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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
Sun Dec 01 2013
Journal Name
Baghdad Science Journal
Assessment of Inflammasome Activity in Type 2 Diabetes Mellitus and Simple Obesity: Comparative Study
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Inflammasome is a multiprotein oligomer complex which is the precursor procaspase-1 and is a component of the innate immune system so that this study aimed to determine the serum levels of interleukin-1? and 18 in patients with T2D and simple obesity in an attempt to clarify the role of inflammasome in these disorders.Twenty five known cases of T2D, twenty four patients with simple obesity and twenty healthy subjects were randomly recruited from AL-Kindy Teaching Hospital in Baghdad to enroll in this study. All the data about the demographic characteristics and anthropometric measurements were determined in all patients, also the C-reactive protein and serum interleukin (IL)-1? and IL-18 levels were obtained from each patient. The results s

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Publication Date
Wed Jan 01 2020
Journal Name
Annals Of Tropical Medicine And Public Health
The extent of UMOD gene polymorphism and its level in type 2 diabetes patients
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching
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In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

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Publication Date
Thu Oct 31 2024
Journal Name
Iraqi Geological Journal
Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

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Publication Date
Thu Jul 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Role of F-box WD Repeat Domain Containing 7 in Type 1 Diabetes
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Type I diabetes (T1DM) is a chronic immune system disease characterized by the devastation or injury of ß-cells in the Langerhans Island, resulting in insulin deficiency and hyperglycemia. This study determines the new marker F-box and WD repeat domain containing 7 (FBXW7). One hundred twenty type 1 diabetic patients from three different places (central child hospital, Alkindi center for diabetes and endocrinology, Children’s Education Hospital) in Iraq during the period from (20 December 2021 to 25 March 2022) an age ranges of (4-17) years. The patient group consisted of being derived to three groups: group one healthy patient group (33) was included as healthy patient, group two (20) newly diagnosed T1DM and (67) type 1 diabetic wit

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Preparing a New Type of Concrete Based on Sulfur-melamine Modifier
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In this research work, a new type of concrete based on sulfur-melamine modification was introduced, and its various properties were studied. This new type of concrete was prepared based on the sulfur-melamine modification and various ingredients. The new sulfur-melamine modifier was fabricated, and its fabrication was confirmed by IR spectroscopy and TG analysis. The surface morphology resulted from this modifier was studied by SEM and EDS analysis. The components ratios in concrete, chemical and physical characteristics resulted from sulfur-melamine modifier, chemical and corrosion resistance of concrete, stability of concrete against water adsorption, stability of concrete against freezing, physical and mechanical properties and durabi

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data
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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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Publication Date
Thu Jun 01 2023
Journal Name
Journal Of Engineering
Fault Location of Doukan-Erbil 132kv Double Transmission Lines Using Artificial Neural Network ANN
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Engineering And Sustainable Development
Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Evaluation of Lipocalin-2 and Vaspin Levels in In Iraqi Women with Type 2 Diabetes Mellitus
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     The main objective of this study would be that if serum lipocalin-2 and Vaspin levels may be utilized as indicators for chronic in Type 2 diabetes mellitus (T2DM) patients. T2DM treatment is to maintain a healthy glycemic level. If this goal is not met, diabetes consequences, both acute and chronic, may emerge, one of which is obesity. As a result, researchers have investigated the levels of Lipocalin-2 and Vaspin, as well as their connection to obesity and insulin resistance. In this study, we included 60 T2DM (ages 35 to 65 years) and 30 healthy controls. After an overnight fast, blood serum samples were collected, and routine biochemical parameters such as lipocalin-2, Vaspin, and glucose were measured in all samples. At a

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