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Robust Estimations for power Spectrum in ARMA(1,1) Model Simulation Study
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Simulation Study

 

Abstract :

Robust statistics Known as, Resistance to mistakes resulting of the deviation of Check hypotheses of statistical properties ( Adjacent Unbiased  , The Efficiency of data taken from a wide range of probability distributions follow a normal distribution or a mixture of other distributions with different standard deviations.

 power spectrum function lead to, President role in the analysis of Stationary random processes, organized according to time, may be discrete random variables or continuous. Measuring  its total capacity as frequency function.

Estimation methods Share with the concept of nonparametric in the absence of a model with clearly defined parameters (Free distribution) part with the distributed according to the Normal distribution, while the other part is unknown distribution, and thus it became the distribution of tainted its parameters is  unknown, so it can be considered the Robust Methods is the highest level in grades nonparametric methods which is will be based on the conversion calculable test to a Standard formula Conducted by the convergence operations.

The aim of the Search finding the best estimator of Power spectrum With the mixed ARMA (1,1) model for time series follow a Normal distribution. By Using Simulation experiments, on samples [n=50,100,150,200,250],and Different virtual values for و θ . It has been Showen  in tables 1,2,3 The Obvious difference between all the initial default values and generated values , Which may give results far from the real system results.

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Publication Date
Tue Dec 21 2021
Journal Name
Mendel
Hybrid Deep Learning Model for Singing Voice Separation
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

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Publication Date
Fri Apr 30 2021
Journal Name
Iraqi Geological Journal
Geological Model for Mauddud Reservoir Khabaz Oil Field
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The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there

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Publication Date
Tue Jan 01 2019
Journal Name
Cpwr
Development of a workforce sustainability model for construction
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Publication Date
Sat Dec 17 2022
Journal Name
Applied Sciences
A Hybrid Artificial Intelligence Model for Detecting Keratoconus
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

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Publication Date
Sun Dec 07 2008
Journal Name
Baghdad Science Journal
Optimal Color Model for Information Hidingin Color Images
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In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.

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Publication Date
Thu Jan 01 2026
Journal Name
Aip Conference Proceedings
Bayesian methodology for spatial quantile autoregressive model estimation
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Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data

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Publication Date
Mon Jun 08 2026
Journal Name
Al Kut Journal Of Economics And Administrative Sciences
Use of the Bootstrap in the logistic regression model for Breast cancer disease
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The logistic regression model is one of the oldest and most common of the regression models, and it is known as one of the statistical methods used to describe and estimate the relationship between a dependent random variable and explanatory random variables. Several methods are used to estimate this model, including the bootstrap method, which is one of the estimation methods that depend on the principle of sampling with return, and is represented by a sample reshaping that includes (n) of the elements drawn by randomly returning from (N) from the original data, It is a computational method used to determine the measure of accuracy to estimate the statistics, and for this reason, this method was used to find more accurate estimates. The ma

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Publication Date
Sat Jul 05 2025
Journal Name
Journal Of Machine And Computing
Cyber Neutrosophic Model for Secure and Uncertainty Aware Evaluation in Indoor Design Projects
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To perform a secure evaluation of Indoor Design data, the research introduces a Cyber-Neutrosophic Model, which utilizes AES-256 encryption, Role-Based Access Control, and real-time anomaly detection. It measures the percentage of unpredictability, insecurity, and variance present within model features. Also, it provides reliable data security. Similar features have been identified between the final results of the study, corresponding to the Cyber-Neutrosophic Model analysis, and the cybersecurity layer helped mitigate attacks. It is worth noting that Anomaly Detection successfully achieved response times of less than 2.5 seconds, demonstrating that the model can maintain its integrity while providing privacy. Using neutrosophic sim

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Publication Date
Thu Nov 21 2013
Journal Name
المؤتمر العلمي الدولي الرابع لاتحاد الاحصائيين العرب / بغداد
Estimating Fertility Rates in Iraq by using (Lee-Carter) Model And Forecasting for the Period (2012_2031)
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A large number of researchers had attempted to identify the pattern of the functional relationship between fertility from a side and economic and social characteristics of the population from another, with the strength of effect of each. So, this research aims to monitor and analyze changes in the level of fertility temporally and spatially in recent decades, in addition to estimating fertility levels in Iraq for the period (1977-2011) and then make forecasting to the level of fertility in Iraq at the national level (except for the Kurdistan region), and for the period of (2012-2031). To achieve this goal has been the use of the Lee-Carter model to estimate fertility rates and predictable as well. As this is the form often has been familiar

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Publication Date
Tue Dec 06 2011
Journal Name
Journal Of Planner And Development
The strategies of sustainable urban management in the Islamic city according to binary ( the city and power)
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Urban planning task and the control of constructional planning and urban management for cities considers the main tasks that the government takes care off. The paper discusses the concept of power from historical view of ancient Islamic cities to discover the strategies of urban management that the Islamic city adopt and to employ it in contemporary cities ,the more problems that modern cities suffer from which appears through poorness of urban context ,belongs to the loss of balance exists between the different faces of power that takes the tasks of urban management .therefore enhancement of urban environment is being through re-back that balance in administrative structures that make the cities go ahead . So the attention has b

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