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Enhanced Supervised Principal Component Analysis for Cancer Classification
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In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods.

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Publication Date
Wed May 25 2022
Journal Name
Iraqi Journal Of Science
Effects of the Changes in the Neutron Number of Isotonic Nuclei on the Tow-Component Partial Level Density Formula Corrected for Pairing in Pre-Equilibrium Reactions
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Ericson’s formula describes the partial level density (PLD) of pre-equilibrium reactions and corrections. PLD with pairing correction can be calculated using four methods, namely, pairing, improved pairing, exact Pauli and back shift energy corrections. The variations in the PLD values of each of the four formulas of strontium (88Sr), Yttrium (89Y) and Zirconium (90Zr) isotones have been examined. Results shows that the PLD values that use pairing and improved pairing corrections do not vary for different isotones. However, a small change in PLD values is observed when exact Pauli correction and back shift energy were utilised. The change in the PLD values using back shift energy correction is bigg

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Publication Date
Wed Jan 01 2020
Journal Name
Advanced Composites Letters
Enhanced thermal and electrical properties of epoxy/carbon fiber–silicon carbide composites
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The silicon carbide/carbon fiber (SiC/CF) hybrid fillers were introduced to improve the electrical and thermal conductivities of the epoxy resin composites. Results of Fourier transform infrared spectroscopy revealed that the peaks at 3532 and 2850 cm−1 relate to carboxylic acid O–H stretching and aldehyde C–H stretching appearing deeper with an increased volume fraction of SiC. Scanning electron microscopic image shows a better interface bonding between the fiber and the matrix when the volume fraction of SiC particles are increased. As frequency increases from 102 Hz to 106 Hz, dielectric constants decrease slightly. Dissipation factor (tan δ) values keep low a

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Publication Date
Sun Jun 30 2024
Journal Name
Science And Technology Indonesia
Evaluating the Biocompatibility of Maxillofacial Silicone Enhanced by Hexagonal Boron Nitride Particles
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This study aims to evaluate the biocompatibility of a novel filler material intended to improve the longevity of polymer systems used in prosthetics in respect of cytotoxicity and skin irritation. RTV50F silicone elastomer incorporated with various percentages of hexagonal boron nitride (H-BN) (0.1, 0.3, 0.5, 0.7, and 1 wt%) have been tested. Silicone without H-BN was utilized as the control for comparison. The in vitro cytotoxicity test includes specimens (n=18) with 10 mm in diameter and 2 mm in thickness applied directly to the normal human fibroblast cell line (NHF) and incubated for 72 hours, then 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to determine the cell viability. The skin irritati

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Publication Date
Thu Dec 30 2021
Journal Name
Iraqi Journal Of Science
Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms
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    Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means

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Publication Date
Sun Jan 26 2020
Journal Name
Iraqi Journal Of Science
New Updated Classification of Shallow Earthquakes Based on Faulting Style
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Earthquakes occur on faults and create new faults. They also occur on  normal, reverse and strike-slip faults. The aim of this work is to suggest a new unified classification of Shallow depth earthquakes based on the faulting styles, and to characterize each class. The characterization criteria include the maximum magnitude, focal depth, b-constant value, return period and relations between magnitude, focal depth and dip of fault plane. Global Centroid Moment Tensor (GCMT) catalog is the source of the used data. This catalog covers the period from Jan.1976 to Dec. 2017. We selected only the shallow (depth less than 70kms) pure, normal, strike-slip and reverse earthquakes (magnitude ≥ 5) and excluded the oblique earthquakes. Th

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Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Engineering
Satellite Images Classification in Rural Areas Based on Fractal Dimension
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Fractal geometry is receiving increase attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite images. In this paper, a satellite image is used which was taken by Quick Bird that contains different visible classes. After pre-processing, this image passes through two stages: segmentation and classification. The segmentation carried out by hybrid two methods used to produce effective results; the two methods are Quadtree method that operated inside Horizontal-Vertical method. The hybrid method is segmented the image into two rectangular blocks, either horizontally or vertically depending on spectral uniformity crit

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Publication Date
Thu Jul 01 2021
Journal Name
Iraqi Journal Of Science
Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans
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COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The expe

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Publication Date
Mon Oct 10 2016
Journal Name
Iraqi Journal Of Science
Satellite image classification using KL-transformation and modified vector quantization
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In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

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Publication Date
Tue Dec 25 2018
Journal Name
Iraqi Journal Of Science
Demographic and Clinical Study on Patients Referred to The National Cancer Research Center for Pap Smears
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A pap test is  a simple technique which  can detect pre-cancerous and cancerous cells in the vagina and cervix. Cervical cancer is the easiest gynecologic cancer we could  prevent it, with regular screening tests and follow-up this screening may avoid  cervical cancer  or detact  it early.This study aims to estimate cytological changes and precancerus lesions during Pap smear test and visual inspection of the cervix on Iraqi women and determine the relationship with demographic characteristics.   The study included 50 women aged 18-56 years (mean 39 ±10)   in  National Cancer of  Research Center (NCRC) belong to Baghdad University. These women suffered from genital problems or

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Publication Date
Thu Jan 12 2017
Journal Name
Journal Of Agricultural And Food Chemistry
Using Standing Gold Nanorod Arrays as Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Detection of Carbaryl Residues in Fruit Juice and Milk
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