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Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model could be used to predict future iron concentrations as the results from the verification of the ANN model for years 2012 and 2013 indicated good accuracy with a coefficient of determination R2 = 0.8965.

 

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Engineering
New Correlation for Predicting Undersaturated Oil Compressibility for Mishrif Reservoir in the Southern Iraqi Oil Fields
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Reservoir fluids properties are very important in reservoir engineering computations such as material balance calculations, well testing analyses, reserve estimates, and numerical reservoir simulations. Isothermal oil compressibility is required in fluid flow problems, extension of fluid properties from values at the bubble point pressure to higher pressures of interest and in material balance calculations (Ramey, Spivey, and McCain). Isothermal oil compressibility is a measure of the fractional change in volume as pressure is changed at constant temperature (McCain). The most accurate method for determining the Isothermal oil compressibility is a laboratory PVT analysis; however, the evaluation of exploratory wells often require an esti

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Publication Date
Sun Nov 01 2015
Journal Name
Al-qadisiyah Medical Journal
A survey of dermatophytes isolated from Iraqi patients in Baghdad City
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SJ Mohammed, AA Noaimi, KE Sharquie, JM Karhoot, MS Jebur, JR Abood, A Al-Hamadani, Al-Qadisiyah Medical Journal, 2015 - Cited by 20

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Publication Date
Mon Mar 15 2021
Journal Name
Iraqi National Journal Of Nursing Specialties
Feeding Behaviors of Children with Autism Spectrum Disorder in Baghdad City
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Objective(s): To assess the behavior that impedes the eating of children with autism spectrum disorders in Baghdad city, and find out the relationships between the behaviors that impede eating of autistic children and their demographic characteristics.
Methodology: The study started from the period of 16th September 2019 to the 16th of March 2020. A non-probability (purposive) sample of 80 children with autism spectrum disorders was selected. The questionnaire was designed and composed of two parts: the first part includes the autistic children demographic data, the second part includes scales of behavior that impede eating followed by parents towards autistic child. The reliability of the questionnaire was determined through a pilot

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Publication Date
Fri Nov 02 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Assessment of Bio-social Aspect with Cholelithiasis Patients in Baghdad City
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Objective: To find out the relationship between the bio-social aspect with cholelithiasis patients and
demographic characteristics in Baghdad city.
Methodology: A purposive (non-probability) sample of (100) patients, from (20-70) years old, who were
selected from patients who were admitted to hospital at preoperative stage, from Gastroenterology and
Hepatology Hospital, Baghdad Teaching Hospital, Al-Yarmook Teaching Hospital, Al-Karama Teaching
Hospital, Teaching Hospital. A descriptive study was carried out from 25th of June 2004 to the end of October
2004.
An assessment form was constructed for the purpose of the study. Test-retest reliability was employed through
computation of Pearson correlation coefficient.

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Publication Date
Sat Sep 30 2017
Journal Name
Al-khwarizmi Engineering Journal
Modeling of Monocrystalline PV Cell Considering Ambient Conditions in Baghdad City
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Abstract

 

The environmental conditions are important factors, because they affect both the efficiency of a photovoltaic module and the energy load. This research was carried out experimentally and modeling was done in MATLAB –Simulink by monitoring the variation in power output of the system with environmental conditions such as solar radiation, ambient temperature, wind speed, and humidity of Baghdad city. From the results, the ambient temperatures are inversely proportional to humidity and the output power performance of the system, while the wind speed is directly proportional with the output power performance of the system.  

 

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Publication Date
Fri Dec 14 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Knowledge of Paramedical Staff Regarding Drugs Addiction in Baghdad City/ Iraq
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Objectives: To determine the level of knowledge regarding drugs addiction among paramedical staff; and to
identify the association between the level of knowledge and their socio-demographic variable.
Methodology: A cross-sectional study was Conducted in Baghdad on Staff for the period from 1st June,
2016 to30th September, 2016 . The sample included 510 Health professionals . There are 9 sectors in center
of Baghdad , 5 of them in Al- Kurkh side of the city and 4 in Al-Russafa side. The sample was selected from
49 primary health care centers and 8 public hospitals through a multi-stage sample using a simple random
sampling technique. The information was collected through the design of a questionnaire whose stability and<

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Publication Date
Thu May 05 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Classification SINGLE-LEAD ECG by using conventional neural network algorithm
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Publication Date
Mon Jan 02 2012
Journal Name
Journal Of Engineering
3-D Object Recognition using Multi-Wavelet and Neural Network
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This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

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Publication Date
Wed May 10 2023
Journal Name
Journal Of Engineering
3-D OBJECT RECOGNITION USING MULTI-WAVELET AND NEURAL NETWORK
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This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

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