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Diagnosis of Diabetes Using Artificial Intelligence Programs
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Scientific development has occupied a prominent place in the field of diagnosis, far from traditional procedures. Scientific progress and the development of cities have imposed diseases that have spread due to this development, perhaps the most prominent of which is diabetes for accurate diagnosis without examining blood samples and using image analysis by comparing two images of the affected person for no less than a period. Less than ten years ago they used artificial intelligence programs to analyze and prove the validity of this study by collecting samples of infected people and healthy people using one of the Python program libraries, which is (Open-CV) specialized in measuring changes to the human face, through which we can infer the infection. Smart technologies have provided the medical field with health predictions for early detection, and digital diagnosis is one of the most important sources on which research has relied on. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Publication Date
Wed Mar 23 2011
Journal Name
Journal Of Baghdad College Of Dentistry
Factors associated with parotid gland enlargement among poorly controlled Type II Diabetes Mellitus
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Background: Microscopic examination of parotid gland reveals hypertrophy of the aciner cells sometimes two to three times greater than normal size of PG, in cases associated with longstanding diabetes. This study was designed to determine the effects of duration, fasting plasma glucose and glycosylated hemoglobin on parotid gland enlargement among poorly controlled type 2 diabetes mellitus. Subjects, Materials, and Method: This study was conducted on 36 parotid glands of 18 with type 2 DM , at age range ( 40-60) years, all of them were selected from subjects attending (Endocrine clinic for diabetic patients) in Baghdad Teaching Hospital. , pg was measured with ultrasonography in both longitudinal and horizontal plane. Results: the rate of e

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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem
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Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this
paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will
be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to
solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the
domain of optimization and operation research. Several research papers dealt with methods of solving this
issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested
employing the improved algorithm to confirm its effectiveness and evaluate its ex

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Publication Date
Sun Jun 01 2014
Journal Name
Nasaq
Mathematical Logical Intelligence and its Relationship with Achievement among College of Education Students in Baghdad Governorate
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Publication Date
Thu Dec 13 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Effect of Obesity and Socioeconomic Status on Adolescents' High School Students' Intelligence Quotient in Baghdad City
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Objective(s): To determine the effect of obesity and socioeconomic status upon adolescents' high school students' intelligence quotient in Baghdad City. Methodology: A descriptive design is carried throughout the study to determine the effect of obesity and socioeconomic status on adolescents' high schools students' intelligence quotient in Baghdad City for the period of January 7th 2017 to May 29th 2017. A non-probability, purposive sample, of (120) high school students, is selected. The sample is comprised of (12) students from 7th grade, (26) students from 8 th grade, (14) students from 9th grade, (3

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Publication Date
Sun Mar 04 2012
Journal Name
Baghdad Science Journal
Biochemical and Kinetic Studies on Alkaline Phosphatase and other Biochemical Features in Sera of Patients with type 2 Diabetes
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Background :Alkaline phosphatase (ALP) was a widely used marker for skeletal and hepatobiliary disorders, but its activity was also increased in atherosclerosis and peripheral vascular disease. Several study has showed that ALP activity was increased in the sera of diabetic patients. The current study was conducted to evaluate ALP activity in type 2 diabetic patients and optimum conditions for enzyme activity in their sera.Methods: This study was carried out at in AL-Yarmok hospital(diabetic center) between February /2009 and April /2009. Fifty two patients with type 2 diabetes have been enrolled. Besides BMI, WHR, serum fasting blood glucose, ALP, HbA1C,uric acid and lipid profile levels have been performed .The relationship bet

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Publication Date
Sat Jun 01 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Elements of the economic intelligence of the organization and its role in achieving economic growth: Applied research
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   The aim of the research is to demonstrate of the relation and the influence of the components of economic intelligence (strategic alertness, information security policy, impact policy) in achieving of economic growth (creativity, competitiveness, quality improvement). The questionnaire was used as a main tool for selected sample. Answers analyzed by using the statistical program (SPSS)  to calculate the arithmetic mean, standard deviation, weight percentage, correlation, F test, and Squared factor (R2).

 The research derived its importance from the distinguished role of information systems in the work of industrial companies, and its impact toward achieving economic growth rates in its various activities. T

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Publication Date
Sat Jan 01 2022
Journal Name
Aip Conference Proceedings
Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil
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Publication Date
Sat Dec 31 2022
Journal Name
Mathematical Modelling Of Engineering Problems
Experimental and Numerical Study of Open Channel Flow with T-Section Artificial Bed Roughness
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Experimental and numerical studies have been conducted on the effects of bed roughness elements such as cubic and T-section elements that are regularly half-channel arrayed on one side of the river on turbulent flow characteristics and bed erosion downstream of the roughness elements. The experimental study has been done for two types of bed roughness elements (cubic and T-section shape) to study the effect of these elements on the velocity profile downstream the elements with respect to different water flow discharges and water depths. A comparison between the cubic and T-section artificial bed roughness showed that the velocity profile downstream the T-section increased in smooth side from the river and decrease in the rough side

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Publication Date
Mon Feb 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology
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<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

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Publication Date
Tue Jan 14 2025
Journal Name
South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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