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Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
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The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.

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Publication Date
Fri Jan 01 2016
Journal Name
Machine Learning And Data Mining In Pattern Recognition
A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association
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Publication Date
Thu Dec 13 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Causes of maternal death in Mizan Aman and Gebretsadik shawo general hospitals from 2011 – 2015: A case - control study using propensity score matching analysis
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Objective: To identify causes of maternal death in Mizan Aman and Gebretsadik shawo general hospitals
Methodology: A case control study on 595 charts, 119 cases and 476 controls was conducted in Mizan
Aman & Gebretsadik shawo general hospitals. Data was analyzed by STATA 13.1. Propensity score
matching analysis was used to see causes of maternal death.
Results: Hemorrhage were the main direct causes of maternal death which accounts 47.9% (β =0.58
(95% CI (0.28,0.87)) in hospital but when projected to population based the sample (β =0.26 (95% CI
(0.22,0.31)). Followed by infection 36 (25.21%) (β = 0.50 (95% CI (0.08, 0.92)). when projected to
population based the sample PIH 7.6%) is significant cause (β = 0.16

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Publication Date
Tue Nov 01 2016
Journal Name
Journal Of Engineering
Synthesis and Characterization Of CaMgO2 Nanoparticles Photocatalyst For the Decolorization Of Orange G Dye
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A series of heterogeneous basic catalysts of CaO, MgO and CaMgO2 at different calcination temperature were synthesized via solution combustion method. Different characterization techniques have been carried out to investigate the structure of the produced catalysts i.e. X-ray diffraction (XRD), particle size analyzer, morphology by atomic force microscope (AFM) and reflection using UV-VIS diffuse reflectance spectra. The particles size analyzer revealed that the mixed oxide catalysts calcined at different calcination temperature possess smaller nano size particles compared to pure CaO. Moreover, the energy band gap was calculated based on the results of diffuse reflectance spectra. The energy band gap was redu

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Engineering
Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq
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The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The develope

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Engineering/
Water quality assessment and total dissolved solids prediction using artificial neural network in Al-Hawizeh marsh south of Iraq
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The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The

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Publication Date
Wed Jan 01 2020
Journal Name
Methods And Objects Of Chemical Analysis
Predicting The Composition Of Qurna Crude Oil Fraction By Ternary Composition Diagram
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With a goal to identify, and ultimately removing from the oil fraction, the carcinogenic components, an oil fraction oil has been analyzed into a main three hydrocarbon groups, paraffins, aromatics, and polycyclic saturates. A multi-stage adsorption apparatus has been used. Four units of 300 g alumina each seems to be sufficient for removing the polynuclear aromatics from 75 g of an oil fraction boiling between 365-375 °C from Qurna crude oil. The usefulness of the ternary diagram for analyzing the oil fraction to the three hydrocarbons groups has been studied and verified. An experimentally based linear relationship of density and refractive index was established to enable of identifying the composition of an oil fraction using th

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Publication Date
Sun Oct 01 2017
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Pre-operative serum TSH level estimation for predicting malignant nodular thyroid disease
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Publication Date
Wed Aug 28 2019
Journal Name
Journal Of Engineering
Predicting Wetting Patterns in Soil from a Single Subsurface Drip Irrigation System
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Soil wetted pattern from a subsurface drip plays great importance in the design of subsurface drip irrigation (SDI) system for delivering the required water directly to the roots of the plant. An equation to estimate the dimensions of the wetted area in soil are taking into account water uptake by roots is simulated numerically using HYDRUS (2D/3D) software. In this paper, three soil textures namely loamy sand, sandy loam, and loam soil were used with three different types of crops tomato, pepper, and cucumber, respectively, and different values of drip discharge, drip depth, and initial soil moisture content were proposed. The soil wetting patterns were obtained at every thirty minutes for a total time of irrigation equ

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Publication Date
Wed Oct 17 2018
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
ESTIMATION OF MUNICIPAL SOLID WASTE GENERATION AND LANDFILL VOLUME GENERATION AND LANDFILL VOLUME USING ARTIFICIAL NEURAL NETWORKS
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Publication Date
Sat Dec 01 2018
Journal Name
Indian Journal Of Ecology
Classification of al-hammar marshes satellite images in Iraq using artificial neural network based on coding representation
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