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Rigid trunk sewer deterioration prediction models using multiple discriminant and neural network models in Baghdad city, Iraq
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The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the multiple discriminant model (MDM) and neural network model (NNM). Zublin trunk sewer in Baghdad city was selected as a case study. The deterioration model based on the NNDM provide the highest overall prediction efficiency which could be attributed to its inherent ability to model complex processes. The MDDM provided relatively low overall prediction efficiency, this may be due to the restrictive assumptions by this model. For the NNDM the confusion matrix gave overall prediction efficiency about 87.3% for model training and 70% for model validation, and the overall conclusion from these models may predict that Zublin trunk sewer is of a poor condition.

Publication Date
Tue Feb 01 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Finger Vein Recognition Based on PCA and Fusion Convolutional Neural Network
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Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

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Publication Date
Tue Aug 01 2023
Journal Name
Journal Of Engineering
An Extensive Literature Review on Risk Assessment Models (Techniques and Methodology) for Construction Industry
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This study looks into the many methods that are used in the risk assessment procedure that is used in the construction industry nowadays. As a result of the slow adoption of novel assessment methods, professionals frequently resort to strategies that have previously been validated as being successful. When it comes to risk assessment, having a precise analytical tool that uses the cost of risk as a measurement and draws on the knowledge of professionals could potentially assist bridge the gap between theory and practice. This step will examine relevant literature, sort articles according to their published year, and identify domains and qualities. Consequently, the most significant findings have been presented in a manne

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Publication Date
Wed Oct 01 2014
Journal Name
Engineering And Technology Journal
Investigating Forward kinematic Analysis of a 5-axes Robotic Manipulator using Denavit-Hartenberg Method and Artificial Neural Network
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Publication Date
Tue Jan 01 2019
Journal Name
Advances In Computational Intelligence And Robotics
Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images
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Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

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Publication Date
Tue Dec 31 2024
Journal Name
Iraqi Geological Journal
Geomechanical Modeling and Artificial Neural Network Technique for Predicting Breakout Failure in Nasiriyah Oilfield
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Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It

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Publication Date
Wed Aug 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
A Study on Transportation Models in Their Minimum and Maximum Values with Applications of Real Data
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The purpose of this paper is to apply different transportation models in their minimum and maximum values by finding starting basic feasible solution and finding the optimal solution. The requirements of transportation models were presented with one of their applications in the case of minimizing the objective function, which was conducted by the researcher as real data, which took place one month in 2015, in one of the poultry farms for the production of eggs

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Publication Date
Mon Jun 30 2025
Journal Name
Infotech Spectrum: Iraqi Journal Of Data Science
Evaluating AI Language Models in News Retrieval: A Comparative Study Of ChatGPT-Plus and DeepSeek (R1)
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The increasing complexity of how humans interact with and process information has demonstrated significant advancements in Natural Language Processing (NLP), transitioning from task-specific architectures to generalized frameworks applicable across multiple tasks. Despite their success, challenges persist in specialized domains such as translation, where instruction tuning may prioritize fluency over accuracy. Against this backdrop, the present study conducts a comparative evaluation of ChatGPT-Plus and DeepSeek (R1) on a high-fidelity bilingual retrieval-and-translation task. A single standardize prompt directs each model to access the Arabic-language news section of the College of Medicine, University of Baghdad, retrieve the three most r

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Publication Date
Tue Jan 01 2019
Journal Name
Spe Europec Featured At 81st Eage Conference And Exhibition
Development of Artificial Neural Networks and Multiple Regression Analysis for Estimating of Formation Permeability
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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Global Pharma Technology
Assessment of heavy metals in some ground water wells at Baghdad City/Iraq
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Occurrence the heavy metals in water is one of the most important concerns. may cause savior health problems. In this work we made an attempt to know the quantity of six heavy metals in groundwater in different locations of Baghdad city. Examinations were made on groundwater of the review region to assess the heavy metals. Groundwater samples were gathered and analyzed utilizing Atomic Absorption Spectrophotometer for their Manganese, Iron, Zinc, Cadmium, Copper and Lead content and their levels compared with World Health Organization (WHO) specified maximum contaminant level. In order to accomplish this, water samples were obtained from 10 randomly selected wells in the region, in February and August, 2016. The study showed that the ground

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Publication Date
Tue Mar 27 2012
Journal Name
Bmc Blood Disorders
Molecular characterization of glucose-6-phosphate dehydrogenase deficient variants in Baghdad city - Iraq
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Abstract<sec><title>Background

Although G6PD deficiency is the most common genetically determined blood disorder among Iraqis, its molecular basis has only recently been studied among the Kurds in North Iraq, while studies focusing on Arabs in other parts of Iraq are still absent.

Methods

A total of 1810 apparently healthy adult male blood donors were randomly recruited from the national blood transfusion center in Baghdad. They were classified into G6PD deficient and non-deficient individuals based on the results of methemoglobin reduction test (MHRT), with confirmation of deficiency by subsequent enzyme assays. DNA from defi

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