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Fault Location of Doukan-Erbil 132kv Double Transmission Lines Using Artificial Neural Network ANN
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's performance was evaluated, and tests were run. Line-to-ground faults were examined. The study demonstrates how effective, rapid, and precise this method is at locating faults. The neural network's performance was examined, and tests were run on it. The overall performance of the mean square error in the trained network execution was 0.11792 at 35 epochs. The correlation coefficient at the entire target was 0.99987 percent of an error on the Doukan-Erbil double transmission lines.

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Publication Date
Mon Jun 30 2008
Journal Name
Iraqi Journal Of Science
On the Greedy Ridge Function Neural Networks for Approximation Multidimensional Functions
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The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).

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Publication Date
Wed Sep 07 2022
Journal Name
2022 Iraqi International Conference On Communication And Information Technologies (iiccit)
Vehicular Ad-hoc Network (VANET) – A Review
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This paper explores VANET topics: architecture, characteristics, security, routing protocols, applications, simulators, and 5G integration. We update, edit, and summarize some of the published data as we analyze each notion. For ease of comprehension and clarity, we give part of the data as tables and figures. This survey also raises issues for potential future research topics, such as how to integrate VANET with a 5G cellular network and how to use trust mechanisms to enhance security, scalability, effectiveness, and other VANET features and services. In short, this review may aid academics and developers in choosing the key VANET characteristics for their objectives in a single document.

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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Classification of Rural Road Network in Al-Najaf Governorate
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This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Generative Adversarial Network for Imitation Learning from Single Demonstration
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Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

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Publication Date
Mon Jan 27 2020
Journal Name
Iraqi Journal Of Science
Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene
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Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was tr

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Publication Date
Sun Sep 03 2017
Journal Name
Baghdad Science Journal
Polymorphism study of MTHFR 677C?T and its correlation with oxidative stress and their influence on female infertility in Erbil – Iraq
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This research includes a study of Methylenetetrahydrofolate reductase gene’s allele 677C?T and its correlation with oxidative stress and their impact on female infertility. Fifty infertile women with the range age (23-42) years and twenty five fertile women with the range age (22-39) years as control group living in Erbil city were selected. The serum level of Malondialdehyde (MDA), superoxide dismutase (SOD), prolactin hormone (PRL), Luteinizing hormone (LH), Thyroid stimulating hormone (TSH), Triiodothyronine hormone (T3), and Thyroxine hormone (T4) were measured, also a body mass index (BMI) was calculated. A restriction enzyme (Hinf1) was used to improve the mutation in DNA bands of infertile women. The results showed significant inc

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Publication Date
Sun Sep 07 2014
Journal Name
Baghdad Science Journal
An Ecological Observation on Inland water Ecosystem in Erbil –Iraq Kurdistan with particular reference to blue green algae Glaucospira
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Regular sampling for six months from January to July 2012 were taken in small, shallow, perennial, standing ponds near the Greater Zab River, Gwer district, Erbil. A variety of physicochemical parameters were determined. Air and water temperature were falling between 15.2 - 34.7 ? C and 15.5 and 26.5 ?C. The waters are neutral (pH 7.38-8.27), hard, alkaline, salty, high in TDS and EC (892-966?S/cm, and rich in nutrients (NO3: 2.1-4.1mg/l, PO4: 0.33-0.62 mg / l , SO4: 24.7-80.2 mg / l ). The attention fixed on a filamentous blue- green algae Glaucospira Lagerheim, 1982) which is new to Iraqi flora. It is a filament (trichome), solitary, pale or yellowish blue – green, without sheath, Screw like coiled, motile, some of them are activ

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Publication Date
Wed Mar 24 2021
Journal Name
Journal Of Research In Medical And Dental Science
Efficacy of Oregano Essential Oil Mouthwash in Reducing Oral Halitosis: A Randomized, Double-Blind Clinical Trial
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Efficacy of Oregano Essential Oil Mouthwash in Reducing Oral Halitosis: A Randomized, Double-Blind Clinical Trial, Mohamed Saeed M Ali, Ayser Najah Mohammed*

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Publication Date
Sun Jul 01 2018
Journal Name
International Journal Of Engineering Research And Management
The first and Second Order Polynomial Models with Double Scalar Quantization for Image Compression
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Publication Date
Mon Jul 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Solving Some Fractional Partial Differential Equations by Invariant Subspace and Double Sumudu Transform Methods
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      In this paper, several types of space-time fractional partial differential equations has been solved by using most of special double linear integral transform ”double  Sumudu ”. Also, we are going to argue the truth of these solutions by another analytically method “invariant subspace method”. All results are illustrative numerically and graphically.

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