sociodemographical
characteristics.
Methodology: A descriptive study was carried out at public medical clinics which has started from December
2
nd, 2008 to August 8th, 2009. A purposive "non-probability" sample of (300) chronic diseases individuals who
were clients of Public Medical Clinics who have one or more of the following chronic diseases (hypertension,
diabetes mellitus, heart diseases, and previous stroke), in Baghdad city. The data were collected through the use
of a constructed questionnaire which consists