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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
Mon Jul 01 2019
Journal Name
Iop Conference Series: Materials Science And Engineering
Optical Diagnosis of Prostate Cancer
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Publication Date
Fri Jan 06 2023
Journal Name
Journal Of Advanced Biotechnology And Experimental Therapeutics
Evaluation of oxidative stress activity and the levels of homocysteine, vitamin B12, and DNA methylation among women with breast cancer
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Breast cancer (BC) is the most common malignant tumor in women and the leading cause of cancer deaths worldwide. This work was conducted to estimate the roles of oxidative stress, vitamin B12, homocysteine (HCY), and DNA methylation in BC disease progression. Sixty BC patients (age range 33–80 years) and 30 healthy controls were recruited for this study. Patients with BC were split to group 1 consisted of stage II BC women (low level), and group 2 consisted of patients in stages III and IV (high level). Malondialdehyde (MDA), glutathione peroxidase 3 (GPX3), HCY, and vitamin B12 levels in the study groups were measured. Also, the 5-methylcytosine (5mC) global DNA methylation levels were evaluated. The results showed a significant

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Publication Date
Sun Sep 02 2018
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Applying food frequency questionnaire to evaluate the dietary pattern and life style on women with breast cancer
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Background: Breast cancer is the most common cancer among women and ranked number two after
lung cancer in the world. According to the World Health Organization, breast cancer accounts for
22.9% among cancers in women in 2012.
Objective: This study was designed to evaluate the dietary pattern particularly dairy products
consumption and the role of lifestyle on women with breast cancer in Baghdad city and its suburbs.
Subjects and Methods: The study was conducted on 100 women with breast cancer, as a case group,
compared with a control group comprised of 100 healthy women. Demographic data were collected
from both groups. A food frequency questionnaire was used of 33 items of foods for reporting the

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Publication Date
Sat Jan 30 2021
Journal Name
Iraqi Journal Of Science
Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems
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A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm

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Publication Date
Thu Dec 30 2021
Journal Name
Al-kindy College Medical Journal
Cancer Patients’ Experiences and Understanding of Venous Thromboembolism
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Background: Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), presents an extra challenge in the management of patients with cancer, given the increase in morbidity and mortality in having both conditions. Cancer patients are well known to have a high risk of VTE; particularly; those who have had major surgery, chemotherapy and/or hormonal therapy. These groups of patients need to understand the risk factors and the prophylactic measures to prevent developing VTE. This review aims to provide an overview of the literature on cancer patients’ understanding of VTE and their experiences of cancer-associated thrombosis (CAT).

Method: A scoping review wa

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Publication Date
Mon Sep 12 2022
Journal Name
Electronics
TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem
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Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wol

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Publication Date
Thu Jun 01 2023
Journal Name
Iaes International Journal Of Artificial Intelligence (ij-ai)
Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm
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<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. T

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Publication Date
Mon Feb 20 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A Hybrid Algorithm to Protect Computer Networks Based on Human Biometrics and Computer Attributes
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Objective of this work is the mixing between human biometric characteristics and unique attributes of the computer in order to protect computer networks and resources environments through the development of authentication and authorization techniques. In human biometric side has been studying the best methods and algorithms used, and the conclusion is that the fingerprint is the best, but it has some flaws. Fingerprint algorithm has been improved so that their performance can be adapted to enhance the clarity of the edge of the gully structures of pictures fingerprint, taking into account the evaluation of the direction of the nearby edges and repeat. In the side of the computer features, computer and its components like human have uniqu

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
An Effective Hybrid Deep Neural Network for Arabic Fake News Detection
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

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Publication Date
Tue Mar 02 2021
Journal Name
Turkish Journal Of Computer And Mathematics Education
Deep understanding skills and their relationship to mathematical modelling among fifth grader
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Abstract: The aim of the current research is to identify (the relationship between deep understanding skills and mathematical modeling among fifth grade students) the research sample consisted of (411) male and female students of the fifth grade of biology distributed over the General Directorates of Education in Baghdad / Al-Rusafa / 2 / and Al-Karkh / 1 /, and two research tools were built: 1- A test of deep understanding skills, consisting of (20) test items and a scale for two skills. 2- The second test consists of (24) test items distributed among (18) essay items and (6) objective items. The psychometric properties of validity, stability, discriminatory strength, and effectiveness of alternatives were verified for the two tests fo

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