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Analyzing the behavior of different classification algorithms in diabetes prediction
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<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.</span>

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Publication Date
Sun Oct 25 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
EFFECT OF EXERCISES USING DIFFERENT RESISTORS IN THE DEVELOPMENT OF THE SPECIAL STRENGTH OF THE ARMS AND THE COMPLETION OF JAVELIN FOR THE ATHLETES OF THE SCHOOL SPECIALIZED ATHLETICS
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Athletics are different from other games as a competition between individuals to show their competence and physical ability to achieve new record numbers in the various activities and various between the boards, jumping and throwing and each type of these activities in particular performance so found the researcher to find the method of training resistors in the development of special power and achievement In the effectiveness of javelin, where the researchers chose the sample of the athletes from the specialized school of athletics to effectively throw the spear at the ages of 15-17 years and carried out the tests of the research, which includes the strength of the speed of the arms and explosive power and The various resistance exercise

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
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Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region
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Publication Date
Thu Dec 31 2015
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Performance Equations for Household Compressors Depending on Manufacturing Data for Refrigerators and Freezers
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Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm3.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment and Sodium Adsorption Ratio Prediction of Tigris River Using Artificial Neural Network
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Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

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Publication Date
Sun Feb 03 2019
Journal Name
Journal Of The College Of Education For Women
Anti-Social behavior and its relationship to the need to love at Baghdad University students
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1. Anti-social behavior Baghdad University students.
2. Know the differences in anti's social behavior according to gender, specialization.
3. Know-the need for love at Bagdad University students.
4. Know the relationship between anti-social behavior and the need to love.
The research sample consisted of (100) students, the researcher numbers tools to measure anti behavior and the need to love.
The results showed that college students have an anti-social behavior is weak and they need to love and there is no correlation between them Function relationship. Researcher has shown a number of recommendations and proposals.

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Publication Date
Tue Jul 14 2020
Journal Name
Materials Science Forum
The Cyclic Fatigue Behavior for 6061-T6 Al Alloy Shafts Processed by Laser Shock Peening
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The optimal combination of aluminum quality, sufficient strength, high stress to weight ratio and clean finish make it a good choice in driveshafts fabrication. This study has been devoted to experimentally investigate the effect of applying laser shock peening (LSP) on the fatigue performance for 6061-T6 aluminum alloy rotary shafts. Q-switched pulsed Nd:YAG laser was used with operating parameters of 500 mJ and 600 mJ pulse energies, 12 ns pulse duration and 10 Hz pulse repetition rate. The LSP is applied at the waist of the prepared samples for the cyclic fatigue test. The results show that applying 500 mJ pulse energy yields a noticeable effect on enhancing the fatigue strength by increasing the required number of cycles to fracture the

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Publication Date
Sat Dec 31 2022
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
EFFECT OF ADDING DIFFERENT LEVELS OF DILL SEEDS TO THE DIET ON PHYSIOLOGICAL TRAITS OF BROILERS : EFFECT OF ADDING DIFFERENT LEVELS OF DILL SEEDS TO THE DIET ON PHYSIOLOGICAL TRAITS OF BROILERS
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ABSTRACT

This study was conducted in the poultry field of the department of animal production, college of agricultural engineering sciences, university of Baghdad for the period from 10/15/2021 to 11/25/2021 with the aim of showing the effect of adding different levels of dill seeds to the diet on productive and carcass traits For broiler meat. In this study, 200 unsexed broiler chicks of breed (Ross 308) were used, one day age, with a starting weight of 41.46 g. The chicks were randomly distributed to 5 treatments, and each treatment included 4 replicates, 10 birds for each replicate. The birds were fed three diets: the starter diet, the growth diet and the final diet. The experiment treatments were T1,

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Publication Date
Fri Sep 27 2024
Journal Name
Journal Of Applied Mathematics And Computational Mechanics
Fruit classification by assessing slice hardness based on RGB imaging. Case study: apple slices
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Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

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