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Mathematical Models for Predicting of Organic and Inorganic Pollutants in Diyala River Using AnalysisNeural Network
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Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourteen parameters pH, DO, BOD, PO4, NO3,Ca, Mg, TH, K, Na, SO4,Cl, EC, Alk. The results indicated that the best correlation coefficient is 86.5% for BOD, and the most important parameter is Chloride Cl, and the best correlation coefficient is 95.4% for TDS and the most important parameters are total hardness TH and electrical conductivity EC, according to direct relation between these parameters and TDS.

Publication Date
Wed Jul 05 2017
Journal Name
Neural Computing And Applications
Hybrid soft computing approach for determining water quality indicator: Euphrates River
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Publication Date
Tue Mar 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Analyzing indicators of the results of applying forecasting methods for production plans (A case study at the Diyala State Company for Electrical Industries)
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Economic organizations operate in a dynamic environment, which necessitates the use of quantitative techniques to make their decisions. Here, the role of forecasting production plans emerges. So, this study aims to the analysis of the results of applying forecasting methods to production plans for the past years, in the Diyala State Company for Electrical Industries.

The Diyala State Company for Electrical Industries was chosen as a field of research for its role in providing distinguished products as well as the development and growth of its products and quality, and because it produces many products, and the study period was limited to ten years, from 2010 to 2019. This study used the descriptive approa

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Publication Date
Tue May 23 2023
Journal Name
Journal Of Engineering
Assessment of Water Quality Index and Water Suitability of the Tigris River for drinking water within Baghdad City, Iraq
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In this study water quality was indicated in terms of Water Quality Index that was determined through summarizing multiple parameters of water test results. This index offers a useful representation of the overall quality of water for public or any intended use as well as indicating pollution, water quality management and decision making. The application of Water Quality Index
(WQI) with sixteen physicochemical water quality parameters was performed to evaluate the quality of Tigris River water for drinking usage. This was done by subjecting the water samples collected from eight stations in Baghdad city during the period 2004-2010 to comprehensive physicochemical analysis. The sixteen physicochemical parameters included: Turbidity, A

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Publication Date
Sat Jun 01 2013
Journal Name
Journal Of Engineering
Assessment of Water Quality Index and Water Suitability of the Tigris River for drinking water within Baghdad City, Iraq
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In this study water quality was indicated in terms of Water Quality Index that was determined through summarizing multiple parameters of water test results. This index offers a useful representation of the overall quality of water for public or any intended use as well as indicating pollution, water quality management and decision making. The application of Water Quality Index (WQI) with sixteen physicochemical water quality parameters was performed to evaluate the quality of Tigris River water for drinking usage. This was done by subjecting the water samples collected from eight stations in Baghdad city during the period 2004-2010 to comprehensive physicochemical analysis. The sixteen physicochemical parameters included: Turbidity,

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Publication Date
Mon Dec 24 2018
Journal Name
Civil Engineering Journal
Artificial Neural Network Model for the Prediction of Groundwater Quality
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The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be

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Publication Date
Sat Oct 20 2018
Journal Name
Journal Of Economics And Administrative Sciences
Using Multivariate GARCH Models CCC (Constant Conditional Correlation) and DCC(Dynamic Conditional Correlation) To Forecast Iraqi Dinar Exchange Rate in Dollar
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Abstract

Multivariate GARCH Models take several forms , the most important DCC dynamic conditional correlation, and CCC constant conditional correlation , The Purpose of this research is the Comparison for both Models.Using three  financial time series which is a series of daily Iraqi dinar exchange rate indollar, Global daily Oil price in dollar and Global daily gold price in dollarfor the period from 01/01/2014 till 01/01/2016, Where it has been transferred to the three time series returns to get the Stationarity, some tests were conducted including Ljung-Box , JarqueBera  , Multivariate ARCH to Returns Series and Residuals Series for both models In Comparison

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Publication Date
Fri May 16 2025
Journal Name
Asean Journal Of Science And Engineering
Enhancing Predictive Maintenance in Energy Systems Using a Hybrid Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) Framework for Rotating Machinery
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This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K

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Publication Date
Sun Mar 01 2020
Journal Name
Indian Journal Of Forensic Medicine And Toxicology,
Effect of algal extracts on the growth of tow bacterial types isloated from pollutants discharge
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Effect of Chlorococcum humicola alcoholic algae extract was studied on the growth of, Pseudomonas aeruginosa, and Klebsiella pneumonia, which were isolated from contaminated water. The extract of Ch. humicola showed a high efficiency in reducing the numbers of the two types of bacteria. . The removal rate of K. pneumonia were 0.0, 48.4 and 57.0, The removal rate of P. aeruginosa were 63.1, 79.8 and 82.9% after24,48, 72 h respectively. The results improved that the K. pneumonia is more sensitive than P. aeruginosa for algae extract concentrations used in study ,and the beast effective time is 24h for the two bacterial species The aim of the study was to eliminate microorganisms using the Alcoholic algae extract. Especially P. aeruginosa and

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Publication Date
Wed Oct 09 2024
Journal Name
Engineering, Technology & Applied Science Research
Improving Pre-trained CNN-LSTM Models for Image Captioning with Hyper-Parameter Optimization
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The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Environmental Science And Development
Estimation of the Concentrations of Some Pollutants Resulting from the Use of Arabian Bakhour and Their Effect in Patients with Asthma in the City of Baghdad
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This research focus on studying 3 types of Bakhour in the markets of Baghdad city and assessing their impact on the quality of life for asthmatic whom used Bakhour at their houses through investigating particles physical properties, also estimating the levels of heavy metals (Cd, Cu, Mn, Pb and Zn), Particulate Matter PM2.5, PM10, Total Volatile Organic Compounds (TVOC) and formaldehyde (HCHO). The quality of life for asthmatic patients whom use Bakhour was assessing by Mini Asthma Quality of Life Questionnaire. The results indicated that shapes of Bakhour particles were irregular or spherical. Burning process generated the higher percent of PM ˂1μm. Type 2 Bakhour showed the highest percent of <1μm which was 73%.The amount of

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