Background: Chief complaint of patients attending dental clinic represents the first step towards treatment plan. However, most of patients are not aware but the extent and severity of periodontal disease, which could be also, misdiagnose by the dentist. Aim of the study: To investigate whether reported chief complaint(s) are consistent with oral hygiene status Materials and methods: Records of 1102 patients, attending periodontics clinics in the college of dentistry/ university of Baghdad, were used to determine ten most commonly reported chief complaints. Sample of patients was further subdivided according to gender and age. In addition, plaque and gingival index were recorded to determine oral hygiene status. Results: Patients mostly

A stochastic process {Xk, k = 1, 2, ...} is a doubly geometric stochastic process if there exists the ratio (a > 0) and the positive function (h(k) > 0), so that {α 1 h-k }; k ak X k = 1, 2, ... is a generalization of a geometric stochastic process. This process is stochastically monotone and can be used to model a point process with multiple trends. In this paper, we use nonparametric methods to investigate statistical inference for doubly geometric stochastic processes. A graphical technique for determining whether a process is in agreement with a doubly geometric stochastic process is proposed. Further, we can estimate the parameters a, b, μ and σ2 of the doubly geometric stochastic process by using the least squares estimate for Xk a

This paper deal with the estimation of the shape parameter (a) of Generalized Exponential (GE) distribution when the scale parameter (l) is known via preliminary test single stage shrinkage estimator (SSSE) when a prior knowledge (a₀) a vailable about the shape parameter as initial value due past experiences as well as suitable region (R) for testing this prior knowledge.

The Expression for the Bias, Mean squared error [MSE] and Relative Efficiency [R.Eff(×)] for the proposed estimator are derived. Numerical results about beha

The present research deals with the spatial variance analysis in Jwartadistrict and conducting a comparison on the spatial and seasonal changes of the vegetation cover between (2007-2013) in order to deduce the relationship between the vegetation density and the areas which are exposed to the risk of water erosion by using Plant Variation Index  NDVI) C (coefficient and by using Satellite images of Landsat satellite which are taken in 2/7/2007 and Satellite images of Landsat satellite taken in 11/1/ 2013, the programs of remote sensitivity and the Geographic Information Systems.

    The study reveals that there is a variance in the density of vegetation cover of the area under study betwee 2007 and 2013. Howev

Background: Speckle tracking echocardiography (STE)-derived mitral annular displacement (MAD) utilizes the speckle tracking technique to measure strain vectors, which provides accurate estimates of left ventricular ejection fraction (LVEF).Objectives: To validate the accuracy of mitral annular displacement (MAD), assessed by Speckle Tracking Echocardiography (STE), as a surrogate for determination of left ventricular systolic function in comparison to 2-Dimensions Simpson method in patients with different heart diseases.Methods : This cross-sectional study included patients who referred to outpatient department of Ibn Albitar Center for Cardiac Surgery, Baghdad, Iraq, between October 2012 and April 2013. STE continuously tracked annular

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer