The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).

This study is dedicated to solving multicollinearity problem for the general linear model by using Ridge regression method. The basic formulation of this method and suggested forms for Ridge parameter is applied to the Gross Domestic Product data in Iraq. This data has normal distribution. The best linear regression model is obtained after solving multicollinearity problem with the suggesting of 10 k value.

Background: Porcelain veneers are under a great deal of stress which may lead to clinical failure as fracture or dettachment. This study examined whether different finishing lines and lingual shoulder preparations in the incisal area of the maxillary central incisor affect the bond of the porcelain veneers. Materials and methods: A two- dimensional finite element model was made. Location and magnitude of maximum Von Mises stresses were calculated in porcelain veneer. Six types of preparations were drawn as:incisal overlap of 0.5mm, 1mm and 1.5mm depth and lingual shoulder, and incisal overlap of 0.5mm, 1mm and 1.5mm depth without shoulder preparation. Results: Stress formation is maximum in the incisal edge region. All the lingual shoulder

Abstract

In this research provide theoretical aspects of one of the most important statistical distributions which it is Lomax, which has many applications in several areas, set of estimation methods was used(MLE,LSE,GWPM) and compare with (RRE) estimation method ,in order to find out best estimation method set of simulation experiment (36) with many replications  in order  to get mean square error and used it to make compare , simulation experiment  contrast with (estimation method, sample size ,value of location and shape parameter) results show that estimation method effected by simulation experiment factors and ability of using other estimation methods such as(Shrinkage, jackknif

     Recently Tobit  Quantile Regression(TQR) has emerged as an important tool in statistical analysis . in order to improve the parameter estimation in (TQR) we proposed Bayesian hierarchical model with double adaptive elastic net technique  and Bayesian hierarchical model with adaptive ridge regression technique .

 in double adaptive elastic net technique we assume  different penalization parameters  for penalization different regression coefficients in both parameters λ₁and  λ₂  , also in adaptive ridge regression technique we assume different  penalization parameters for penalization different regression coefficients i

One of the critical factors for dental implant success is accurate clinical and radiological assessment. Cone-beam computed tomography systems (CBCT) allow surgeons to determine the quality and quantity before surgery, aiding in treatment planning. This study highlights the importance of such an assessment Materials and Methods A total of 40 patients were referred to CBCT scanning for pre-dental implant assessment in the Oral and Maxillofacial Radiology department at Al-Shaheed Gazi Al Hariri Hospital from 2021 to 2022. The patients were between 18 and 50 years old. All data were analyzed with on-demand 3D software (Kavo OP 3D: Cone Beam 3D imaging Germany). Results: Regarding the ridge shape: In the upper anterior and posterior regions, th

The problem of Multicollinearity is one of the most common problems, which deal to a large extent with the internal correlation between explanatory variables. This problem is especially Appear in economics and applied research, The problem of Multicollinearity has a negative effect on the regression model, such as oversized variance degree and estimation of parameters that are unstable when we use the Least Square Method ( OLS), Therefore, other methods were used to estimate the parameters of the negative binomial model, including the estimated Ridge Regression Method and the Liu type estimator, The negative binomial regression model is a nonline