HLA genes are associated with more than 100 diseases, including infectious diseases like HIV, and some cancers. Some autoimmune conditions, including diabetes and multiple sclerosis, are also linked to specific variations in the HLA. In MS, the immune system fails to distinguish between the body's tissues and foreign proteins resulting in it attacking myelin as if it were foreign. Several HLA genes have been found to influence the risk of developing MS. Some variants make an individual more likely to develop MS, whereas others may have a protective effect and decrease the risk. Although the precise genes involved in the development of multiple sclerosis are still not fully understood, research has identified one HLA gene that is more prevalent in women with MS and may be part of the explanation why more women than men get the condition.
Time series have gained great importance and have been applied in a manner in the economic, financial, health and social fields and used in the analysis through studying the changes and forecasting the future of the phenomenon. One of the most important models of the black box is the "ARMAX" model, which is a mixed model consisting of self-regression with moving averages with external inputs. It consists of several stages, namely determining the rank of the model and the process of estimating the parameters of the model and then the prediction process to know the amount of compensation granted to workers in the future in order to fulfil the future obligations of the Fund. , And using the regular least squares method and the frequ
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The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
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