Um-Al-Naaj region in Al-Hawiezah Marsh, Southern Iraq was chosen to study the environmental variations of some water characteristics during 2008, seasonally. The results showed clear seasonal changes in values of some environmental variables (temperature, depth, light penetration, turbidity, total suspended solids, pH, dissolved oxygen, reactive phosphate, reactive nitrite, and reactive nitrate), while there were no clear seasonal changes in electrical conductivity and salinity values. In addition, high nutrients concentrations and light penetration were noted. Statistical analysis showed significant positive relationship between air and water temperature; electrical conductivity and salinity. Water turbidity was significantly affecte

This study focuses on determining the heterogeneity of Yamama Formation and its spatial distribution in south of Iraq using three indices namely, Coefficient of Variation, Lorenz Coefficient, and Dykstra – Parsons Coefficient. The porosity and permeability values from eleven wells in south of Iraq (Basra and Maysan oil fields) are used for computing heterogeneity indices. Ordinary kriging technique is used to interpolate the computed indices and to show the spatial distribution of these indices over the study area. Results indicated that the average values of Lorenz and Dykstra – Parsons Indices are 0.73 and 0.86, respectively which refer to the extremely heterogeneity nature of Yamama Formation in the study area.The spatial distribu

some ecological (physical and chemical varible) of water samples were studies monthly from December 2008 to May 2009 at two stations( St.1) Al - Chibayesh marsh and (St.2) Abu – Zirik marsh which are located in the south of Iraq . These variables included : Temperature, pH, EC, Dissolved oxygen , Total alkalinity, Nitrate, Sulphate, and phosphate, Si-SiO2 and Ca ,Mg, Cl, The marsh Considered as fresh water and alkaline. Abu-Zirik less than Al-Chibayesh.

          In the present research, a crane frame has been investigated by using finite element method. The damage is simulated by reducing the stiffness of assumed elements with ratios (10% and 20 %) in mid- span of the vertical column in crane frame. The cracked beam with a one-edge and non-propagating crack has been used. Six cases of damage are modeled for crane frame and by introducing cracked elements at different locations with ratio of depth of crack to the height of the beam (a/h) 0.1, 0.20. A FEM program coded in Matlab 6.5 was used to model the numerical simulation of the damage scenarios. The results showed a decreasing in the five natural frequencies from undamaged beam which means

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

In this paper the wind data that is measured for 12 months (January to December 2011) at Al-Hay district of Wasit province, southern IRAQ country has been analyzed statistically. The wind speed at heights of 10 m above ground level was measured for every 10 minutes interval. The statistical analysis of wind data was performed using WAsP software which is based on Weibull distributions. The Weibull shape and scale parameters is obtained and used in this paper statistics. The achieved results demonstrated that the study area has Annual Mean Energy Production (AMEP) about 219.002 MWh. The computations have been performed on 70m hub‟s height of the turbine and on Earth surface roughness length (0.0, 0.03, 0.1, 0.4, 1.5) m respectively.

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

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.

When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every

    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accu