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Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eyes' observation of the different colors and features of images. We propose a multi-layer hybrid system for deep learning using the unsupervised CAE architecture and using the color clustering of the K-mean algorithm to compress images and determine their size and color intensity. The system is implemented using Kodak and Challenge on Learned Image Compression (CLIC) dataset for deep learning. Experimental results show that our proposed method is superior to the traditional compression methods of the autoencoder, and the proposed work has better performance in terms of performance speed and quality measures Peak Signal To Noise Ratio (PSNR) and Structural Similarity Index (SSIM) where the results achieved better performance and high efficiency With high compression bit rates and low Mean Squared Error (MSE) rate the results recorded the highest compression ratios that ranged between (0.7117 to 0.8707) for the Kodak dataset and (0.7191 to 0.9930) for CLIC dataset. The system achieved high accuracy and quality in comparison to the error coefficient, which was recorded (0.0126 to reach 0.0003) below, and this system is onsidered the most quality and accurate compared to the methods of deep learning compared to the deep learning methods of the autoencoder

Publication Date
Mon Sep 14 2015
Journal Name
Spe North Africa Technical Conference And Exhibition
Feasibility of Gas Lift to Increase Oil Production in an Iraqi Giant Oil Field
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Abstract<p>Gas lift is one of the artificial lift techniques which it is frequently implemented to raise oil production. Conventionally, the oil wells produce depending on the energy of reservoir pressure and solution gas which declines due to continuous production. Therefore, many oil wells after a certain production time become unable to lift oil to the surface. Thus, the continuity of production requires implementation of gas lift which works to decrease the average fluid density in the tubing by injection gas through the annulus into the tubing. This paper aims to get maximum oil production of an Iraqi giant oil field at optimum injected gas rate. The field is located in south of Iraq and in</p> ... Show More
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Publication Date
Sat Feb 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Analysis of the Causal Relationship Long-and Short-term Between the Price of Crude Oil, the Global Price of Gold and the US. Dollar Exchange Rate
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This research aims to test the causal relationship long-and short-run between the price of gold the global crude oil price and the exchange rate of the dollar and how you can take advantage of the nature of this relationship, particularly in the Arab oil states that achieve huge surpluses, including Iraq and how to keep on the purchasing power of these surpluses or reduce the levels of risk.

The problem is that the Arab oil countries, adversely affected, as a result of that relationship, due to the fact that its role confined to the sale of crude oil only. They do not have control in the dollar, then they are not able to take advantage of its impact on the price of gold the fact that gold is effective pr

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Publication Date
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type
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Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
Efficient Algorithm for Solving Fuzzy Singularly Perturbed Volterra Integro-Differential Equation
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     In this paper, we design a fuzzy neural network to solve fuzzy singularly perturbed Volterra integro-differential equation by using a High Performance Training Algorithm such as the Levenberge-Marqaurdt (TrianLM) and the sigmoid function of the hidden units which is the hyperbolic tangent activation function. A fuzzy trial solution to fuzzy singularly perturbed Volterra integro-differential equation is written as a sum of two components. The first component meets the fuzzy requirements, however, it does not have any fuzzy adjustable parameters. The second component is a feed-forward fuzzy neural network with fuzzy adjustable parameters. The proposed method is compared with the analytical solutions. We find that the proposed meth

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Publication Date
Sun Jan 01 2012
Journal Name
International Journal Of Contemporary Mathematical Sciences
Pre-Topology Generated by the Short Path Problems
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Let G be a graph, each edge e of which is given a weight w(e). The shortest path problem is a path of minimum weight connecting two specified vertices a and b, and from it we have a pre-topology. Furthermore, we study the restriction and separators in pre-topology generated by the shortest path problems. Finally, we study the rate of liaison in pre-topology between two subgraphs. It is formally shown that the new distance measure is a metric

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Publication Date
Thu Apr 28 2022
Journal Name
Iraqi Journal Of Science
Determination of the shape for (54Xe and 82pb) nuclei from deformation parameters ( , )
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The shape for even-even (54Xe 118≤ A ≤ 140 and 82Pb 204 ≤ A ≤ 210 ) nuclei have been studied and investigated through the deformation parameters and δ , these deformation parameters were calculated by two different methods. The first one is nucleus quadrupole deformation parameter β2 from reduced transition probability B(E2)↑ for 0+→2+1 transitions and the second is nucleus quadrupole deformation parameters δ from quadrupole moment Qo.The relationship between two deformation parameters ( , ) and neutrons magic number (N=82 & 126) was studied through plotting the deformation parameters ( , ) as a function of neutrons number , from this relationship we can see very cleary that the deformation of nucleus decreased when th

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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Medical waste management in Al-Kut City
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This research investigates solid waste management in Al-Kut City. It included the collection of medical and general solid waste generated in five hospitals different in their specialization and capacity through one week, starting from 03/02/2012. Samples were  collected and analyzed periodically to find their generation rate, composition, and physical properties. Analysis results indicated that generation rate ranged between (1102 – 212) kg / bed / day, moisture content and density were (19.0 % - 197 kg/ m3) respectively for medical waste and (41%-255 kg/ m3) respectively for general waste. Theoretically, medical solid waste generated in Al-Kut City (like any other city), affected by capacity, number of patients in a day, and hosp

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Publication Date
Sat Jul 31 2021
Journal Name
Iraqi Journal Of Science
Enhancement of Spectrum Sensing Performance via Cooperative Cognitive Radio ‎Networks at Low SNR
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     The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achi

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Publication Date
Thu May 05 2022
Journal Name
Al-kindy College Medical Journal
Safety profile of immediate post-partum intrauterine device insertion during caesarean delivery – a clinical trial with three years of follow up: Utility of Immediate Post Placental Insertion of Intrauterine Device During Cesarean Delivery
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Background: Many countries recommend the use of long-acting reversible contraceptive intrauterine device immediately after cesarean delivery. The cesarean delivery rate in Iraqi public hospitals is 32.2% and may reach 85.8% in private hospitals. Immediate post-partum intrauterine device insertion at cesarean is rarely done in Iraq.

Objectives: To assess the safety and practicality of immediate post-partum intrauterine device insertion during cesarean delivery for family planning and pregnancy spacing in Iraqi women.

Subjects and Methods: A single arm clinical trial included 150 eligible women who attended Al-‎Elwiyah Maternity Teaching Hospital or Al Hayat Rahibat Hospita

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Publication Date
Sun Oct 22 2023
Journal Name
Iraqi Journal Of Science
Study of Matter Density Distributions, Elastic Electron Scattering form Factors and Reaction Cross Sections of 8He And 17B Exotic Nuclei
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The ground state densities of unstable neutron-rich 8He and 17B exotic nuclei are studied via the framework of the two-frequency shell model (TFSM) and the binary cluster model (BCM). In TFSM, the single particle harmonic oscillator wave functions are used with two different oscillator size parameters βc and βv where the former is for the core (inner) orbits and the latter is for the valence (halo) orbits. In BCM, the internal densities of the clusters are described by single particle Gaussian wave functions. Shell model calculations for the two valence neutrons in 8He and 17B are performed via the computer code OXBASH. The long tail performance is clearly noticed in the calculated neutron and matter density distributions of these nucl

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