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Exploring Important Factors in Predicting Heart Disease Based on Ensemble- Extra Feature Selection Approach
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Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficacy of several classification algorithms on four reputable datasets, using both the full features set and the reduced features subset selected through the proposed method. The results show that the feature selection technique achieves outstanding classification accuracy, precision, and recall, with an impressive 97% accuracy when used with the Extra Tree classifier algorithm. The research reveals the promising potential of the feature selection method for improving classifier accuracy by focusing on the most informative features and simultaneously decreasing computational burden.

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Publication Date
Wed Dec 01 2010
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Equilibrium Mixing Index and Optimum Mixing Time for Three solid materials in Fluidized Column
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     Equilibrium and rate of mixing of free flowing solid materials are found using gas fluidized bed. The solid materials were sand (size 0.7 mm), sugar (size0.7 mm) and 15% cast iron used as a tracer. The fluidizing gas was air with velocity ranged from 0.45-0.65 m/s while the mixing time was up to 10 minutes. The mixing index for each experiment was calculated by averaging the results of 10 samples taken from different radial and axial positions in fluidized QVF column 150 mm ID and 900 mm height.

     The experimental results were used in solving a mathematical model of mixing rate and mixing index at an equilibrium proposed by Rose. The results show that mixing index increases with inc

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Publication Date
Tue Mar 01 2022
Journal Name
Eurasian Journalof History, Geography And Economics
Effect of Oil Prices Fluctuations on Agricultural Gross Domestic Product in Iraq: Autoregressive Distributed Lag (ARDL) Approach
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The research dealt with the analysis of the relations between the GDP of the agricultural sector in Iraq, oil prices, the exchange rate and the GDP both on the short term and long term. The research adopted data analysis for the period from 1980-2019 using the ARDL model. the results indicate the existence of long-term relationships between oil prices and the prices of each agricultural commodity at a significance level of 5%. Also, oil prices have a negative consequence on agricultural production in Iraq, and the Iraqi economy is a rentier economy that depends mainly on oil as a source of income and budget financing.

Publication Date
Mon Sep 23 2019
Journal Name
Baghdad Science Journal
A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network
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Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

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Publication Date
Thu Apr 04 2024
Journal Name
Journal Of Electrical Systems
AI-Driven Prediction of Average Per Capita GDP: Exploring Linear and Nonlinear Statistical Techniques
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Average per capita GDP income is an important economic indicator. Economists use this term to determine the amount of progress or decline in the country's economy. It is also used to determine the order of countries and compare them with each other. Average per capita GDP income was first studied using the Time Series (Box Jenkins method), and the second is linear and non-linear regression; these methods are the most important and most commonly used statistical methods for forecasting because they are flexible and accurate in practice. The comparison is made to determine the best method between the two methods mentioned above using specific statistical criteria. The research found that the best approach is to build a model for predi

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Research In Social Sciences & Humanities
HRM Management Approaches, With a Focus on Employee Centric Approach: A Case Study In The Rafidain Bank In Iraq
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This research attempts to shed light on a topic that is considered one of the most important topics of HRMs management, which is the Employee centric approach by examining its philosophy and understanding . To achieve the goal, the research relied on the philosophical analytical method, which is one of the approaches used in theoretical studies. The research reached a set of conclusions, the most important of which are the theoretical studies that addressed this entry in the English language and the lack of it in the Arabic language, according to the researcher's knowledge. The research reached a set of recommendations, the most important of which was that this approach needs more research, analysis and study at the practical and th

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Publication Date
Mon Jan 01 2018
Journal Name
Complexity
Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease
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Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demon

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Publication Date
Fri Dec 01 2023
Journal Name
Civil And Environmental Engineering
Influence of Different Factors on Permanent Deformation of Hot Asphalt Concrete Mixtures
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The performance of flexible pavements is significantly impacted by the permanent deformation (rutting) of asphalt pavements. Rutting shortens the pavement's useful service life and poses significant risks to those using the highway since it alters vehicle handling characteristics.. The aim of this research is to evaluate the permanent deformation of asphalt mixtures under different conditions,to achieve this aim 108 cylindrical specimens has been prepared and tested under repeated loading in uniaxial compression mode. Five factors were considered in this research, these factors represent the effect of environmental condition and traffic loading as well as mixture properties, they include testing temperature, loading condition (stress level

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Publication Date
Sat Aug 01 2015
Journal Name
2015 37th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Influence of multiple dynamic factors on the performance of myoelectric pattern recognition
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Publication Date
Sun Sep 07 2014
Journal Name
Baghdad Science Journal
Factors Influence on the yield of Bacterial Cellulose of Kombucha (Khubdat Humza)
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Kombucha(Khubdat Humza) is composed of yeast and acetic acid bacteria especially, Acetobacter xylinum which forms a cellulose pellicle on tea broth. Kombucha(Khubdat Humza) produces bacterial cellulose pellicles, with unique purity and fine structure. It can be used in many forms, such as an emulsifier, stabilizer, dispersing agent, thickener and gelling agent but these are generally subsidiary to its most important use of holding on to water. Recently, bacterial cellulose is used in many special applications such as a scaffold for tissue engineering of cartilages and blood vessels, also for artificial skin for temporary covering of wounds, as well as its used in the clothing industry. The yield of cellulose produced were investiga

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Publication Date
Tue Aug 23 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Face mask detection based on algorithm YOLOv5s
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Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

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