In this article, we propose a Bayesian Adaptive bridge regression for ordinal model. We developed a new hierarchical model for ordinal regression in the Bayesian adaptive bridge. We consider a fully Bayesian approach that yields a new algorithm with tractable full conditional posteriors. All of the results in real data and simulation application indicate that our method is effective and performs very good compared to other methods. We can also observe that the estimator parameters in our proposed method, compared with other methods, are very close to the true parameter values.
Objective conditions for the possibility of punishment are legal or material facts –positive or negative that depart from the activity of the offender. The legislator comments on their subsequent verification on the formation of some crimes the possibility of.The application of punishment to the offender , but although they are facts of an object nature that approach and overlap with many systems and cases , they are distinguished by a certain subjectivity that differentiates them from each case that may seem similar or approach them. To clarify the ambiguity that may surround these conditions , Which may lead to confusion between them and what be similar to other cases due to the common effect that they have in common , which is the f
... Show MoreMost frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagn
... Show More"The aim of the research is to identify the availability of the dimensions of the research variables represented by organizational symmetry and the quality of work-life at the University of Information and Communications Technology, which is one of the formations of the Ministry of Higher Education and Scientific Research in Baghdad, in addition to knowing the relationship and influence between them. The research relied on the descriptive analytical approach based on peer description. The research was analyzed and the research sample consisted of (148) individuals, the sample was chosen using the comprehensive inventory method, data was obtained by relying on the questionnaire which was prepared from ready-made m
... Show MoreThe use of containers in transportation leads to the reduction in time and effort of the process of loading and unloading of goods as well as protecting the goods from damage and breakage and to reduce the financial costs.
. This development has led also to make changes in the sizes and capacity of ships, therefore changes in the ports must be taken place where they must be provided with an appropriate depth for such vessels, that means the increase in the depth of ports and the establishment of wide storage yards and to provide appropriate mechanisms for handling process.
In this study, the researcher has dealt with this type of transport business in Iraqi ports, namely:
1- Khur Al-Zubair port.
2- Um Qaser port.
In order
A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. This paper proposes an algorithm based on hybrid transform (stationary wavelet transform proceeding by slantlet transform); The slantlet transform is applied to the approximation subband of the stationary wavelet transform. BlockShrink thresholding technique is applied to the hybrid transform coefficients. This technique can decide the optimal block size and thresholding for every wavelet subband by risk estimate (SURE). The proposed algorithm was executed by using MATLAB R2010aminimizing Stein’s unbiased with natural images contaminated by white Gaussian noise. Numerical results show that our algorithm co
... Show MoreTuberculosis is caused by Mycobacterium tuberculosis; it is considered as one of the most common, infectious diseases and major causes of morbidity and mortality worldwide. A prospective study was conducted to obtain more clarification about the impact of causative agent and its treatment to enhance autoantibodies production such as ANCA and BPI which used as diagnostic markers for several diseases, and to provide further insight into the classical risk factors (age and sex).Seventy patients with tuberculosis involved in this study, 35 of them were untreated and 35 with treatment administration these patients were attending to directorate of general health national reference laboratory in Baghdad during the period between November/ 2012
... Show MoreOur goal in this work is to describe the structure of a class of bimodal self maps on the compact real interval I with zero topological entropy and transitive.
Recent accumulated evidences suggest that prolactin is an important immunomodulator and may have a role in the pathogenesis of systemic lupus erythematosus (SLE). The aim of this study was to assess the frequency of hyperprolactinemia in women with SLE and to evaluate its correlation with disease flares. Serum prolactin levels were measured in 62 women with SLE and 50 age- and sex-matched healthy controls. In patients and control groups prolactin levels were determined by immunoradiometric assay (IRMA). The prolactin level was found to be higher than normal rang in (40.3%) of SLE patients in active stage versus only (8.06%) of the same SLE patients but in the inactive stage and in (4%) of control group, the elevation was ranging between mi
... Show MoreSupport Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.