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

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

Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

KE Sharquie, AA Noaimi, ER Shwail, J Clin Exp Dermatol Res, 2012 - Cited by 41

Esterification considers the most important reaction in biodiesel production. In this study, oleic acid was used as a suggested feedstock in order to study and simulate production of biodiesel. The batch esterification reaction of oleic acid was carried out at various operating conditions; temperature from 40 to 70 °C, ethanol to oleic acid molar ratio from 3/1 and 6/1 and a reaction time up to 180 min.
The catalyst used was prepared NaY zeolite, which is added to the reaction mixture as 2, 5 and 10 wt.% of oleic acid.
The results show that the optimum conditions, gives 0.81 conversion of oleic acid, were 6/1 molar ratio of ethanol/oleic acid, 5 wt.% NaY relative to initial oleic acid, 70°C and 60 minutes. The activation energy o

Thermal and catalytic pyrolysis of waste plastics in an inert atmosphere has been regarded as a creative method, since pyrolysis can convert plastics waste into hydrocarbons that can be used either as fuels or as a source of chemicals.

Natural Iraqi kaolin clay was used to synthesis the NaX nano- zeolite by hydrothermal conditions with average particle size equal to 77.63nm.Thermal decomposition kinetics of high-density polyethylene (HDPE) in the absence and presence of catalysts nano NaX Zeolite was investigated. Thermal and catalytic degradation of HDPE was performed using a thermogravimetric analyzer in nitrogen atmosphere under non-isothermal conditions 4, 7 and 10 °C/min heating rates were employed in thermogravimetric anal

The present work reports a direct experimental comparison of the catalytic hydrodesulfurization of
thiophene over Co-Mo/Al2O3 in fixed- and fluidized-bed reactors under the same conditions. An
experimental pilot plant scale was constructed in the laboratories of chemical engineering department,
Baghdad University; fixed-bed unit (2.54 cm diameter, and 60cm length) and fluidized-bed unit (diameter of 2.54 cm and 40 cm long with a separation zone of 30 cm long and 12.7 cm diameter). The affecting
variables studied in the two systems were reaction temperature of (308 – 460) oC, Liquid hourly space
velocity of (2 – 5) hr-1, and catalyst particle size of (0.075-0.5) mm. It was found in both operations that the
conversion