Volcaniclastic rocks of Al Muqdadiya Formation (Pliocene) in Injana area, southern Hemrin anticline, NE of Iraq, were studied ( petrographically, physically, mineralogically and geochemically , as well as the engineering properties) to assess the suitability of volcaniclastic rocks to use them in industry as refractories. The results show that the physical and engineering properties change with the temperature change. The bulk density and the specific gravity increase by increasing temperature while the apparent porosity, water sorption and the linear shrinkage decrease. On the other hand the compressive strength increase by increasing temperature. The volcaniclastics have very low thermal conductivi

Baluti Formation of the Rhaetian (Late Triassic) age is composed mainly of dolomite, the unit formed with dolomitic limestone, dolomitic breccias and limestone begins with gray or dark gray colored and sugar textured dolomitic limestones including micrite with shale horizons. Baluti Formation was deposited in carbonate platform, and slumped to deeper margins forming carbonate debrites and breccias of various types.
Petrographic examination of the dolomites reveals various crystal habits and textures of the dolomites. Planktonic bivalve, calcisphere and echinoid spicules were found in the Baluti Formation settled in deep-margin carbonate environment. Nine dolomite-rock textures were identified and classified according to the crystal-si

Volcaniclastic rocks of Al Muqdadiya Formation (Pliocene) in Injana area, southern Hemrin anticline, NE of Iraq, were studied ( petrographically, physically, mineralogically and geochemically , as well as the engineering properties) to assess the suitability of volcaniclastic rocks to use them in industry as refractories. The results show that the physical and engineering properties change with the temperature change. The bulk density and the specific gravity increase by increasing temperature while the apparent porosity, water sorption and the linear shrinkage decrease. On the other hand the compressive strength increase by increasing temperature. The volcaniclastics have very low thermal conductivity.
The petrography, mineralogy and

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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

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

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

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