Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

     In this model, we use the C++ programming language to develop a program that calculates the atmospheric earth model from the surface to 250 kilometers. The balance forces theory is used to derive the pressure equation. The hydrostatic equation is utilized to calculate these parameters analytically. Variations of the parameters with altitude (density, pressure, temperature, and molecular weight) are investigated intensively. The equations for gravitational acceleration, sound speed, and scale height are also obtained. This model is used to investigate the effects of the earth's atmosphere on the space shuttle and the moving bodies inside it.

A session is a period of time linked to a user, which is initiated when he/she arrives at a web application and it ends when his/her browser is closed or after a certain time of inactivity. Attackers can hijack a user's session by exploiting session management vulnerabilities by means of session fixation and cross-site request forgery attacks.
Very often, session IDs are not only identification tokens, but also authenticators. This means that upon login, users are authenticated based on their credentials (e.g., usernames/passwords or digital certificates) and issued session IDs that will effectively serve as temporary static passwords for accessing their sessions. This makes session IDs a very appealing target for attackers. In many c

     Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this arti

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly

The Dammam Formation ( Middle-Late Eocene) has a total thickness 150 m , 143 m and 89.7 m at the studied wells (B.H.8, B.H.3 and B.H.1) respectively. They are located in Al- Najaf Governorate, 35 Km southwest of Al-Najaf city. The petrographic components of the Dammam limestone comprises skeletal grainsforaminifera (both benthic and planktonic), echinoderm plates and Mollusca, shell fragments, Bryozoan, Bioclasts are common, non-skeletal grainsare pellets, lithoclast (carbonate and non carbonate), and groundmass (micrite and sparry calcite). In term of mineralogy, the X-Ray analysis shows the presence of non clay minerals is calcite, dolomite as the main minerals and quartz and scattered evaporate, whereas clay minerals as secondary mine

Urinary Tract Infection is an infection that caused by the members of the genus
Proteus that depends mainly on the availability of virulence factors ;Various
virulence factors including biofilm, swarming migration , polysaccharide
,heamolysin,protease, DNase, urease production weredetermined for 45Proteus
isolates that obtained from clinical specimens of Urinry Tract Infection patient .
The distribution of virulence factors was showed variation among the testedisolates
and strain specific in most cases. All Proteus isolates showed 45 (100%)biofilm ,
polysaccharide andSwarming capabilities with different extents. High
ureaseproduction was demonstrated in most isolates 40 (88.8%);In addition, they
were abling to