Prediction of the formation of pore and fracture pressure before constructing a drilling wells program are a crucial since it helps to prevent several drilling operations issues including lost circulation, kick, pipe sticking, blowout, and other issues. IP (Interactive Petrophysics) software is used to calculate and measure pore and fracture pressure. Eaton method, Matthews and Kelly, Modified Eaton, and Barker and Wood equations are used to calculate fracture pressure, whereas only Eaton method is used to measure pore pressure. These approaches are based on log data obtained from six wells, three from the north dome; BUCN-52, BUCN-51, BUCN-43 and the other from the south dome; BUCS-49, BUCS-48, BUCS-47. Along with the overburden pr

In the field of civil engineering, the adoption and use of Falling Weight Deflectometers (FWDs) is seen as a response to the ever changing and technology-driven world. Specifically, FWDs refer to devices that aid in evaluating the physical properties of a pavement. This paper has assessed the concepts of data processing, storage, and analysis via FWDs. The device has been found to play an important role in enabling the operators and field practitioners to understand vertical deflection responses upon subjecting pavements to impulse loads. In turn, the resultant data and its analysis outcomes lead to the backcalculation of the state of stiffness, with initial analyses of the deflection bowl occurring in conjunction with the measured or assum

This research sought to present a concept of cross-sectional data models,  A crucial double data to take the impact of the change in time and obtained from the measured phenomenon of repeated observations in different time periods, Where the models of the panel  data were defined by different types of fixed , random and mixed, and Comparing them by studying and analyzing the mathematical relationship between the influence of time with a set of basic variables Which are the main axes on which the research is based and is represented by the monthly revenue of the working individual and the profits it generates, which represents the variable response And its relationship to a set of explanatory variables represented by the

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

 The financial markets are one of the sectors whose data is characterized by continuous movement in most of the times and it is constantly changing, so it is difficult to predict its trends , and this leads to the need of methods , means and techniques for making decisions, and that pushes investors and analysts in the financial markets to use various and different methods in order to reach at predicting the movement of the direction of the financial markets. In order to reach the goal of making decisions in different investments, where the algorithm of the support vector machine and the CART regression tree algorithm are used to classify the stock data in order to determine

A seemingly uncorrelated regression (SUR) model is a special case of multivariate models, in which the error terms in these equations are contemporaneously related. The method estimator (GLS) is efficient because it takes into account the covariance structure of errors, but it is also very sensitive to outliers. The robust SUR estimator can dealing outliers. We propose two robust methods for calculating the estimator, which are (S-Estimations, and FastSUR). We find that it significantly improved the quality of SUR model estimates. In addition, the results gave the FastSUR method superiority over the S method in dealing with outliers contained in the data set, as it has lower (MSE and RMSE) and higher (R-Squared and R-Square Adjus

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the