The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in)

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

Research includes three axes, the first is the average estimate time of achievement (day) to work oversight, to five supervisory departments in the Office of Financial Supervision Federal and then choose the three control outputs and at the level of each of the five departments above, and after analyzing the data statistically back to us that the distribution of the times of achievement It is the exponential distribution (Exponential Distribution) a parameter (q), and the distribution of normal (Normal Distribution) with two parameters (μ, σ²), and introduced four methods of parameter estimation (q) as well as four modalities parameter to estimate (

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

Land use change, particularly the expansion of urban areas and associated human activities at the expense of natural and semi-natural areas, is a major ecological issue in urban areas around the world. Climate change being a very strong additional driver for changing the temperature and habitat in the cities. This also applies to Baghdad, Iraq, where urbanisation and climate change exerts a major pressure on the natural habitats of the city, and thus may affect the ability of city planners to adapt to future climate change scenarios. Here we present evidence of substantial growth in urban areas, increases in temperature, and degradation of natural vegetation within Baghdad city by using Remote Sensing techniques and an assessment for the

Abstract

                  The research seeks to shed light on green accounting information systems, analyze them, identify sustainability reporting and how to improve it, as well as study the importance of the Iraqi oil sector, analyze it, and work on applying green accounting information systems in order to improve the quality of sustainability reporting. Oil as a branch of the General Corporation for the Distribution of Oil and Gas Products to apply the practical aspect and prove the hypothesis of the research. Explaining the company's role in improving environmental conditions

This research introduce a study with application on Principal Component Regression obtained from some of the explainatory variables to limitate Multicollinearity problem among these variables and gain staibilty in their estimations more than those which yield from Ordinary Least Squares. But the cost that we pay in the other hand losing a little power of the estimation of the predictive regression function in explaining the essential variations. A suggested numerical formula has been proposed and applied by the researchers as optimal solution, and vererifing the its efficiency by a program written by the researchers themselves for this porpuse through some creterions: Cumulative Percentage Variance, Coefficient of Determination, Variance