Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit

The current study aims to investigate the effect of strategic knowledge management practices on an excellent performance at the Institution of Industrial Development and Research- the Ministry of Iraqi Industry (IDRMII). The present research is designed according to the descriptive method. To achieve the mentioned research objective, the researchers used the questionnaire as the main data collection tool. The research sample was 150 managers who are working at the top and middle management levels. To analyses the data gathered and reaching the results, several statistical techniques were used within AMOS.V25, SPSS.V21Software, This study reached a set of results, the most important of which is the existence of a positive correlat

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.

The current study designed to determine the effect of Glucosamine sulfate on the liver tissue of Albino mice .the study included (40)mice divided in to 4 groups(control group had distilled water orally ).The other groups treated with(1000,2000,3000)ml/k .respectively for 8 week .the liver have been taken from dissected animal for microscopic preparation to study the histological changes .Frequently histopathologicale changes appeared in the liver tissue of the exposure groups during (4-8)week .This changes depends on (Dose and Time ). The effects were Congestion ,Infiltration ,Swelling ,Vaculation ,Hyalinization , Amyloid and Necrosis.

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.

The current study designed to determine the effect of Glucosamine sulfate on the liver tissue of Albino mice .the study included (40)mice divided in to 4 groups(control group had distilled water orally ).The other groups treated with(1000,2000,3000)ml/k .respectively for 8 week .the liver have been taken from dissected animal for microscopic preparation to study the histological changes .Frequently histopathologicale changes appeared in the liver tissue of the exposure groups during (4-8)week .This changes depends on (Dose and Time ). The effects were Congestion ,Infiltration ,Swelling ,Vaculation ,Hyalinization , Amyloid and Necrosis.

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.