In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

The historic cities in the Arab and Islamic world (including the cities of the holy shrines in Iraq) suffer from many and varied problems and most notably it is clear in its origin functional structure. It had been transformed from a basic place to live for a few thousand of the population to the center or part of the main center of large city populations of more than one million. Functional structure of the ancient city have been change with the beginning of the twentieth century and accelerated rate of change in the second half of the last century. The research aims to analyze the problems of the historic centers of holy cities and analyze the methods of dealing with these problems, leading to discrimination method could be the

     Terrorist operations disturbed societies and threatened their existence due to destructive ideas carried by those groups that believe in radical change and the use of multiple methods to implement their ideas, as well as taking methods of concealment among society, so it has become difficult to detect them. The study's purpose is to locate sleeper cells of terrorist operations in areas dense with various human activities using remote sensing techniques and geographic information systems programs. The programs were used to analyze the group of terrorist incidents data that occurred in Baghdad in 2011 and study how to limit the crimes spreading in Baghdad. The study included building a model in spatial information systems for site

In this paper we proposed a new method for selecting a smoothing parameter in kernel estimator to estimate a nonparametric regression function in the presence of missing values. The proposed method is based on work on the golden ratio and Surah AL-E-Imran in the Qur'an. Simulation experiments were conducted to study a small sample behavior. The results proved the superiority the proposed on the competition method for selecting smoothing parameter.

  In this paper we introduce a new class of degree of best algebraic approximation polynomial Α,, for unbounded functions in weighted space Lp,α(X), 1 ∞ .We shall prove direct and converse theorems for best algebraic approximation in terms modulus of smoothness in weighted space

Due to the popularity of radar, receivers often “hear” a great number of other transmitters in
addition to their own return merely in noise. The dealing with the problem of identifying and/or
separating a sum of tens of such pulse trains from a number of different sources are often received on
the one communication channel. It is then of interest to identify which pulses are from which source,
based on the assumption that the different sources have different characteristics. This search deals with a
graphical user interface (GUI) to generate the radar pulse in order to use the required radar signal in any
specified location.

     The Exponentiated Lomax Distribution is considered one of the most commonly used continuous distribution which has a major role in analysing and modelling life time data. Therefore, A family was formed for the Exponential Lomax Distribution by introducing two new distributions as special case of the Exponentiated Lomax Distribution: (Modified Exponentiated Lomax Distribution (MELD) and Restricted Exponentiated Lomax Distribution (RELD. Furthermore, to assess the usefulness and flexibility, the two distributions were applied upon simulation study besides real application with real data set. The simulation results clearly shown the flexible performance of the maximum likelihood estimators for the parameter. Also, the real applicat

Background: Osteoporosis (OP) is a systemic disease characterized by low bone mass and micro architectural deterioration of bone tissue, resulting in an increased risk of fractures and has touched rampant proportions. Osteocalcin, one of the osteoblast-specific proteins, showed that its functions as a hormone improves glucose metabolism and reduces fat mass ratio. This study is aimed to estimate the osteocalcin and glucose level in blood serum of osteoporotic postmenopausal Women with and without Type 2 Diabetes.Materials and methods: 60 postmenopausal women with osteoporosis divided into two groups depending on with or without T2DM, 30 patients for each. Serum samples of 30 healthy postmenopausal women were collected as control group. Ost