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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 Al-Dora WTP, ANN 3 model could be used as R was 92.8%.

Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Prediction of Daily Maximum Air Temperature for Transitional Seasons by Statistical Methods in Baghdad
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     Predicting the maximum temperature is of great importance because it is related to various aspects of life, starting from people’s lives and their comfort, passing through the medical, industrial, agricultural and commercial fields, as well as concerning global warming and what can result from it. Thus, the historical observations of maximum and minimum air temperature, wind speed and relative humidity were analyzed in this work. In Baghdad, the climatic variables were recorded on clear sky days dawn at 0300 GMT for the period between (2005-2020). Using weather station's variables multiple linear regression equation, their correlation coefficients were calculated to predict the daily maximum air temperature for any day during

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Publication Date
Sun Oct 30 2022
Journal Name
Iraqi Journal Of Science
Using Sequencing Technique for Diagnostic Different Species of Genus Rhizobium Which Isolated from Legume Plants
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      Samples of the root nodules were collected to isolate different species of the genus Rhizobium from several leguminous plants; Trigonella  foenum-graecum, Medicago sativa, Lens culinaris, Vigna mungo, Vicia faba,  Phaseolus vulgaris, and Cicer arietinum, and based on their morphological, cultural, and biochemical characteristics, in addition to the identification of each isolate at the species level by amplified polymerase chain reaction (PCR) and using the sequencing of the nitrogenous bases of the 16S rRNA gene, it was identified as Sinrhizobium meliloti, Sinrhizobium meliloti, Bradyrhizobium elkanii, Rhizobium leguminosarium biovar viciae, Rhizobium leguminosarium biovar phaseoli and Mesorh

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Publication Date
Mon Dec 16 2024
Journal Name
International Journal Of Computing And Digital Systems
Digital Intelligence for University Students Using Artificial Intelligence Techniques
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The research problem arose from the researchers’ sense of the importance of Digital Intelligence (DI), as it is a basic requirement to help students engage in the digital world and be disciplined in using technology and digital techniques, as students’ ideas are sufficiently susceptible to influence at this stage in light of modern technology. The research aims to determine the level of DI among university students using Artificial Intelligence (AI) techniques. To verify this, the researchers built a measure of DI. The measure in its final form consisted of (24) items distributed among (8) main skills, and the validity and reliability of the tool were confirmed. It was applied to a sample of 139 male and female students who were chosen

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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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Publication Date
Wed Sep 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Application
Suggested methods for prediction using semiparametric regression function
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Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

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Publication Date
Sun Oct 22 2023
Journal Name
Iraqi Journal Of Science
Study the Impact of the waste which discharged from Al-Karama and Sharq- Dijla water treatment plants in water pollution of Tigris River water
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This study was conducted to evaluate the effect of discharge of water purification plants ,on the purity of Tigris River water in Baghdad city. The two studied plants were involved in the study : Sharq –Dijla and Al-Karama water purification plants. The study was attempted to focus on probable pollutants. Chemical, physical tests were accomplished on water samples collected from four sites with fact of three replicates for each sample of each site of the river and the plant : before, after, inside the plants and at the pipe. This study started from October 2012 to September 2013. In case of heavy metals Results showed that the highest level of aluminum was 1.08 ppm during (December-January) at Sharq-Dijla plants, while the lowest level

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Publication Date
Wed Jan 10 2018
Journal Name
Al–bahith Al–a'alami
Using of digital media and its effect on constructing the value system of Iraqi society (Field study on a sample of the audience for the city of Baghdad model)
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With the great development in the field of the Internet, the talk about the new media and its implications began, And its interactive services have made the future of media material sometimes participating in it and manufacturing it at other times,
the public is seeking information and choosing the appropriate ones, as well as exchanging messages with the sender after what the role of the receiver is just receiving information only.
This study aims to demonstrate the effects of using digital media in various forms and types to construct the value system of Iraqi society through the identification of the following aims:
Identify the most popular digital media for the Iraqi public in their daily lives on the Internet.
Identify

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

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Publication Date
Mon May 01 2023
Journal Name
Ain Shams Engineering Journal
Neural network modeling of rutting performance for sustainable asphalt mixtures modified by industrial waste alumina
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Publication Date
Wed Mar 31 2021
Journal Name
Electronics
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model
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Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

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