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jih-544
Identify The Optimal Values of the Geometric Deformable Models Parameters to Segment Multiple objects in Digital Images
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 Accuracy in multiple objects segmentation using geometric deformable models sometimes is not achieved for reasons relating to a number of parameters. In this research, we will study the effect of changing the parameters values on the work of the geometric deformable model and define their efficient values, as well as finding out the relations that link these parameters with each other, by depending on different case studies including multiple objects different in spacing, colors, and illumination. For specific ranges of parameters values the segmentation results are found good, where the success of the work of geometric deformable models has been limited within certain limits to the values of these parameters.

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
USE OF MODIFIED MAXIMUM LIKELIHOOD METHOD TO ESTIMATE PARAMETERS OF THE MULTIPLE LINEAR REGRESSION MODEL
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Scopus
Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Engineering
Impact of Induction Motor Faults on the Basic Parameters' Values
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Unlike fault diagnosis approaches based on the direct analysis of current and voltage signals, this paper proposes a diagnosis of induction motor faults through monitoring the variations in motor's parameters when it is subjected to an open circuit or short circuit faults. These parameters include stator and rotor resistances, self-inductances, and mutual inductance. The genetic algorithm and the trust-region method are used for the estimation process. Simulation results confirm the efficiency of both the genetic algorithm and the trust-region method in estimating the motor parameters; however, better performance in terms of estimation time is obtained when the trust-region method is adopted. The results also show the po

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Publication Date
Sun Dec 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Use Generalized Pareto Survival Models to Estimation Optimal Survival Time for Myocardial Infarction Patients
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The survival analysis is one of the modern methods of analysis that is based on the fact that the dependent variable represents time until the event concerned in the study. There are many survival models that deal with the impact of explanatory factors on the likelihood of survival, including the models proposed by the world, David Cox, one of the most important and common models of survival, where it consists of two functions, one of which is a parametric function that does not depend on the survival time and the other a nonparametric function that depends on times of survival, which the Cox model is defined as a semi parametric model, The set of parametric models that depend on the time-to-event distribution parameters such as

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Publication Date
Mon Nov 09 2020
Journal Name
Construction Research Congress 2020
Alternative Risk Models for Optimal Investment in Portfolio-Based Community Solar
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Publication Date
Tue Apr 02 2024
Journal Name
Advances In Systems Science And Applications
A New Face Swap Detection Technique for Digital Images
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Scopus
Publication Date
Tue Aug 01 2017
Journal Name
Journal Of Engineering
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

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Publication Date
Mon Jul 31 2017
Journal Name
Journal Of Engineering
Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq
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Publication Date
Thu Jan 04 2018
Journal Name
International Journal Of Molecular Sciences
Understanding the Progression of Bone Metastases to Identify Novel Therapeutic Targets
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Publication Date
Fri Sep 15 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Validity of Digital and Rapid Prototyped Orthodontic Study Models
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Background: The integration of modern computer-aided design and manufacturing technologies in diagnosis, treatment planning, and appliance construction is changing the way in which orthodontic treatment is provided to patients. The aim of this study is to assess the validity of digital and rapid prototyped orthodontic study models as compared to their original stone models. Materials and methods: The sample of the study consisted of 30 study models with well-aligned, Angle Class I malocclusion. The models were digitized with desktop scanner to create digital models. Digital files were then converted to plastic physical casts using prototyping machine, which utilizes the fused deposition modeling technology. Polylactic acid polymer was chose

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks
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It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

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