In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

The Arabic grammatical theory is characterized by the characteristics that distinguish it from other languages. It is based on the following equation: In its entirety a homogeneous linguistic system that blends with the social nature of the Arab, his beliefs, and his culture.
    This means that this theory was born naturally, after the labor of maintaining an integrated inheritance, starting with its legal text (the Koran), and ends with its features of multiple attributes.
Saber was carrying the founding crucible of that theory, which takes over from his teacher, Hebron, to be built on what it has reached. It is redundant to point to his location and the status of his book.
So came to my research tagged: (c

This research dealt with study of cladistics taxonomy of  five  species related to the genus Rumex L. and Polygonum L. from family polygonaceae in Iraq by using Mesquite software V.2.75. This  research support strongly delimiting  the species P. aviculare L. and P. lapathifolia L.as suggested in floras publication while R. dentatus L. is setted in single group whereas R. vesicarius L. and R. conglomeratus Murray were included in the same group. Also, this study involved characteristics of shape, dimensions, color, and ornamentation of seeds and fruits as  the seed forms were ranging from lenticular to trigonous. In terms of size calculations,  the seeds of R. vesicarius  was recorded the higher range (4.0- 4.5) mm in length w

Purpose: to demonstrate the possibility of moving to electronic data exchange dimensions (regulatory requirements, technical requirements, human requirements, senior management support) to simplify the work procedures dimensions (modern procedures, clarity of procedures, short procedures, availability of information and means required. The simplicity of the models used because of its importance to keep abreast of recent developments in the service of municipal works through the application of electronic data interchange, which simplifies procedures and out of the routine in the performance of the work of municipal departments has developed. It was applied to Municipality (Hilla) so that the transformation

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c