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comparison between the methods estimate nonparametric and semiparametric transfer function model in time series the Using simulation
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 The transfer function model the basic concepts in the time series. This model is used in the case of multivariate time series. As for the design of this model, it depends on the available data in the time series and other information in the series so when the representation of the transfer function model depends on the representation of the data In this research, the transfer function has been estimated using the style nonparametric represented in two method  local linear regression and cubic smoothing spline method The method of semi-parametric represented use semiparametric single index model, With four proposals, , That the goal of this research is comparing the capabilities of the above mentioned method using simulation at sample sizes (n = 100,150,200) as it found that the estimated proposed( C.S.S-L.S.I) is the best among the studied capabilities.

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Publication Date
Fri Feb 04 2022
Journal Name
Iraqi Journal Of Science
Improving Security of ID Card and Passport Using Cubic Spline Curve
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In this paper, the proposes secure system to improving security of ID card and passports is by generating cubic spline co-occurrence code (CCO code) for each ID card. The authentication part, begins passing ID card through the checkpoint then the checkpoint will check the information of card or passport by also extracting features in order to generate the cubic spline co-occurrence code (CCO code), finally comparison is made between extracted CCO code at the checkpoint and CCO code that has been printed on ID card or passport (type of fraud like change personal picture or fraud it’s information). Several tests were conducted to evaluate the performance of the proposed security system. Furthermore, the experiment results reveal that the

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Publication Date
Thu Feb 07 2019
Journal Name
Iraqi Journal Of Laser
Tissue Welding Using (800±10nm) Diode and (1064nm) Nd: YAG Lasers
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Joining tissue is a growing problem in surgery with the advancement of the technology and more precise and difficult surgeries are done. Tissue welding using laser is a promising technique that might help in more advancement of the surgical practice. Objectives: To study the ability of laser in joining tissues and the optimum parameters for yielding good welding of tissues. Methods: An in-vitro study, done at the Institute of Laser, Baghdad University during the period from October 2008 to February 2009. Diode and Nd-YAG lasers were applied, using different sessions, on sheep small intestine with or without solder to obtain welding of a 2-mm length full thickness incision. Different powers and energies were used to get maximum effect. Re

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Publication Date
Sat Dec 01 2018
Journal Name
Fuel
Biodiesel from batch and continuous oleic acid esterification using zeolite catalysts
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Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences
Estimation of return stock rate by using wavelet and kernel smoothers
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This article aim to estimate the Return Stock Rate of the private banking sector, with two banks, by adopting a Partial Linear Model based on the Arbitrage Pricing Model (APT) theory, using Wavelet and Kernel Smoothers. The results have proved that the wavelet method is the best. Also, the results of the market portfolio impact and inflation rate have proved an adversely effectiveness on the rate of return, and direct impact of the money supply.

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Publication Date
Wed Apr 19 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Characterization of CdO film AFM and XRD Diffraction Using Rietveld Refinement
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Nano particles of Cadmium Oxide (CdO) thin films were prepared by spray pyrolysis technique. The synthesized film is annealed at (200 , 300, 450) o C for 3 hours .  The XRD and AFM for the analysis of its structural and micro-structural characteristic has been preformed. The average grain size was found to be about 32.50 nm .There is a preferred orientation along (200)   plane  with texture coefficient 1.79, 1.644, 1.763 and 1.792  for deposited and annealed films, corresponding to grain size 57,58 ,51 and 51 nm. The variations of stress with temperature is ranged from 0.157 - 0.376 GPa .
 

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers
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      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

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Publication Date
Mon Dec 14 2020
Journal Name
2020 13th International Conference On Developments In Esystems Engineering (dese)
Anomaly Based Intrusion Detection System Using Hierarchical Classification and Clustering Techniques
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With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers
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      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
isolation and identification of cloves oil from eugenia caryophyllaa using Ultrasonic
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This research has been prepared to isolate and diagnose one of the most important vegetable oils from the plant medical clove is the famous with Alaeugenol oil and used in many pharmaceuticals were the isolation process using a technique ultrasonic extraction and distillation technology simple

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Publication Date
Fri Mar 12 2021
Journal Name
Sensors
A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments
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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

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