Abstract There are many uncertainty sources that may affect the statistical reasoning. However, traditional methods can not deal with all kinds of uncertainty sources, which has led many researchers to develop traditional methods. Studies still exist to this day, making hypotheses to create a common understanding for the purpose of reaching new solutions through the use of new methods that combine traditional and modern theories of sources of uncertainty The aim of current study was to develop the adaptive fuzzy linear regression model in the case of using inaccurate data as the source of uncertainty. Specifically, the model proposed by [1]. However, instead of what dominant in fuzzy linear regression analysis, we used a new born method that uses the positions and entropy to fuzzification instead membership function. As for the comparison method we used the mean absolute difference as performance's accuracy measures. The results of this study showed the efficiency of the use of the position and the entropy function to describe the fuzzy numbers over the use of the membership functions. The results also indicated that the develop model has the best results compared to the model adapted using the membershop functions in [1].
Type 2 daibetes mellitus (T2DM) is a global concern boosted by both population growth and ageing, the majority of affected people are aged between (40- 59 year). The objective of this research was to estimate the impact of age and gender on glycaemic control parameters: Fasting blood glucose (FBC), glycated hemoglobin (HbA1C), insulin, insulin resistance (IR) and insulin sensitivity (IS), renal function parameters: urea, creatinine and oxidative stress parameters: total antioxidant capacity (TAC) and reactive oxygen species (ROS). Eighty-one random samples of T2DM patients (35 men and 46 women) were included in this study, their average age was 52.75±9.63 year. Current study found that FBG, HbA1C and IR were highly significant (P<0.01) inc
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The corona virus epidemic outbreak has urged an extreme worldwide effort for re‐purposing obtainable approved medications for its treatment. In this review, we're focusing on the chemicals properties andpharmacologicaleffectiveness of medicationsofsmallmolecule that are presently being evaluated in clinical trials for the management of corona virus (COVID‐19). The current review sheds light on a number of drugs that have been diagnosed to treat COVID‐19 and their biological effects.
This paper is interested in certain subclasses of univalent and bi-univalent functions concerning to shell- like curves connected with k-Fibonacci numbers involving modified Sigmoid activation function θ(t)=2/(1+e^(-t) ) ,t ≥0 in unit disk |z|<1 . For estimating of the initial coefficients |c_2 | , |c_3 |, Fekete-Szego ̈ inequality and the second Hankel determinant have been investigated for the functions in our classes.
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In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete
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This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).
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The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the
... Show MoreThis research deals with unusual approach for analyzing the Simple Linear Regression via Linear Programming by Two - phase method, which is known in Operations Research: “O.R.”. The estimation here is found by solving optimization problem when adding artificial variables: Ri. Another method to analyze the Simple Linear Regression is introduced in this research, where the conditional Median of (y) was taken under consideration by minimizing the Sum of Absolute Residuals instead of finding the conditional Mean of (y) which depends on minimizing the Sum of Squared Residuals, that is called: “Median Regression”. Also, an Iterative Reweighted Least Squared based on the Absolute Residuals as weights is performed here as another method to
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Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.