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.

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

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.

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.

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

This research was carried out to study the effect of plants on the wetted area for two soil types in Iraq and predict an equation to determine the wetted radius and depth for two different soil types cultivated with different types of plants, the wetting patterns for the soils were predicted at every thirty minute for a total irrigation time equal to 3 hr. Five defferent discharges of emitter and five initial volumetric soil moisture contents were used ranged between field capacity and wilting point were utilized to simulate the wetting patterns. The simulation of the water flow from a single point emitter was completed by utilized HYDRUS-2D/3D software, version 2.05. Two methods were used in developing equations to predict the domains o

This research is a case study to solve control problems in Al Rasheed edible oil factory fire tube boilers. they have hopes to develop a new control system to manage boilers operation. The suggestion is to use Zelio soft programmable relays instead of the unavailable old control units. Operation philosophy was studied through works of literature, operation manuals, and standards. Programmable logic control relay is proposed as an advanced selection than PLC's. Boilers operation is accompanied by operation risks. many boilers were exploded in Iraq for different reasons. Some problems are attributed to manual operation mistakes. Extensive work was done to understand the operation sequence, emergency shutdown, and faults causing the trips. A c

The raw material soil of Al-Sowera factory quarry (quarry soil and mixture) used for building brick industry was tested mineralogically, geochemically and geotechnically. Mineral components of soil are characterized by Clay minerals (Palygoriskite and chlorite) and nonclay minerals like calcite, quratz, feldspar, gypsum and halite. The raw material is deficient in SiO2, Al2O3, K2O, Fe2O3 and MgO, while enriched in CaO. Loss on ignition and Na2O are in suitable level and appear to be concordant with the standard. Grain size analyses show that the decreasing sand and clay, and increasing silt ratio in both quarry soil and mixture caused decreasing in strength of brick during molding and after firing. The quarry soil is characterized by high p