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Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, comes in second place with a gross ratio of 91%. Furthermore, Bayesian ridge (BR), linear regressor (LR), and stochastic gradient descent (SGD), with mean square error and with accuracy ratios of 84.365%, 84.363%, and 79%. As a result, the performance precision of these regression models yields. The interaction framework was designed to be a straightforward tool for working with this paradigm. This model is a valuable tool for establishing strategies to counter the swiftness of climate change in the area under study.</span>

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Publication Date
Fri Mar 01 2013
Journal Name
Diyala Journal Of Engineering Sciences
Kinetic of Atropine Pertraction from the Seeds of Datura Metel Linn Plant Using Liquidliquid Membrane Technique
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The kinetic of atropine pertraction from seeds of Datura Metel Linn plant was studied. Diisopropyl ether, n-hexane and n-heptane were used as membranes for atropine recovery. The effect of speed of agitation and time in the range of 200-300 rpm and 0-3.5h, respectively were studied using the proposed membranes. The pertraction experiments were carried outs in a batch laboratory unit. The liquid-liquid pertraction was found to be very suitable for atropine recovery from its liquid extracts of Datura Metel seeds. A high purity (94-96%) can be obtained in the receiver phase. The pertraction process was found to be very selective for atropine recovery with diisopropyl ether membrane. As the speed of agitation increases the efficiency of pertrac

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Publication Date
Tue Mar 30 2021
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Treating Wet Oil in Amara Oil Field Using Nanomaterial (SiO2) With Different Types of De emulsifiers
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One of the most important problems in the oil production process and when its continuous flow, is emulsified oil (w/o emulsion), which in turn causes many problems, from the production line to the extended pipelines that are then transported to the oil refining process. It was observed that the nanomaterial (SiO2) supported the separation process by adding it to the emulsion sample and showed a high separation rate with the demulsifiers (RB6000) and (sebamax) where the percentage of separation was greater than (90 and 80 )%  respectively, and less than that when dealing with (Sodium dodecyl sulfate and Diethylene glycol), the percentage of separation was (60% and 50%) respectively.

   The high proportion

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
The effect of using different fibres on the impact-resistance of slurry infiltrated fibrous concrete (SIFCON)
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Abstract<p>Slurry infiltrated fibrous concrete (SIFCON) is a modern type of fibre reinforced concrete (FRC). It has unique properties; SIFCON is superior in compressive strength, flexural strength, tensile strength, impact resistance, energy absorption and ductility. Because of this superiority in these characteristics, SIFCON was qualified for applications of special structures, which require resisting sudden dynamic loads such as explosions and earthquakes. The main aim of this investigation is to determine the effect of fibre type on the apparent density of SIFCON and on performance under impact load. In this investigation, hook-end steel fibre and polyolefin fibre were used. Purely once and </p> ... Show More
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Publication Date
Tue Dec 01 2009
Journal Name
2009 7th International Conference On Information, Communications And Signal Processing (icics)
The effect of traffic load on using XCAST based routing protocol in wireless ad hoc networks
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Publication Date
Sun Jun 23 2019
Journal Name
American Rock Mechanics Association
Using an Analytical Model to Predict Collapse Volume During Drilling: A Case Study from Southern Iraq
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Zubair Formation is one of the richest petroleum systems in Southern Iraq. This formation is composed mainly of sandstones interbedded with shale sequences, with minor streaks of limestone and siltstone. Borehole collapse is one of the most critical challenges that continuously appear in drilling and production operations. Problems associated with borehole collapse, such as tight hole while tripping, stuck pipe and logging tools, hole enlargement, poor log quality, and poor primary cement jobs, are the cause of the majority of the nonproductive time (NPT) in the Zubair reservoir developments. Several studies released models predicting the onset of borehole collapse and the amount of enlargement of the wellbore cross-section. However, assump

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Publication Date
Tue Jan 01 2019
Journal Name
Plant Archives
Using the foreign trade elasticities in policy of demand of imports of agricultural products in Iraq
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Publication Date
Tue Nov 03 2020
Journal Name
Modern Sport
The effect of using multimedia in learning the skill of passing from the bottom of volleyball
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The aim of the research is to:. Preparation and implementation of special educational units using multimedia to learn the skill of scrolling from below. 2 to recognize the impact of the use of multimedia in learning the skill of scrolling from below. 3 to identify the differences between the tests after the two groups research in learning the skill of passing from the bottom volleyball. The research represented the students of the first stage and the sample of the research was drawn randomly and the number of (50) students were divided into two experimental and control groups and each group (25) students were used standardized tests and conducting pre-tests and the implementation of the main exp

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Publication Date
Sat Jan 20 2024
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset
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Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

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Publication Date
Mon May 28 2018
Journal Name
Iraqi Journal Of Science
Hide Secret Messages in Raster Images for Transmission to Satellites using a 2-D Wavelet Packet
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     The hiding of information has become of great importance in recent times. With dissemination through the internet, and communication through satellites, information needs to be secure. Therefore, a new algorithm is proposed that enables secret messages to be embedded inside satellite images, wherein images of any size or format can be hidden, using a system’s image compression techniques. This operation is executed in three main steps: first phase – the original image is converted into a raster image; second phase– steganography, in which a binary secret message is hidden inside a raster image, using a 4×4 array as the secret key; and third phase– compre

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Publication Date
Sun Jan 01 2023
Journal Name
Ieee Access
Fuzzy-Based Ensemble Feature Selection for Automated Estimation of Speaker Height and Age Using Vocal Characteristics
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