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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
Mon Dec 25 2017
Journal Name
Al-khwarizmi Engineering Journal
A new Cumulative Damage Model for Fatigue Life Prediction under Shot Peening Treatment
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 Abstract

In this paper, fatigue damage accumulation were studied using many methods i.e.Corton-Dalon (CD),Corton-Dalon-Marsh(CDM), new non-linear model and experimental method. The prediction of fatigue lifetimes based on the two classical methods, Corton-Dalon (CD)andCorton-Dalon-Marsh (CDM), are uneconomic and non-conservative respectively. However satisfactory predictions were obtained by applying the proposed non-linear model (present model) for medium carbon steel compared with experimental work. Many shortcomings of the two classical methods are related to their inability to take into account the surface treatment effect as shot peening. It is clear that the new model shows that a much better and cons

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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Publication Date
Wed Mar 24 2021
Journal Name
Ieee Access
Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System
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An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

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Publication Date
Fri Nov 01 2024
Journal Name
Current Medicinal Chemistry
Synthesis, In Silico Prediction, and In Vitro Evaluation of Anti-tumor Activities of Novel 4'-Hydroxybiphenyl-4-carboxylic Acid Derivatives as EGFR Allosteric Site Inhibitors
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Introduction:

Allosteric inhibition of EGFR tyrosine kinase (TK) is currently among the most attractive approaches for designing and developing anti-cancer drugs to avoid chemoresistance exhibited by clinically approved ATP-competitive inhibitors. The current work aimed to synthesize new biphenyl-containing derivatives that were predicted to act as EGFR TK allosteric site inhibitors based on molecular docking studies.

Methods:

A new series of 4'-hydroxybiphenyl-4-carboxylic acid derivatives, including hydrazine-1-carbothioamide (S3-S6) and 1,2,4-triazole (S7-S10) derivatives, were synthesized and characterized using IR, 1HNMR, 13CNMR

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Publication Date
Thu Jun 15 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Oral Health Status, Salivary MMP-8& Secretory Leukocyte Peptidase Inhibitor (SLPI) Among Uncontrolled Type-I Diabetes Mellitus In Iraqi Patients
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Background: diabetes is a metabolic disease characterized by hyperglycemia that results in deficiency or absence of insulin production. The dental caries and gingivitis/periodontitis are widespread chronic diseases in diabetes. The aim of the present study was determined the salivary matrix metalloproteinase (MMP-8), Secretory Leukocyte Peptidase Inhibitor (SLPI) and oral health status among uncontrolled diabetic group in comparison with healthy control group. Materials and Methods: The total sample composed of 90 adults aged (18-35) years. Divided into 60 uncontrolled diabetic patients (HbA1c >7%) and 30 healthy control group. Unstimulated saliva was collected from each subject with type-I DM, BMI, duration of diabetes, HbA1c%, DMFT, gingi

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Publication Date
Fri Jun 27 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Salivary α-Amylase and Albumin Levels in Patients with Chronic Periodontitis and Poorly or Well Controlled Type II Diabetes Mellitus
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Background: Recent studies suggest that chronic periodontitis (CP) and type2 diabetes mellitus (T2DM) are bidirectionally associated. Analysis of saliva as a mirror of oral and systemic health could allow identification of α amylase (α-Am) and albumin (A1) antioxidant system markers to assist in the diagnosis and monitoring of both diseases. The present study aims at comparing the clinical periodontal parameters in chronic periodontitis patients with poorly or well controlled Type 2Diabetes Mellitus, salivary α-Am, A1, flow rate (FR) and pH then correlate between biochemical, physical and clinical periodontal parameters of each study and control groups. Materials and Methods: 80 males, with an age range of (35-50) years were divide

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Advanced Biotechnology And Experimental Therapeutics
Kidney injury molecule-1 and cystatin C as early biomarkers for renal dysfunction in Iraqi type 2 diabetes mellitus patients
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Diabetic kidney disease (DKD) is caused by a variety of processes. As a result, one biomarker is insufficient to represent the complete process. This study Evaluate the diagnostic value of serum kidney injury molecule-1(KIM-1) and cystatin C (CysC) as early biochemical markers of DKD and predictive their sensitivities and specificities as biomarkers of nephropathy in Iraqi type 2 diabetic (T2DM) patients. This cross-sectional study include 161 T2DM patients from Diabetes and Endocrinology Center at Merjan medical city in Babylon. Patients divided according to urinary albumin creatinine ratio(ACR) (Group1:ACR≤30mg/g,Group2:ACR>30mg/g). Random spot urine and fasting blood samples were taken from each patient and urinary ACR, bloo

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Publication Date
Thu Apr 01 2021
Journal Name
Biochem. Cell. Arch.
AGE AND GENDER IMPACT ON GLYCAEMIC CONTROL, RENAL FUNCTION AND OXIDATIVE STRESS PARAMETERS IN IRAQI PATIENTS TYPE 2 DIABETES MELLITUS
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Type 2 daibetes mellitus (T2DM) is a global concern boosted by both population growth and ageing, the majority of affected people are aged between (40- 59 year). The objective of this research was to estimate the impact of age and gender on glycaemic control parameters: Fasting blood glucose (FBC), glycated hemoglobin (HbA1C), insulin, insulin resistance (IR) and insulin sensitivity (IS), renal function parameters: urea, creatinine and oxidative stress parameters: total antioxidant capacity (TAC) and reactive oxygen species (ROS). Eighty-one random samples of T2DM patients (35 men and 46 women) were included in this study, their average age was 52.75±9.63 year. Current study found that FBG, HbA1C and IR were highly significant (P<0.01) inc

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Publication Date
Sat Jun 19 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
The Pharmacological Effects of Kappa Carrageenan on Different Human Cell Lines and Genomic DNA: An in vitro study
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Carrageenan extract is a compound of sulfated polyglycan that is taken out from red seaweeds. Being hydrocolloid in nature, carrageenan has gelling, emulsifying and thickening properties allowing it to be commonly used in the oral healthcare products and cosmetics. Due to its bioactive compounds, carrageenan has been shown to have antimicrobial, antiviral, and antitumor properties. The purpose of this work is to study the probable use of carrageenan on the diseases that are related to oral cavity and on the genomic DNA in in vitro experimental model

In this study, the effects of k-carrageenan on four different cell lines related to the cancer and normal cells which cultured on selective media were done. Moreover, the eff

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Publication Date
Sun Jul 01 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
ESTIMATION OF GENETIC VARIATIONS IN DIFFERENT TAXA IN BRASSICACEAE BY RAPD AND ISSR ANALYSIS
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Twelve species from Brassicaceae family were studied using two different molecular techniques: RAPD and ISSR; both of these techniques were used to detect some molecular markers associated with the genotype identification. RAPD results, from using five random primers, revealed 241 amplified fragments, 62 of them were polymorphic (26%).

 

    ISSR results showed that out of seven primers, three (ISSR3, UBC807, UBC811) could not amplify the genomic DNA; other primers revealed 183 amplified fragments, 36 of them were polymorphic (20%). The similarity evidence and dendrogram for the genetic distances of the incorporation between the two techniques showed that the highest similarity was 0.897 between the va

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