Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

(Use of models of game theory in determining the policies to maximize profits for the Pepsi Cola and Coca-Cola in the province of Baghdad)

Due to the importance of the theory of games especially theories of oligopoly in the study of the reality of competition among companies or governments and others the researcher linked theories of oligopoly to Econometrics to include all the policies used by companies after these theories were based on price and quantity only the researcher applied these theories to data taken from Pepsi Cola and Coca-Cola In Baghdad Steps of the solution where stated for the models proposed and solutions where found to be balance points is for the two companies according to the princi

   Nonlinear regression models are important tools for solving optimization problems. As traditional techniques would fail to reach satisfactory solutions for the parameter estimation problem.  Hence, in this paper, the BAT algorithm  to estimate the parameters of  Nonlinear Regression models is used . The simulation study is considered to investigate the performance of the proposed algorithm with the maximum likelihood (MLE) and Least square (LS) methods. The results show that the Bat algorithm provides accurate estimation and it is satisfactory for the parameter estimation of the nonlinear regression models than MLE and LS methods depend on Mean Square error.

Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we eva

The aim of this study is to determine the level of pollution with heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) and their potential sources in dust samples collected from schools in Ramadi City, Iraq. The dust samples were collected from 40 primary schools and two kindergartens and analyzed by using atomic absorption spectrophotometer. The heavy metal concentrations were found to follow the order Cr > Cu > Pb > Ni > Zn > Cd.  The results indicated that the concentrations of Cd, Cu, and Pb exceeded the permitted background values. The pollution level was assessed using the geo-accumulation index (I_geo) and pollution load index (PLI). The classification of dust samples according to I_geo values showed that

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime

Cuneiform symbols recognition represents a complicated task in pattern recognition  and image analysis as a result of problems that related to cuneiform symbols like distortion and unwanted objects that associated with applying Binrizetion process like spots and writing lines. This paper aims to present new proposed algorithms to solve these problems for reaching uniform results about cuneiform symbols recognition that related to (select appropriate Binerized method, erased writing lines and spots) based on statistical Skewness measure, image morphology and distance transform concepts. The experiment results show that our proposed algorithms have excellent result and can be adopted

Algorithms using the second order of B -splines [B (x)] and the third order of B -splines [B,3(x)] are derived to solve 1' , 2nd and 3rd linear Fredholm integro-differential equations (F1DEs). These new procedures have all the useful properties of B -spline function and can be used comparatively greater computational ease and efficiency.The results of these algorithms are compared with the cubic spline function.Two numerical examples are given for conciliated the results of this method.