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Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
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Abstract<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.</p>
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Publication Date
Sun Mar 07 2010
Journal Name
Baghdad Science Journal
Studying the ability of transformation of lymphocytes by using PHA in patient with Cytomegalovirus
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Blood samples were collected from (31) pregnant women infected with cytomegalovirus , also (15) blood samples were collected from congenitally infected infants, and (20),(15),(15) blood samples were collected from pregnant women ,non pregnant and infants, respectively, all of them were as control groups. CMV infection identified by using ELISA assay to detect specific CMV IgM & IgG in sera. The results of lymphocyte transformation showed a significant decrease when phytoheamagglutinin (PHA) used as stimulator, lymphocytes response in infected pregnant women was lower than that of non infected pregnant women as well as non pregnant women. Result also showed a significant decrease in the ability of lymphocyte division in healthy pregnant w

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Publication Date
Wed Apr 19 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Calculation of the Radionuclide Concentrations in Samples of Condiments by Using Gamma Spectroscopy System
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 The aim of this work  is to evaluate concentrations of  natural and artificial radionuclide  in nine different samples of condiments from local markets.     The concentrations of 238 U , 232 Th ,40k  and 137Cs  were measured by using gamma spectroscopy with a high- purity germanium detector. The concentrations of 238 U, 232 Th ,40k  and 137Cs   were found to be in a range of  (21.4 - 91.13), (15.7 - 88.11) , (285.56 – 1100) and (5.1 - 27.5) Bq.kg-1  respectively. These concentrations are not hazardous to public health and the activities are within the allowed  levels

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Publication Date
Mon Dec 10 2018
Journal Name
Indian Journal Of Natural Sciences
Verification the Pollution of Tigris River in South of Baghdad using Remote Sensing Techniques
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This paper describes the use of remote sensing techniques in verification of the polluted area in Diyala River and Tigris River and the effected of AL-Rustamiyah wastewater treatment plant, which is located on Diyala River, one of the branches of Tigris River in south of Baghdad. SPOT-5 a French satellite image of Baghdad, Iraq was used with ground resolution of 2.5 m in May 2016. ENVI 5.1 software programming was utilized for Image processing to assess the water pollution of Diyala and Tigris River’s water. Five regions were selected from a study area and then classified using the unsupervised ISODATA method. The results indicated that four classes of water quality which are successful in assessing and mapping water pollution which confi

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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
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Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

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Publication Date
Sun May 02 2021
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Rationalization of costs by using the tool of the quality function deployment: / applied research in the State Company for Textile and Leather Industries
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The research aims to apply one of the techniques of management accounting, which is the technique of the quality function deployment on the men's leather shoe product Model (79043) in the General Company for Textile and Leather Industries by determining the basic requirements of the customer and then designing the characteristics and specifications of the product according to the preferences of the customer in order to respond to the customer's voice in agreement With the characteristics and technical characteristics of the product, taking into account the products of the competing companies to achieve the maximum customer satisfaction, the highest quality and the lowest costs. Hence, the importance of research has emerged, which indicat

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Publication Date
Sat Jan 01 2011
Journal Name
Journal: Ibn Al-haitham Journal For Pure And Applied Sciences
A Study the effect the direction of the distribution of lighting to improve Images in different lighting by using technique adaptive histogram equalization
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Publication Date
Thu Sep 01 2016
Journal Name
2016 8th Computer Science And Electronic Engineering (ceec)
Class-specific pre-trained sparse autoencoders for learning effective features for document classification
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Publication Date
Tue Sep 10 2019
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
A classification model on tumor cancer disease based mutual information and firefly algorithm
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Publication Date
Sat Jul 06 2024
Journal Name
Multimedia Tools And Applications
Text classification based on optimization feature selection methods: a review and future directions
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A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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