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Classification of al-hammar marshes satellite images in Iraq using artificial neural network based on coding representation
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Publication Date
Sun Mar 01 2020
Journal Name
Sustainable Chemistry And Pharmacy
A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Performance of Case-Based Reasoning Retrieval Using Classification Based on Associations versus Jcolibri and FreeCBR: A Further Validation Study
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Publication Date
Fri May 01 2026
Journal Name
Retos
Using artificial neural networks to assign soccer players by physical and motor abilities
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Introduction: The introduction of analytics tools in sports indicates that artificial neural networks can be one of the intelligent approaches to process complex data and identify patterns that help players move according to their most suitable positions. Objective: The purpose of this research is to investigate the possibility of using artificial neural networks to determine the physical and motor abilities of football players and determine their suitable playing positions based on exact quantitative indicators. Method: The study sample consists of 45 youth players aged (15–16) years from the Espanyol Football Academy in Baghdad. The results are analyzed using a multilayer perceptron (MLP) artificial neural network model to ident

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Publication Date
Sun Jul 01 2018
Journal Name
Journal Of Pharmaceutical Sciences And Research
Study of Environmental and Biological characteristics of AL-Chibaish marshes and their effect to the biodiversity of southern Iraq
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The Mesopotamian marshlands faced a massive destruction from many years and this lead to effect to ecosystem. In this study a survey was made on the physical chemical and heavy metals characteristics and microbiological analysis of AL Chibaish marsh during the two months. Water analyses revealed unacceptable values for almost all physiochemical and biological properties, according to WHO standard limits for drinking water. Almost all major ions and heavy metal concentrations in water showed a distinct decreasing trend at the marsh outlet station compared to other stations. In general, major and minor ions, as well as heavy metals exhibit higher concentrations in location 1 than in location 3. The concentrations of heavy metals in water show

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Scopus
Publication Date
Fri Feb 04 2022
Journal Name
Neuroquantology
Detecting Damaged Buildings on Post-Hurricane Satellite Imagery based on Transfer Learning
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In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

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Publication Date
Thu Sep 01 2022
Journal Name
Iop Conference Series: Earth And Environmental Science
Non-Diatomic Diversity and Community Structure in Hoor-Al- Azime Marshes, Iran
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Abstract<p>This study was conducted to delineate diversity and species composition of non-diatoms planktonic algae in Hoor- Al- Azime marshes, Iran. The samples were collected from four sites at monthly basis from April 2011 to March 2012. A total 88 taxa were identified, out of which (40 taxa, 45.45%) belonging to Cyanophyta followed by Chlorophyta (29 taxa, 32.96%), Euglenophyta (18 taxa, 20.45%) and (1 taxa, 1.14%) of Dinophyta recorded. Comparing species richness (65 taxa, 34.76%) at Shat- Ali (St4) was the highest and the lowest (34 taxa, 18.18%) was observed at Rafi (St2). Species occurrence was associated with temperature where in summer (66 taxa) and (25 taxa) encountered winter. The phy</p> ... Show More
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Publication Date
Fri Jan 01 2021
Journal Name
Communications In Computer And Information Science
Audio Compression Using Transform Coding with LZW and Double Shift Coding
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Home New Trends in Information and Communications Technology Applications Conference paper Audio Compression Using Transform Coding with LZW and Double Shift Coding Zainab J. Ahmed & Loay E. George Conference paper First Online: 11 January 2022 126 Accesses Part of the Communications in Computer and Information Science book series (CCIS,volume 1511) Abstract The need for audio compression is still a vital issue, because of its significance in reducing the data size of one of the most common digital media that is exchanged between distant parties. In this paper, the efficiencies of two audio compression modules were investigated; the first module is based on discrete cosine transform and the second module is based on discrete wavelet tr

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Pollution threatens water quality in the Central Marshes of Southern Iraq
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Water pollution is an issue that can be exacerbated by drought as increased concentrations of unwanted substances are a consequence of lower water levels. Polluted water that flows into natural marshlands leads to the deposition of pollutants in the interior of the marsh. Here we present evidence that the interior of the Central Marsh (CM) in southern Iraq suffers from higher levels of pollution than areas closer to the source of water entering the marsh (the Euphrates River). A 1.7m embankment that halts the flow of the Euphrates is only infrequently breached and so the CM is effectively the terminal destination of the waters (and their associated pollutants and agricultural waste) flowing from the West of Iraq.

A range of water

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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
Thu Jul 17 2025
Journal Name
International Journal Of Environmental Sciences
Inverse Kinematic Analysis For A 5 DOF Robotic Arm Using Deep Neural Network
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In this study, the kinematic analysis including the forward and inverse kinematic developed for a 5 degree of freedom robotic arm. The forward kinematic is elaborated using Denavit-Hartenberg (DH) convention. Inverse Kinematic is established using Deep Neural Network (DNN) model with five hidden layer each contain 50 neurons fully connected using ReLu activation. A data of inputs and outputs are created and trained. The inputs are the end-effector position and orientation. The outputs are the joint angles of the manipulator. The data is generated by the forward kinematics, where a set of joint angles that limited by their corresponding ranges are inserted to the forward kinematic equations to result the end-effector positions and or

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