The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.

This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network. Cascade-forward neural

The increase the rates of natural growth, urbanization and continuous migration, this has generated constant pressure and, as a result, the capital city of Baghdad faces a number of challenges related to its urban environment, including the challenge of acquiring real estate.

 and this research examines the impact of these holdings, representing the main base from which the various projects originate Urban in all areas (economic, social, and recreational).

this leads us to the research problem of the obstacles that arise during the process of acquiring real estate to carry out development projects, and to achieve the objectives of research, namely (work to create a regulatory methodology

The research aims to find out the impact of wages and benefits systems on the performance of employees, which included the research community on a sample of employees in the company, and the sample consisted of (50) employees and an employee, A questionnaire composed as prepared (23) paragraph, use the promised statistically methods in data collected by the questionnaire analysis. The research reached a number of results, the most prominent of which were: There is a correlation between wage systems, benefits and performance of employees, and the presence of the impact of the systems of wages and benefits to the performance of employees. The research was presented a set of recommendations including: increasing the effectiveness of

  The aim of this study was to  measure the concentration of some heavy metals (Lead, Cadmium ,Iron and Cupper) in blood samples  of workers who operate the diesel generator in Baghdad city(Dora, Harthya, Bayaa and Hay al Jameaa ) ,and their effects on Testosterone hormone. The study included two groups: the first control group consisted of 20 normal male (non workers and non smoking), and the second consisted of 20 male (generator workers). Blood samples were collected from each two groups, where each sample was divided into two parts, the first part estimated blood concentrations of heavy metals while the second tested testosterone. Data were treated statistically using (Statistical Analysis System 2012) program, and the

Objectives: The study aims to evaluate the effectiveness of the educational program on nurses’ knowledge towards nursing management for patients undergoing percutaneous coronary intervention (PCI), as well as to find out the relationship between nurses' knowledge and some of their demographic characteristics (age, gender, level of education, and years of experience in cardiac units).

Methodology:  A Quasi-experimental as one group (pre and post test) study was conducted at the Heart Center in Al-Diwaniyah city for the period from December 7, 2019 to February 23, 2020. A sample of (40) nurses working in the heart center was chosen from different nursing addresses. The sample covered one gro

Three hundred Iraqi people participated in demographic and attitudes study about red and white meat consumption. The mean age of the participants was 50 SD ± 11 years (mean 30-72); 51% were females and 49% males, mostly in forties who lived ≥ 5 years in Baghdad. The results showed that 80% of individuals prefer red meat. A 90% of people prefer fresh meat compared to frozen and processed meat. A 60% of people buy meat from popular markets. Nearly 87% of respondents believe the improving of livestock sector is essential and 80% of people confirmed there are obstacles to development this sector. An 80% of participates thought the reasons of the high prices of local fresh meat is the lack of planning and support to livestock sector. A survey

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.