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Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
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Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are further classified into either malignant or benign. The collected 20 breast cancer features are utilized to test the performance of the proposed classification system with Leave-One-Out (LOO) cross validation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance the classes. Furthermore, correlation-based feature selection (CFS) was employed in an exploratory analysis to find the best features for the 2-stage classification system.

Results: Classification accuracy of 94% for stage-1 and 100% for stage-2was achieved with a Naïve Bayesclassifier which outperformed other three methods. In addition, CFS selected small subset of features as being the best five features out of the all 20 features for both stage-1 and stage-2.

Conclusion: We achieved a high classification accuracy which is promising to help improve the early diagnosis of breast tumor. The outcome of this study also shows the importance of CA15-3protein in saliva and blood as well as carcinoembryonic antigen level and total protein in blood, and Estrogen hormone level in saliva, for predicting breast tumors.

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Publication Date
Thu May 05 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Classification SINGLE-LEAD ECG by using conventional neural network algorithm
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Publication Date
Sun Apr 30 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Modeling of the Effect of Grain Size on Final Stage of Sintering Al2O3
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  This research studies the effect of grain size for the final stage in sintering Al2O3 .The experimental results for α- Al2O3 powder are modeled using ( L2-Regression ) technique in  order to study the effect grain size distribution on densification rate using four kinds for the initial particle size which were ( 1.44 , 2.54 , 0.7-2.54 ,1.15-3.53 ) µm , and for sintering time  (0-241) min. The mathematical simulation for grain size changing shows that the densification rates boots up as the grain size goes lower, this was due to the increase of contact area between the grains.

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Publication Date
Sat Jul 01 2023
Journal Name
Electric Power Systems Research
Analytical and measurement-based wideband two-port modeling of DC-DC converters for electromagnetic transient studies
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Power-electronic converters are essential elements for the effective interconnection of renewable energy sources to the power grid, as well as to include energy storage units, vehicle charging stations, microgrids, etc. Converter models that provide an accurate representation of their wideband operation and interconnection with other active and passive grid components and systems are necessary for reliable steady state and transient analyses during normal or abnormal grid operating conditions. This paper introduces two Laplace domain-based approaches to model buck and boost DC-DC converters for electromagnetic transient studies. The first approach is an analytical one, where the converter is represented by a two-port admittance model via mo

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Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Bayes Estimators With others , for scale parameter and Reliability function of two parameters Frechet distribution
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Publication Date
Sun Sep 25 2022
Journal Name
American Scientific Research Journal For Engineering, Technology, And Sciences
Design and Fabrication of an Electromechanical Tester to Perform Two-dimensional Tensile Testing for Flexible Materials
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There are many diseases that affect the arteries, especially those related to their elasticity and stiffness, and they can be guessed by estimating and calculating the modulus of elasticity. Hence, the accurate calculation of the elastic modulus leads to an accurate assessment of these diseases, especially in their early stages, which can contribute to the treatment of these diseases early. Most of the calculations used the one-dimensional (1D) modulus of elasticity. From a mechanical point of view, the stresses to which the artery is subjected are not one-dimensional, but three-dimensional. Therefore, estimating at least a two-dimensional (2D) modulus of elasticity will necessarily be more accurate. To the knowledge of researchers, there i

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Publication Date
Sat Mar 18 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Effect of Drought Stress on Barley ( Hordeum vulgare L.) in Germination Stage
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The Present study was aimed to evaluate the responses of sixteen species of barley Hordeum vulgar L. cultivated in Iraq against drought stress through treatments of seedling with varying concentrations of Polyethylene glycol (PEG6000) to reach the water stress(-3,-6,-9) in addition to control (without PEG 6000). The results showed variation of barley species in their ability to tolerate the drought stress. For instance, Boraq and Arefat were shown to be the most tolerable species, since  their percentage of seeds germination were non significantly affected by the highest water potential (-9)  bar, while (bawadi,amal,nor alqadsea and nomar) were shown to be moderately drought tolerable, since their percentage of  seeds germ

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Advance Science And Technology
MR Images Classification of Alzheimer's Disease Based on Deep Belief Network Method
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Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

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Publication Date
Sun Feb 03 2019
Journal Name
Iraqi Journal Of Physics
Change detection of remotely sensed image using NDVI subtractive and classification methods.
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Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac

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Publication Date
Sun Feb 17 2019
Journal Name
Iraqi Journal Of Physics
Classification of the galaxy Milky Way using variable precision rough sets technique
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Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

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Publication Date
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type
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Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

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