The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Multiple eliminations (de-multiple) are one of seismic processing steps to remove their effects and delineate the correct primary refractors. Using normal move out to flatten primaries is the way to eliminate multiples through transforming these data to frequency-wavenumber domain. The flatten primaries are aligned with zero axis of the frequency-wavenumber domain and any other reflection types (multiples and random noise) are distributed elsewhere. Dip-filter is applied to pass the aligned data and reject others will separate primaries from multiple after transforming the data back from frequency-wavenumber domain to time-distance domain. For that, a suggested name for this technique as normal move out- frequency-wavenumber domain

This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.

We compare two methods Bayesian and . Then the results were compared using MSe criteria.

A simulation had been used to study the empirical behavior for the Logistic model , with  different sample sizes and variances. The results using represent that the Bayesian method is better than the   at small samples sizes.

Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

Purpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement. Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both. Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad. Findings: The Mean Circular Erro

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

Tight reservoirs have attracted the interest of the oil industry in recent years according to its significant impact on the global oil product. Several challenges are present when producing from these reservoirs due to its low to extra low permeability and very narrow pore throat radius. Development strategy selection for these reservoirs such as horizontal well placement, hydraulic fracture design, well completion, and smart production program, wellbore stability all need accurate characterizations of geomechanical parameters for these reservoirs. Geomechanical properties, including uniaxial compressive strength (UCS), static Young’s modulus (Es), and Poisson’s ratio (υs), were measured experimentally using both static and dynamic met

The transportation model is a well-recognized and applied algorithm in the distribution of products of logistics operations in enterprises. Multiple forms of solution are algorithmic and technological, which are applied to determine the optimal allocation of one type of product. In this research, the general formulation of the transport model by means of linear programming, where the optimal solution is integrated for different types of related products, and through a digital, dynamic, easy illustration Develops understanding of the Computer in Excel QM program. When choosing, the implementation of the form in the organization is provided.