The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

     The worldwide pandemic Coronavirus (Covid-19) is a new viral disease that spreads mostly through nasal discharge and saliva from the lips while coughing or sneezing. This highly infectious disease spreads quickly and can overwhelm healthcare systems if not controlled. However, the employment of machine learning algorithms to monitor analytical data has a substantial influence on the speed of decision-making in some government entities.        ML algorithms trained on labeled patients’ symptoms cannot discriminate between diverse types of diseases such as COVID-19. Cough, fever, headache, sore throat, and shortness of breath were common symptoms of many bacterial and viral diseases.

This research focused on the nu

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-co

Extracting moving object from video sequence is one of the most important steps
in the video-based analysis. Background subtraction is the most commonly used
moving object detection methods in video, in which the extracted object will be
feed to a higher-level process ( i.e. object localization, object tracking ).
The main requirement of background subtraction method is to construct a
stationary background model and then to compare every new coming frame with it
in order to detect the moving object.
Relied on the supposition that the background occurs with the higher appearance
frequency, a proposed background reconstruction algorithm has been presented
based on pixel intensity classification ( PIC ) approach.

Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...

Cost estimation is considered one of the important tasks in the construction projects management. The precise estimation of the construction cost affect on the success and quality of a construction project. Elemental estimation is considered a very important stage to the project team because it represents one of the key project elements. It helps in formulating the basis to strategies and execution plans for construction and engineering.  Elemental estimation, which in the early stage, estimates the construction costs depending on  . minimum details of the project so that it gives an indication for the initial design stage of a project. This paper studies the factors that affect the elemental cost estimation as well as the rela

In this paper, the maximum likelihood estimates for parameter ( ) of two parameter's Weibull are studied, as well as white estimators and (Bain & Antle) estimators, also Bayes estimator for scale parameter ( ), the simulation procedures are used to find the estimators and comparing between them using MSE. Also the application is done on the data for 20 patients suffering from a headache disease.

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered