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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 Mar 08 2023
Journal Name
Sensors
A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology
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To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

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Publication Date
Wed Jan 12 2022
Journal Name
Iraqi Journal Of Science
Face Detection by Using OpenCV’s Viola-Jones Algorithm based on coding eyes
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Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc

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Publication Date
Sat Jun 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of some methods for estimating the parameters of the binary logistic regression model using the genetic algorithm with practical application
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Abstract

   Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model

    In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Robotics And Control (jrc)
Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells
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The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

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Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Investigating the Optimal Duration of the Sun Exposure for Adequate Cutaneous Synthesis of Vitamin D3 in Baghdad City: Depending on Fitzpatrick Skin Classification for Different Skin Types
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     Vitamin D3 deficiency is regarded as a public health issue in Iraq, particularly during the winter. Sun exposure is the main source of vitamin D3, where the surface ultraviolet (UV) radiation plays an important role in human health. The amount of time that must be spent in the sun each day was determined for the amount of exposed skin, for all skin types, with and without sunscreen under clear sky conditions in the city of Baghdad (Long 44.375, Lat 33.375). UV index data was obtained by TEMIS satellite during the year 2021. From data analysis, we found that most days during the year were within the high level of ultraviolet radiation values ​​in the city of Baghdad, and most of them were during the summer, where the person n

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Publication Date
Tue Mar 01 2016
Journal Name
Journal Of Pharmaceutical Sciences
Development and Evaluation of Biodegradable Particles Coloaded With Antigen and the Toll-Like Receptor Agonist, Pentaerythritol Lipid A, as a Cancer Vaccine
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Publication Date
Fri Feb 26 2021
Journal Name
Iraqi Journal Of Science
Study of β-Catenin as Immunohistochemistry Marker in Women with Breast Cancer
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Background & Objective: Breast cancer (BC) is the most prevalent disease among women around the world, considered the world's leading cause of death (15% of the total cancer deaths) in women in 2018. β-catenin is a multifunctional protein located in the cytoplasm and/or nucleus of the cell. Several studies suggested that β-catenin expression plays a critical role in cancer invasion and metastasis. This research sought to examine β-catenin expression in breast cancer and its associations with clinico-pathological features (such as histopathological types, grade, and invasion depth of tumor as well as lymph node involvement) and breast cancer patient survival. Methods:

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Publication Date
Tue Jun 23 2015
Journal Name
Internattiional Journal Of Pharma Sciences
Assessment of her2neu expression using immunohistochemistry in association with clinicopathological features and hormonal receptors in Iraqi breast cancer women patients
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Seventy four Iraqi breast cancer paraffin blocks were collected from patients were attended to center health laboratory, histopathology department, Bagdad, Iraq. The patients information’s which included: name, age, and the pathological stage, grade, tumor size were obtained from the clinical records of the patients also relation with sex hormones was recorded. The cases which has been taken included invasive ductal and invasive lobular carcinoma type Women age were ranged from 24-80 years peak age frequency of tumor occurred in the category of more than 40 years old. Immunohistochemical expression of her-2/neu was from total 74 cases of infiltrative ductal carcinoma cases, 27(36.49%)were positive for Her-2/neu expression, 47(63.51%) were

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Publication Date
Wed Apr 15 2020
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Antimicrobial resistance patterns of Acinetobacter baumannii colonization patients skin
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Background: Acinetobacter baumannii is a significant opportunistic pathogen and it is generally associated with benign colonization of hospitalized patients.

Objective: To investigate skin colonizationwith Acinetobacter baumannii in hospitalized patients and healthy volunteers.Antimicrobial resistance patterns of Acinetobacter baumanniiwas assessed by determining the minimum inhibitory concentrations (MICs) of thirteen different antimicrobial agents.

Patients and Methods: The study performed on hospitalized patients at Rizgary and Hawler teaching hospitals and healthy volunteers who attended to supermarkets in Erbil, Iraq. A single sample was ob

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering
Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm
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In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%

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