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Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning models for a variety of tasks under the control of a unified architecture for each proposed model.

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Publication Date
Wed Aug 13 2025
Journal Name
Modern Sport
Administrative Skills and Their Role in Distinguishing the Institutional Performance of Directors of Sports Activity in Iraqi Universities
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The research aims to identify the administrative skills and their role in institutional performance, and used the descriptive approach in a survey style and correlational relations to suit the nature of the problem to be studied.The research community was identified with the directors of sports activity in the Iraqi universities in a deliberate manner, and their number is (134) directors, where two scales (administrative skills and institutional performance) were used and the two scales were distributed to the above-mentioned sample in order to obtain answers that meet the required and representative sample of the community on the day 3/12/2021. The answers to all the statements were completed and after processing the results statistically

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Publication Date
Thu Jul 01 2021
Journal Name
Energy
Experimental investigations of the performance of a flat-plate solar collector using carbon and metal oxides based nanofluids
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Publication Date
Sun May 01 2016
Journal Name
Journal Of Engineering
Prediction of Ryznar Index for the treated water from WTPs on Al-Karakh side of Baghdad City using Artificial Neural Network (ANN) technique
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In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Effect of Allowable Vertical Load and Length/Diameter Ratio (L/D) on Behavior of Pile Group Subjected to Torsion
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Some structures such as tall buildings, offshore platforms, and bridge bents are subjected to lateral loads of considerable magnitude due to wind and wave actions, ship impacts, or high-speed vehicles. Significant torsional forces can be transferred to the foundation piles by virtue of eccentric lateral loading. The testing program of this study includes one group consists of 3 piles, four percentages of allowable vertical load were used (0%, 25%, 50%, and 100%) with two L/D ratios 20 and 30, vertical allowable load 110 N for L/D = 20 and 156 N for L/D = 30. The results obtained indicate that the torsional capacity for pile group increases with increasing the percentage of allowable vertical load, when the percentage of allowable vertica

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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Study of Television Advertising and its Impact on Consumer Behavior in a Sample of Children Aged 9-12 Years
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The aim of this study was to study TV advertising and consumer behavior in children and to meet the needs of children. The study included 100 children from Baghdad who were randomly selected from different regions ranging in age from 9-12 years of both males and females. The current research was based on the interview and the completion of special forms prepared for this purpose. This age group was selected for the rare studies conducted on television advertising and limited to different sectors. Data on age and sex were documented, as the results of this study proved The afternoon period was the preferred period for watching the TV show in males, where it was 22%, while the morning period was the female favorite, and it was 23%. The ind

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Publication Date
Tue Oct 29 2024
Journal Name
Journal Of Advanced Pharmacy Education And Research
Evaluating the effects of different doses of fimasartan on methotrexate-induced renal inflammation in rats
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The nephrotoxicity induced by methotrexate is a severe condition that greatly affects its therapeutic potential and has a significant inflammatory component. Fimasartan is an angiotensin receptor blocker that offers organ-protective effects and may be useful in mitigating renal injury. The present study explored the anti-inflammatory potential of two doses of fimasartan against methotrexate-mediated nephrotoxicity. Albino rats were intraperitoneally administered a single methotrexate (20 mg/kg). Intraperitoneal treatment with fimasartan (5 or 10 mg/kg/day) was initiated on day two after methotrexate injection and continued for seven consecutive days. Methotrexate significantly increased serum urea, creatinine, and NGAL concentrations. It al

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Publication Date
Tue Nov 01 2016
Journal Name
Journal Of Engineering
Modeling of Comparative Performance of Asphalt Concrete under Hammer, Gyratory, and Roller Compaction
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The main objective of this study is to develop predictive models using SPSS software (version 18) for Marshall Test results of asphalt mixtures compacted by Hammer, Gyratory, and Roller compaction. Bulk density of (2.351) gm/cc, at OAC of (4.7) % was obtained as a benchmark after using Marshall Compactor as laboratory compactive effort with 75-blows. Same density was achieved by Roller and Gyratory Compactors using its mix designed methods.

A total of (75) specimens, for Marshall, Gyratory, and Roller Compactors have been prepared, based on OAC of (4.7) % with an additional asphalt contents of more and less than (0.5) % from the optimum value. All specimens have been subjected to Marshall Test. Mathematical model

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Publication Date
Mon Dec 18 2017
Journal Name
Al-khwarizmi Engineering Journal
Comparative Study of Performance and Emission Characteristics between Spark Ignition Engine and Homogeneous Charge Compression Ignition Engine (HCCI)
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Many researchers consider Homogeneous Charge Compression Ignition (HCCI) engine mode as a promising alternative to combustion in Spark Ignition and Compression Ignition Engines. The HCCI engine runs on lean mixtures of fuel and air, and the combustion is produced from the fuel autoignition instead of ignited by a spark. This combustion mode was investigated in this paper. A variable compression ratio, spark ignition engine type TD110 was used in the experiments. The tested fuel was Iraqi conventional gasoline (ON=82).

The results showed that HCCI engine can run in very lean equivalence ratios. The brake specific fuel consumption was reduced about 28% compared with a spark ignition engine. The experimental tests showed that the em

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Publication Date
Tue Jun 11 2019
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Evaluating Health-Related Quality of Life (HRQoL) in Iraqi Adult and Pediatric Patients with Beta-Thalassemia Major Using Two Different Iron Chelation Therapies
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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Improve the Performance of PID Controller by Two Algorithms for Controlling the DC Servo Motor
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The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

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