Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

Abstract: The issue of rebuilding the strategiccapacity of Iraq after the occupation and the US invasion in 2003 has been oneof the most contentious issues of debate and debate. It raises a number ofpolitical, social, economic, military and security problems. We say politicalprimarily because the climate and political conditions play a large and activerole along with other factors complementary to them. The important questionthat arises here is: How can the ruling political blocs move to the stage ofreconstruction without reservations? In order to answer this question, wedivided the study into three main axes. The first deals with the concept ofpower and its types. The second axis deals with the relationship betweenstrategic capa

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo

     A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets

      This study shows the effects of copper material electrode, applied voltage, and different pressure values on electrical discharge plasma. The purpose of the work is the application of the spectral analysis method to obtain accurate results of nitrogen plasma parameters. By using the optical emission spectroscopy (OES), many N₂ molecular spectra peaks appeared in the range from 300 to 480 nm. Also, some additional peaks were recorded, corresponding to atomic and ionic lines for nitrogen, target material, and hydrogen, in all samples. The electron density (n_e) was calculated from the measurement of Stark broadening effect, which was found to decrease with increasing pressure from 0.1 mba

    The present study aimed to the isolation and identification of Penicillium chrysogenum from subclinical bovine mastitis as well as the evaluation of their potential to produce the main virulence factors by assessing proteinase production, urease production, growth rate at 37 ̊C, and hemolytic activity on Blood agar. One hundred milk samples were assembled from the White Gold village and surrounded outlying farms of Abu-Ghraib, Baghdad province, during the period from November 2018 to March 2019. Each milk sample was tested for California Mastitis (CMT). The results indicated that 85% of the samples gave positive (+ve) results for CMT. Sixty six mycotic isolates were detected, including 31 isolates of Peni

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa