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Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network
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     Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments.

The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, of which 240 are diabetic and 42 are non-diabetic patients. The model consists of three main phases.  In the first phase, two steps are applied as a pre-processing for the dataset, which include statistical analysis and missing values handling. In the second phase, feature extraction is used for diabetes Type II using three main features, reflecting measurements of three blood parameters (C. peptide, fasting Blood Sugar, and Haemoglobin A1C). Finally, classification and performance evaluation are implemented using Feed Forward Neural Network algorithm. The experimental results of the performance of the proposed model showed 98.6% accuracy for diabetes classification.

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Publication Date
Fri Dec 14 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of Diabetes Self-Management among Patients in Baghdad City: A Comparative Study
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Objective(s): To Evaluate Diabetes self –management among patients in Baghdad City and to compare
between these patients self-management relative to the type of the disease.
Methodology: A descriptive design was conducted in Baghdad city, started from November 16th 2017 to the
end of May 17 th 2018 in order to evaluate Diabetes self-management. Purposive (non-probability) sample,
which was consisted of (120) patients who were diagnosed with D.M. The sample is comprised of (60) patient
with diabetes type I and (60) patient with diabetes type II. It is consisted of (60) male and (60) female. A
questionnaire is constructed for the purpose of the study. It is composed of (42) items. Reliability and validity of
the ques

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Publication Date
Mon Apr 01 2013
Journal Name
International Journal Of Electrical, Electronics And Telecommunication Engineering
Performance Analysis of xPON Network for Different Queuing Models
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Passive optical network (PON) is a point to multipoint, bidirectional, high rate optical network for data communication. Different standards of PONs are being implemented, first of all PON was ATM PON (APON) which evolved in Broadband PON (BPON). The two major types are Ethernet PON (EPON) and Gigabit passive optical network (GPON). PON with these different standards is called xPON. To have an efficient performance for the last two standards of PON, some important issues will considered. In our work we will integrate a network with different queuing models such M/M/1 and M/M/m model. After analyzing IPACT as a DBA scheme for this integrated network, we modulate cycle time, traffic load, throughput, utilization and overall delay

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Publication Date
Sat Aug 01 2015
Journal Name
2015 Ieee Conference On Computational Intelligence In Bioinformatics And Computational Biology (cibcb)
Granular computing approach for the design of medical data classification systems
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Publication Date
Sun Mar 04 2018
Journal Name
Iraqi Journal Of Science
Classification of k-Sets in PG(1,25), for k=4,…,13
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A -set in the projective line is a set of  projectively distinct points. From the fundamental theorem over the projective line, all -sets are projectively equivalent. In this research, the inequivalent -sets in have been computed and each -set classified to its -sets where  Also, the  has been splitting into two distinct -sets, equivalent and inequivalent.

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Classification of Diseases in Oil Palm Leaves Using the GoogLeNet Model
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The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthe

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
Effect of Genetic Algorithm as a Feature Selection for Image Classification
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     Analysis of image content is important in the classification of images, identification, retrieval, and recognition processes. The medical image datasets for content-based medical image retrieval (  are large datasets that are limited by high computational costs and poor performance. The aim of the proposed method is to enhance this image retrieval and classification by using a genetic algorithm (GA) to choose the reduced features and dimensionality. This process was created in three stages. In the first stage, two algorithms are applied to extract the important features; the first algorithm is the Contrast Enhancement method and the second is a Discrete Cosine Transform algorithm. In the next stage, we used datasets of the medi

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Publication Date
Sun Jul 28 2019
Journal Name
Open Access Macedonian Journal Of Medical Sciences
Value of Combination of Standard Axial and Thin-Section Coronal Diffusion-weighted Imaging in Diagnosis of Acute Brainstem Infarction
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AIM: To determine the value of the combination of thin-section 3 mm ‎coronal and standard ‎axial DWI and their impact in facilitating the diagnosis of ‎‎acute brainstem infarction. METHODS: A cross-sectional study conducted from the 1st of April 2017 to the end of February 2018 on 100 consecutive patients (66% were male, and 34% were female) with isolated acute ischemic infarction in the ‎brainstem. The abnormal MRI findings concerning the ischemic lesions were interpreted on standard axial 5 mm and thin-section coronal 3mm DWI. RESULTS: The mean age of the studied group was 69.2 ± 4.3 for male and 72.3 ± 2.5 years. The standard axial DWI can diagnose 20%, 6.7% and 6.7% of the infarctions in midbrain, pons an

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Publication Date
Wed Jun 30 2021
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
SYNTHESIS OF NEW LEVOFLOXACIN SELECTIVE MEMBRANE SENSOR BASED ON MOLECULARLY IMPRINTED POLYMERS.: SYNTHESIS OF NEW LEVOFLOXACIN SELECTIVE MEMBRANE SENSOR BASED ON MOLECULARLY IMPRINTED POLYMERS.
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Two molecular imprinted polymer (MIP) membranes for Levofloxacin (LEV) were prepared based on PVC matrix. The imprinted polymers were prepared by polymerization of styrene (STY) as monomer, N,N methylene di acrylamide as a cross linker ,benzoyl peroxide (BPO) as an initiator and levofloxacin as a template. Di methyl adepate (DMA) and acetophenone (AOPH) were used as plasticizers , the molecular imprinted membranes and the non molecular imprinted membranes were prepared.  The slopes and detection limits of the liquid electrodes ranged from -21.96 – -19.38 mV/decade and 2×10-4M- 4×10-4M, and Its response time was around 1 minute, respectively. The liquid  electrodes were packed with 0.1 M standar

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Publication Date
Wed May 03 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Strongly (Completely) Hollow Sub-modules II
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  Let M be an R-module, where R is commutative ring with unity. In this paper we study the behavior of strongly hollow and quasi hollow submodule in the class of strongly comultiplication modules. Beside this we give the relationships between strongly hollow and quasi hollow submodules with V-coprime, coprime, bi-hollow submodules.

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Publication Date
Mon Feb 04 2019
Journal Name
Iraqi Journal Of Physics
Studying the contribution of components and type of spiral galaxy NGC 6946 using digital image processing
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NGC 6946 have been observed with BVRI filters, on October 15-18,
2012, with the Newtonian focus of the 1.88m telescope, Kottamia
observatory, of the National Research Institute of Astronomy and
Geophysics, Egypt (NRIAG), then we combine the BVRI filters to
obtain an astronomical image to the spiral galaxy NGC 6946 which
is regarded main source of information to discover the components of
this galaxy, where galaxies are considered the essential element of
the universe. To know the components of NGC 6946, we studied it
with the Variable Precision Rough Sets technique to determine the
contribution of the Bulge, disk, and arms of NGC 6946 according to
different color in the image. From image we can determined th

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