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A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comparison to the actual observational results. ANN simulation gives a clear insight into three crescent moon visibility regions: invisible (I), probably visible (P), and certainly visible (V). The proposed ANN is suitable for building lunar calendars, so it was used to build a four-year calendar on the horizon of Baghdad. The built calendar was compared with the official Hijri calendar in Iraq.

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Publication Date
Sun Mar 01 2009
Journal Name
Al-khwarizmi Engineering Journal
A Proposed Artificial Intelligence Algorithm for Assessing of Risk Priority for Medical Equipment in Iraqi Hospital
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This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p

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Publication Date
Tue Jun 01 2021
Journal Name
International Medical Journal
Visibility of mandibular canal on CBCT cross-sectional images in comparison with panoramic radiograph: Retrospective study
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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Roadway Deterioration Prediction Using Markov Chain Modeling (Wasit Governorate/ Iraq as a Case Study)
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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Multiphase Flow Behavior Prediction and Optimal Correlation Selection for Vertical Lift Performance in Faihaa Oil Field, Iraq
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In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Robotics And Control (jrc)
Automated Stand-alone Surgical Safety Evaluation for Laparoscopic Cholecystectomy (LC) using Convolutional Neural Network and Constrained Local Models (CNN-CLM)
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In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

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Publication Date
Tue Jun 20 2023
Journal Name
Bulletin Of The Iraq Natural History Museum
REVISION OF THE GENUS XYLOCOPA LATREILLE, 1802 (HYMENOPTERA, APIDAE) WITH A NEW RECORD OF SPECIES IN IRAQ
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In this study, the genus Xylocopa Latreille, 1802 (Hymenoptera: Apidae) was revised. There were 4 species registered in our investigations: X. hottentotta Smith, 1854; X. olivieri Lepeletier, 1841; X. pubescens Spinola, 1838 and X. valga Gerstäcker, 1872, the first species was described as being found for the first time for the insect fauna of Iraq, which were obtained from Solanum melogena L. flowers. Key to the species was constructed and supported by figures of the main diagnostic characters and some morphological features, illustrated and compared with other species, which are recorded in the current survey.

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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
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         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

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Publication Date
Sat Jul 01 2000
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
A NEW SPECIES OF LIODONTOMERUS GAH. FROM IRAQ (HYMENOPTERA, TORYMIDAE)
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A study of the Torymid collection of Iraq. resulted in undescribed species of the genus
Liodontonierus Gah. L. longicorpus sp. n. with 2 figures.

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Publication Date
Sun Jul 01 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
KEY TO THE SPECIES OF THE ORTHETRUM NEWMAN, 1833 (ODONATA, LIBELLULIDAE) WITH A NEW RECORD SPECIES IN IRAQ
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This paper provides an identification key to the species of Orthetrum Newman, 1833 (Odonata, Libellulidae), including six species that were collected from different localities in Iraq.

 

    The species of O. anceps (Schneider, 1845) is registered as a new record in Iraq; the most important characters which are used in diagnostic key are included

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Clinical And Experimental Dentistry
Bond strength of a new Kevlar fiber-reinforced composite post with semi-interpenetrating polymer network (IPN) matrix
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