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Development and control of shaped metal deposition process using tungsten inert gas arc heat source in additive layered manufacturing
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Tungsten inert gas arc welding–based shaped metal deposition is a novel additive manufacturing technology which can be used for fabricating solid dense parts by melting a cold wire on a substrate in a layer-by-layer manner via continuous DC arc heat. The shaped metal deposition method would be an alternative way to traditional manufacturing methods, especially for complex featured and large-scale solid parts manufacturing, and it is particularly used for aerospace structural components, manufacturing, and repairing of die/molds and middle-sized dense parts. This article presents the designing, constructing, and controlling of an additive manufacturing system using tungsten inert gas plus wire–based shaped metal deposition method. The aim of this work is to design and develop tungsten inert gas plus wire–based shaped metal deposition system to be used for fabricating different components directly from computer-aided design data with minimum time consumed in programming and less boring task compared to conventional robotic systems. So, this article covers the important design steps from computer-aided design data to the final deposited part. The developed additive system is capable of producing near-net-shaped components of sizes not exceeding 400 mm in three-dimensional directly from computer-aided design drawing. The results showed that the developed system succeeded to produce near-net-shaped parts for various features of SS308LSi components. Additionally, workshop tests have been conducted in order to verify the capability and reliability of the developed additive manufacturing system. The developed system is also capable of reducing the buy-to-fly ratio from 5 to 2 by reducing waste material from 1717 to 268 g for the sample components.

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Publication Date
Sat Mar 10 2012
Journal Name
الدنانير
Cryptography Using Artificial Neural Network
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Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
strong criminal capabilities، Using simulation .
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The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

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Publication Date
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Best Multiplier Approximation of Unbounded Periodic Functions in L_(p,∅_n ) (B),B=[0,2π] Using Discrete Linear Positive Operators
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The purpose of this paper is to find the best multiplier approximation of unbounded functions in    –space by using some discrete linear positive operators. Also we will estimate the degree of the best multiplier approximation in term of modulus of continuity and the averaged modulus.

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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Evaluation of Common Stocks Using The Fama-French Five Factor Model An Applied Study in The Iraq Stock Exchange
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     The process of stocks evaluating considered as a one of challenges for the financial analysis, since the evaluating focuses on define the current value for the cash flows which the shareholders expected to have. Due to the importance of this subject, the current research aims to choose Fama & French five factors Model to evaluate the common stocks to define the Model accuracy in Fama& French for 2014. It has been used factors of volume, book value to market value, Profitability and investment, in addition to Beta coefficient which used in capital assets pricing Model as a scale for Fama & French five factors Model. The research sample included 11 banks listed in Iraq stock market which have me

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Publication Date
Thu Dec 06 2018
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Comparing the Quality of Life among Patients with Relapsing Remitting Multiple Sclerosis in Iraq Using Different Disease Modifying Therapies
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Multiple sclerosis (MS) is a chronic, inflammatory, immune mediated disease of the central nervous system, mostly affecting young adults with mean age of 30 years, twice as high in women compared to men. The etiology of MS is not fully elucidated. MS symptoms are directly related to demyelination and axonal loss, along with other psychological symptoms, can result in functional limitations, disability and reduced quality of life (QoL).  The QoL assessments in patients with a chronic disease may contribute to improving treatment and could even be of prognostic value. The goals  of this study were  to  compare the QoL of  Iraqi patients with relapsing remitting multiple sclerosis (RRMS),using three different diseas

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Publication Date
Fri Dec 01 2023
Journal Name
Applied Energy
Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent
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The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

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Publication Date
Sun Aug 01 2021
Journal Name
Eurasian Chemical Communications
New sensitive turbidimetric method for determination of losartan potassium in pharmaceutical formulations using flow injection combined with homemade turbidimeter
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Publication Date
Sat Aug 23 2025
Journal Name
Mustansiriyah University
Inhibition the Expression of fimC, fimD, fimH Genes in Uropathogenic E. coli Using TiO2 Nanoparticles Biosynthesized by Probiotics Bacteria
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المستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة

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Publication Date
Thu Dec 15 2022
Journal Name
Journal Of Petroleum Research And Studies
Selection of an Optimum Drilling Fluid Model to Enhance Mud Hydraulic System Using Neural Networks in Iraqi Oil Field
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In drilling processes, the rheological properties pointed to the nature of the run-off and the composition of the drilling mud. Drilling mud performance can be assessed for solving the problems of the hole cleaning, fluid management, and hydraulics controls. The rheology factors are typically termed through the following parameters: Yield Point (Yp) and Plastic Viscosity (μp). The relation of (YP/ μp) is used for measuring of levelling for flow. High YP/ μp percentages are responsible for well cuttings transportation through laminar flow. The adequate values of (YP/ μp) are between 0 to 1 for the rheological models which used in drilling. This is what appeared in most of the models that were used in this study. The pressure loss

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