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An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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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. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

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Publication Date
Mon Nov 05 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Effect of Mechanical Surface Treatment on the Tensile Bond Strength of Repaired Acrylic Denture Base Resin Cured by Two Different Techniques
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Objective: To evaluate and compare the effect of mechanical surface treatment (groove, aluminum oxide particles)
with 45 degree bevel type of joint on tensile bond strength of acrylic specimens repaired by two curing methods
(microwave and water both).
Methodology: Eighty specimens (80) were prepared from pink heat cure acrylic resin. They were divided into two
main groups (40 specimen repaired by microwave energy and 40 specimens repaired by water bath method).Each
group can be divided into four subgroups of ten according to the surface treatment. The control group A was left
intact, group B received no surface treatment, group C and D received surface treatment by (groove, 50 m aluminum
oxide particles). Specimens

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Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Image Compression based on Non-Linear Polynomial Prediction Model
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Publication Date
Fri Nov 01 2024
Journal Name
Heliyon
Strength and Deformation of Encased Concrete Columns by I- Section Steel and I- Section GFRP Subjected to Different Load Conditions
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Publication Date
Mon Dec 28 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
Predicting the Sporting Achievement in the Pole Vault for Men Using Artificial Neural Networks
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The physical sports sector in Iraq suffers from the problem of achieving sports achievements in individual and team games in various Asian and international competitions, for many reasons, including the lack of exploitation of modern, accurate and flexible technologies and means, especially in the field of information technology, especially the technology of artificial neural networks. The main goal of this study is to build an intelligent mathematical model to predict sport achievement in pole vaulting for men, the methodology of the research included the use of five variables as inputs to the neural network, which are Avarage of Speed (m/sec in Before distance 05 meters latest and Distance 05 meters latest, The maximum speed achieved in t

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Modeling of Corrosion Rate Under Two Phase Flow in Horizontal Pipe Using Neural Network
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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

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Publication Date
Thu Dec 28 2017
Journal Name
Al-khwarizmi Engineering Journal
Simulation Study of Mass Transfer Coefficient in Slurry Bubble Column Reactor Using Neural Network
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The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (kLa), 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

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Publication Date
Fri Jan 01 2021
Journal Name
Environmental Pollution
Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models
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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network
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Abstract<p>Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.</p>
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Publication Date
Mon Apr 11 2011
Journal Name
Icgst
Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Publication Date
Tue Mar 26 2024
Journal Name
Scientific Reports
An in vitro assessment of the residual dentin after using three minimally invasive caries removal techniques
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Abstract<p>To evaluate the efficiency and effectiveness of three minimally invasive (MI) techniques in removing deep dentin carious lesions. Forty extracted carious molars were treated by conventional rotary excavation (control), chemomechanical caries removal agent (Brix 3000), ultrasonic abrasion (WOODPECKER, GUILIN, China); and Er, Cr: YSGG laser ablation (BIOLASE San Clemente, CA, USA). The assessments include; the excavation time, DIAGNOdent pen, Raman spectroscopy, Vickers microhardness, and scanning electron microscope combined with energy dispersive X-ray spectroscopy (SEM–EDX). The rotary method recorded the shortest excavation time (p < 0.001), Brix 3000 gel was the slowest. DIAGNOdent pen va</p> ... Show More
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