The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w

Ad-Hoc Networks are a generation of networks that are truly wireless, and can be easily constructed without any operator. There are protocols for management of these networks, in which the effectiveness and the important elements in these networks are the Quality of Service (QoS). In this work the evaluation of QoS performance of MANETs is done by comparing the results of using AODV, DSR, OLSR and TORA routing protocols using the Op-Net Modeler, then conduct an extensive set of performance experiments for these protocols with a wide variety of settings. The results show that the best protocol depends on QoS using two types of applications (+ve and –ve QoS in the FIS evaluation). QoS of the protocol varies from one prot

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (k_La), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

Background: In dentistry, dentist takes the advantages of soft lining materials due to the viscoelastic properties. The major problem is the adhesion of the soft liner with the denture base material. Materials and Methods: Heat cured of high impact acrylic resin specimens prepared with dimensions 75x13x13mm for shear bond strength test, soft lining material (Refit and Mollosil) with a 3-mm thickness and used to join each two acrylic blocks. Also four specimens with the same previous dimensions utilized for chemical and physical surface analysis. The specimens grouped as control (without plasma) and experiment (with oxygen plasma) treated high impact acrylic specimens. Results: Plasma treatment increased the shear bond strength for both Refi

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production

In this work, seven soil samples were brought brought to study and analyses the element concentrations from different southern regions of Iraq using laser-induced breakdown spectroscopy (LIBS) technique. It has been documented as an atomic emission spectroscopy (AES) technique. Laser-induced plasma utilized to analyze elements in materials (gases, liquids, and solids). In order to analyze elements in materials (gases, liquids, and solid). The Nd: YAG laser excitation source at 1064 nm with pulse width 9 ns is used to generate power density of 5.5 x 1012 MW/mm2, with optical spectrum in the range 320-740 nm. From this investigation, the soil sample analysis of the southern cities of Iraqi, it is concluded that the rich soil element of P, Si,