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Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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Publication Date
Sun Sep 07 2014
Journal Name
Baghdad Science Journal
Qualitative and Quantitative study of Epipelic algae in Tigris River within Baghdad City, Iraq
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The present study conducted to study epipelic algae in the Tigris River within Baghdad city for one year from September 2011 to August 2012 due to the importance role of benthic algae in lotic ecosystems. Five sites have been chosen along the river. A total of 154 species of epipelic algae was recorded belongs to 45 genera, where Bacillariophyceae (Diatoms) was the dominant groups followed by Cyanophyceae and Chlorophyceae. The numbers of common types in three sites were 47 species. Bacillariophyceae accounted 88.31% of the total number of epipelic algae, followed by Cyanophyceae 7.14 % and Chlorophyceae 4.55%. A 85 species (29 genera) recorded in site 1, 103 species (34 genera) in site2, 112 species (35 genera) in site3, 96 species

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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
The hydrological regime of the Tigris River in the city of Baghdad
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Water drainage pattern in the rivers and changed the nature of the renewed feeding areas
in the basin in terms of topographic and geological conditions and climate in addition to the
human role in organizing the process flow within these basins. This study addressed the
development of the Tigris River Hydrological in the city of Baghdad and found that the
annual rate of water drainage in the Tigris River was driven down very significantly,
especially in the past twenty years, and since 1996 up to 2014 record flow rates of less than
the overall rate of discharge of water, a (950 m3 / s ), in addition to the quarterly decrease the
discharge rates, especially since the beginning of the year 2000 and took converge all fo

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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
Tue Jun 03 2025
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Comparison of some artificial neural networks for graduate students
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Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Pristina species (oligochaeta: naididae) in tigris river within baghdad city / Iraq
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A total of 72 individuals of genus Pristina were sorted from aquatic plant, Ceratophyllum demersum L., and filamentous algae collected from three sites on Tigris River at Baghdad including: Al-Sarafiya area (S1), Al- Jadiriyah area (S2), and Al- Za´afaraniya area (S3). Four species were identified including P. longiseta, P. aequiseta, P. proboscidea and P. foreli, with percentags of 51.7 , 36.4, 1.1, and 10.5 % respectively. The first two species found in all sites , while , P. proboscidea found only in S1 and P. foreli only in S2.

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Publication Date
Tue Oct 30 2018
Journal Name
Iraqi Journal Of Physics
Determination of radioactive concentrations in Tigris river soil samples in Baghdad province
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In this study, the amounts of activity concentrations of naturally occurring in 10 soil samples of the Tigris river and surrounding areas collected from deferent city of Baghdad have been investigated. Tigris river is an important water source for irrigation and drinking in Iraq. This study was done during 2018 in Protection Center of the Iraqi Ministry of Health and Environment using a high purity germanium detector. The resolution of (HPGe) at 2keV and 30% efficiency. The results of soil sample obtained showed that the effective activity concentration of 40K are ranged from 181.4 Bq/kg in sample S6 to 286.4 Bq/kg in S7. For Raeq values are ranged from 6 Bq/m3 in sample S5 to 17 Bq/m3 in sample S3. The obtained data revealed that the me

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Publication Date
Wed May 31 2023
Journal Name
Iraqi Geological Journal
Studying the Effect of Permeability Prediction on Reservoir History Matching by Using Artificial Intelligence and Flow Zone Indicator Methods
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The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in perme

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Publication Date
Mon Jul 15 2024
Journal Name
2024 46th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network
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