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A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence
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Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relatively high for 2015-2016-2017. 2018 was utilized as a test year to assess the modeling work and validate the experimental results. In the second step, the artificial neural networks approach employs the python program as an AI, and the affinity ratio of real data using the performance measurement of the mean absolute error (MAE) was 0.005. To improve and reduce the value of absolute error, the genetic algorithm uses the python program and the convergence ratio became 0.001. It inferred that the algorithm is efficient in improving results. Thus, the genetic algorithm provided better results with fewer errors than the neural network alone. This concludes that the shown network has superior performance over others and the possibility of its long-term predictions for 2030. A Sing time series helped detect future cases by reading and inferring system data. The development of appropriate work plans will lower internal and external expenses of the systems and help integrate other capabilities by giving correct data sources of raw materials, costs, etc. To facilitate prediction for maintenance workers, an interface has been created that facilitates users to apply them using the python program represented by entering the times, an hour, a day, a month, a year, to predict the type and place of failure.

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

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Publication Date
Thu Jun 10 2021
Journal Name
Engineering, Technology & Applied Science Research
Corruption Risk Analysis at the Project Planning Stage in the Iraqi Construction Sector using the Bowtie Methodology
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In this paper, the bowtie method was utilized by a multidisciplinary team in the Federal Board of Supreme Audit (FBSA)for the purpose of managing corruption risks threatening the Iraqi construction sector. Corruption in Iraq is a widespread phenomenon that threatens to degrade society and halt the wheel of economic development, so it must be reduced through appropriate strategies. A total of eleven corruption risks have been identified by the involved parties in corruption and were analyzed by using probability and impact matrix and their priority has been ranked. Bowtie analysis was conducted on four factors with high score risk in causing corruption in the planning stage. The number and effectiveness of the existing proactive meas

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Publication Date
Thu Mar 30 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Preventive Effects of Different Doses of Pentoxyfilline Against CCl4-Induced Liver Toxicity in Rats
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The liver protective effects of pentoxifylline were studied through pre-treatment of rats with various intraperitoneal (IP) doses (25, 50 and 100mg/kg/day) 14 days before induction of liver toxicity by carbon tetrachloride (CCl4). The parameters of oxidative stress, malondialdehyde (MDA) and reduced glutathione (GSH) were measured in liver homogenate in addition to histopathological examinations.  Analysis of data revealed significant amelioration of oxidative stress in groups of animals pre-treated with different doses of pentoxifylline (PTX) compared to group of animals intoxicated by CCl4 as evidenced by lowering MDA contents and elevation of GSH levels in liver tissue homogenate but the levels still signifi

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Publication Date
Tue Sep 08 2015
Journal Name
International Journal Of Advanced Multidisciplinary Research
Machinery Unit Energy Requirement and Fuel Consumption Tractor in Operation Tillage
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The study was carried out at field agriculture in Baghdad–Iraq in 2015. For purpose evaluated the performance the selected implements tillage, suitable tire pressure and speed tractor under silt clay loam to measured Effective field capacity, Actual Time for plowing One Donam ( hr), Appearance Tillage ( number of clods > 10 cm), Fuel consumption measure in two unit (L/Donam and L/hr) and Machinery Unit Energy Requirement ( kw.hr / Donam). Split – split plot design under randomized complete block design with three replications using Least Significant Design 5 % was used. Three factor used in this experiment included Two types of plows included Chisel and Disk plows which represented main plot, Three Tires Inflation Pressure was second fa

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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Prof. Mohammad Al-Drubi's Manuscripts Editing Methodology
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Efforts of evaluating manuscripts have developed into sciences that take interest into the development of authorship movement. Expanding the rules and fundamentals of this scientific process along with the growing use of modern methods and techniques contributed further to its development.
Such a disciplinedemands comprehensive knowledge in various fields to reach the most valid results that help reveal significant aspects of the cultural heritage since such a process is an ethical responsibility .Therefore, the editor has to be patient and honest in correcting mistakes, choosing the most acceptable narration and pinpointing the additions and differences as well as other requirements of serious editing.
The study was divided into a

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Publication Date
Tue Sep 27 2022
Journal Name
Al–bahith Al–a'alami
Motives for public exposure to advertisement through smart phone applications and its purchasing decisions relationship (A research drawn from a Master Degree thesis)
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Advertisement on smart phone shopping apps are a new way of driving users to satisfy their needs and influence their purchasing decisions, In this way, the research could be aimed to know The role of the relationship between the motivations for audience exposure to shopping apps advertisement and purchasing decisions, In order to achieve the objectives of the research, the researcher adopted the survey method and used the questionnaire and the scale to collect data and information, The researcher chose the "random sample multi stages", The sample size was (475) respondents from Baghdad city center (18 years and above) women and men.

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Publication Date
Wed Mar 01 2006
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Environmental Auditing A proposed Framework For Practice In Industrial Companies: Practical Study In Iraqi State Company For Cement
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This research aims to studying and analyzing the theoretical
framework of the environmental auditing in industrial environment to its a broad and danger environmental effects . It aims to contribute in setting and testing a proposed procedure framework for environmental auditing in that vital activity .The practical aspect focused on testing a proposed framework within practice it in a one Iraqi industrial company that has a huge effect on environmental activity, represented by Iraqi state company

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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