The vast majority of EC applications are the web-based deployed in 3-tire Server-Client environment, the data within such application often resides within several heterogeneous data sources. Building a single application that can access each data sources can be a matter of challenging; this paper concerns with developing a software program that runs transparently against heterogeneous environment for an EC-application.
 

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.

In the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through tha

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

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.

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

Nowadays power systems are huge networks that consist of electrical energy sources, static and lumped load components, connected over long distances by A.C. transmission lines. Voltage improvement is an important aspect of the power system. If the issue is not dealt with properly, may lead to voltage collapse.  In this paper, HVDC links/bipolar connections were inserted in a power system in order to improve the voltage profile. The load flow was simulated by Electrical Transient Analyzer Program (ETAP.16) program in which Newton- Raphson method is used. The load flow simulation studies show a significant enhancement of the power system performance after applying HVDC links on Kurdistan power systems. Th