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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 May 27 2018
Journal Name
Journal Of Advanced Transportation
Accident Management System Based on Vehicular Network for an Intelligent Transportation System in Urban Environments
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As cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficie

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Publication Date
Wed Feb 22 2023
Journal Name
Iraqi Journal Of Science
Extraction Drainage Network for Lesser Zab River Basin from DEM using Model Builder in GIS
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ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

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Publication Date
Thu Feb 01 2024
Journal Name
Baghdad Science Journal
Mangrove Landscaping As An Adaptation Pattern To Reduce The Impact of Climate Change in Segara Anakan Lagoon, Cilacap Regency Indonesia
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Mangrove landscaping in the Segara Anakan Lagoon (SAL) is an adaptation pattern of mangrove ecosystems to live and grow in unstable areas. This research aimed to develop a mangrove landscape to mitigate the impacts of ocean waves, currents, and inundation due to climate change. The study was conducted in SAL and Cilacap Coast (CC) using the environmental properties and climate change data. The data obtained were analyzed using mapping and trendline analyses. The results showed that mangrove landscaping in Segara Anakan had four zones with Nypa frutican, Rhizophora styllosa, Aegiceras corniculatum, Rhizophora apiculata, Avicennia marina, Sonneratia alba identified as the best adaptation of mangrove species. Climate change give a high impa

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Publication Date
Tue Feb 26 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Return Variation on Investment as a Function for the Difference of Market Share: An Applied Research on a Sample of Private Banks listed in Iraq Stock Exchange (ISX)
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    The research specified with study the relation  between the market share for the sample research banks and the amount of the achieved revenues from the investment, where the  dominated belief  that there potentiality enhancing the revenue on investment with the increase of the banks shares in their markets after their success in achieving rates of successive growth in their sales of sales and to a suitable achieve  market coverage for their products and they have dissemination and suitable  promotion activity, the  market share represented the competition  for the banks, and the markets pay attention to the  market share as a strategic objective and to maintain them also increasi

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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Engineering And Sustainable Development
NEW TRENDS FOR ANGLE OF ARRIVAL ESTIMATION
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Publication Date
Fri Sep 05 2014
Journal Name
Engineering And Technology Journal
New Method to Increase the Ability of the Water for Dissolving Total Salts in Soil by Using the Magnetism
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Publication Date
Sun Oct 01 2017
Journal Name
Applied Energy
Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique
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Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
A Study of Apelin-36 and GST Levels with Their Relationship to Lipid and Other Biochemical Parameters in the Prediction of Heart Diseases in PCOS Women Patients
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This work studies the role of serum apelin-36 and Glutathione S-transferases (GST) activity in association with the hormonal, metabolic profiles and their link to the risk of cardiovascular disease (CVD) in healthy and patients' ladies with polycystic ovary syndrome (PCOS). A total of fifty-four (PCOS) patients and thirty-one healthy woman as a control have been studied. The PCOS patients were subdivided on the basis of body-mass-index (BMI), into 2-subgroups (the first group was obese-PCOS with BMI ≥ 30 and the second group was non-obese PCOS MBI<30). Fasting-insulin-levels and Lipid-profile, Homeostatic-model assessment-of-insulin-resistance (HOMA-IR), follicle-stimulating-hormone (FSH), luteinizing-hormone (LH), testosterone and

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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