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A Prediction of Skin Cancer using Mean-Shift Algorithm with Deep Forest Classifier
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      Skin cancer is the most serious health problems in the globe because of its high occurrence compared to other types of cancer. Melanoma and non-melanoma are the two most common kinds of skin cancer. One of the most difficult problems in medical image processing is the automatic detection of skin cancer. Skin melanoma is classified as either benign or malignant based on the results of this test. Impediment due to artifacts in dermoscopic images impacts the analytic activity and decreases the precision level. In this research work, an automatic technique including segmentation and classification is proposed. Initially, pre-processing technique called DullRazor tool is used for hair removal process and semi-supervised mean-shift algorithm is used for segmenting the affected areas of skin cancer images. Finally, these segmented images are given to a deep learning classifier called Deep forest for prediction of skin cancer. The experiments are carried out on two publicly available datasets called ISIC-2019 and HAM10000 datasets for the analysis of segmentation and classification. From the outcomes, it is clearly verified that the projected model achieved better performance than the existing deep learning techniques.

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Publication Date
Wed Jan 30 2013
Journal Name
Al-kindy College Medical Journal
Regeneration of Pain, Touch, Cold, and Warmth, sensations in split thickness human skin grafts in adults (A clinical study)
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Back ground: Skin grafting is the most common form
of reconstructive surgery, and regeneration of
sensations in skin grafts is a complex process
influenced by many factors such as , the thickness of
the graft, the depth of the grafted bed, meshing of the
graft, the condition of the bed and the surrounding
area. So many studies performed on this subject, some
of them clinically based on subjective type of sensation
tests, and others histological to detect the presence of
nerve fibers in the grafted skin
Objectives: To detect return of sensations to split
thickness skin grafts by clinical methods.
Methods: From Oct. 1995 to Oct. 2010, a clinical
prospective study performed in Al wasity Hospital for

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers
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      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Epidemiology of Skin Diseases among Displaced People in Diyala Province
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Background: Diyala have many internally displaced persons as a consequence of the armed conflict. Those peoples experience serious health problems related to their displacement, including skin disorders.
Objective: To determine the prevalence of skin diseases and the use of health care among displaced patients in Diyala.
Methods: A case-series study conducted on 246 displaced patients from May to November 2017, who attended Baqubah teaching hospital in Diyala. All patients were diagnosed by dermatologists depending on clinical findings.
Results: A total of 246 displaced patient from all age groups mean±SD (21.9±18.59) years, range 1-64) consulate the clinic, of them (29.3%) male and (70.7%) female with male to female ratio (1:

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Publication Date
Sun Jan 03 2016
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Serum Lipid Profile in Iraqi patients with Breast Cancer
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Background: Breast cancer (BC) is a type of cancer originating from breast tissue, Lipid profile seems to influence the development of female breast cancer, especially in the presence of an increased body mass index so.
Objective: to explore the status of lipid profile in women with breast cancer.
Subjects and methods: the present study is a cross-sectional study (2010/2011) done at Al-Yarmouk Teaching Hospital. Includes measurement of LP in sera of postmenapausal newly diagnosed women with BC in comparison with healthy control women. This measurement was done using colorimetric method. In The results of this study include a total of 100 patients with BC were involved in this study, they were classified as newly diagnosed postmenop

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Fri Jan 01 2016
Journal Name
Engineering And Technology Journal
Face Retrieval Using Image Moments and Genetic Algorithm
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Publication Date
Thu Dec 31 2015
Journal Name
Al-khwarizmi Engineering Journal
Hexapod Robot Static Stability Enhancement using Genetic Algorithm
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Abstract

Hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot look likes the insect so it has the same gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking is called (the stability margin). In this paper, the forward and inverse kinematics are derived for each hexapod’s leg in order to simulate the hexapod robot model walking using MATLAB R2010a for all gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Nurse Scheduling Problem Using Hybrid Simulated Annealing Algorithm
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Nurse scheduling problem is one of combinatorial optimization problems and it is one of NP-Hard problems which is difficult to be solved as optimal solution. In this paper, we had created an proposed algorithm which it is hybrid simulated annealing algorithm to solve nurse scheduling problem, developed the simulated annealing algorithm and Genetic algorithm. We can note that the proposed algorithm (Hybrid simulated Annealing Algorithm(GS-h)) is the best method among other methods which it is used in this paper because it satisfied minimum average of the total cost and maximum number of Solved , Best and Optimal problems. So we can note that the ratios of the optimal solution are 77% for the proposed algorithm(GS-h), 28.75% for Si

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Publication Date
Sun Mar 04 2018
Journal Name
Iraqi Journal Of Science
Image Encryption Using DNA Encoding and RC4 Algorithm
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Nowadays, the rapid development of multi-media technology and digital images transmission by the Internet leads the digital images to be exposed to several attacks in the transmission process. Therefore, protection of digital images become increasingly important.

 To this end, an image encryption method that adopts Rivest Cipher (RC4) and Deoxyribonucleic Acid (DNA) encoding to increase the secrecy and randomness of the image without affecting its quality is proposed. The Means Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Coefficient Correlation (CC) and histogram analysis are used as an evaluation metrics to evaluate the performance of the proposed method. The results indicate that the proposed method is secure ag

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