BS.c

Oct 1, 1998 - Jul 1, 2002

I hold a bachelor's degree in statistics from the University of Mosul, College of Computer Science and Mathematics.

MS.c

Oct 1, 2002 - May 30, 2005

I hold a master's degree in statistics from the University of Mosul, College of Computer Science and Mathematics.

مسؤول وحدة المجانية في امعهد التقني نينوى [مسؤول التعليم المجاني]

Jan 8, 2006 - Jun 28, 2007

مسؤول شعبة الافراد في المعهد التقني نينوى [مسؤول شعبة الافراد]

Oct 28, 2008 - Feb 13, 2010

Construct A model to Identify Teachers' Assessments Through a Set of Variables Using Artificial Neural networks

Apr 1, 2019

المجلة Tikrit Journal of Administration and Economics Sciences

publisher Tikrit University

العدد 48

المجلد 15

Through this research we will built a model via using Artificial Neural Network in which the teaching groups are categorized by a set of variables. To reach a model that helps us in the future to put the academic staff at the right level and compare with the evaluation obtained through a set of criteria used in the performance evaluation academic in Iraqi universities consisting of 16 variables (standard). The classification is divided into two stages: the first classification function and the second hidden function to reach the outputs. The stages of the assessment were divided into four groups to make a multiple classification.

Use the k nearest neighbor (KNN) to compare the classification of real age and age through the bone for thalassic patients.

Jan 12, 2021

المجلة المجلة العراقية للعلوم الاحصائية (Dec 2020)

publisher College of Computer Science and Mathematics, University of Mosul

DOI https://doi.org/10.33899/iqjoss.2020.167392

العدد 2

المجلد 17

Thalassemia is considered a chronic disease, especially children from the first years of life, and the patient goes through stages over long periods, Data were collected for patients by real age and age through the bone, Therefore, a comparison will be made between the two cases. There are many statistical methods used to arrive at a classification of data, the method of nearest neighbor has been relied upon as a method of classification between societies. The method of classifying each observation depends on the three closest values ​​on the basis of which the observation is placed into the correct group, the naturalness of the data was rather close, so it asked us to use a method that helps us to reach a better classification. The k the nearest neighbor is the best way to reach an optimal classification for such data. Classification by real age was better than classification by bone age using classification. Classification by actual age was better than classification by bone age using k nearest neighbor classification

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Comparison Between Quadratic And Logistic Discrimination Function To Get On The Best Classification With Application

Sep 7, 2021

المجلة Turkish Journal of Computer and Mathematics Education

publisher Karadeniz Technical University

العدد 7

المجلد Vol.12

: The purpose of this research is to compare between quadratic and logistic discrimination function, also to identify the best method in discrimination which helps us to classify the data correctly. A several stages are starting from stratified sampling; classification errors and error rate if the pre-probability is equal and unequal by containing a few errors. Our choice of the correct path in analyzing the data helped us to identify the reasons that increase the errors in classification. In this study, we used some statistical measurements in terms of, the stratified sample, classification errors and the percentage of errors. The percentage of errors in the pre-probability is equal and unequal when the accumulation of errors in the square discrimination function was less than the accumulation of errors in the logistic discrimination function when the probability is equal, but when the probability is not equal, the results of the square function were much better than the square discrimination function. However, the accumulation of errors in the quadratic function is less than the accumulation of errors in the logistic function

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