In this paper, we will illustrate a gamma regression model assuming that the dependent variable (Y) is a gamma distribution and that it's mean ( ) is related through a linear predictor with link function which is identity link function g(μ) = μ. It also contains the shape parameter which is not constant and depends on the linear predictor and with link function which is the log link and we will estimate the parameters of gamma regression by using two estimation methods which are The Maximum Likelihood and the Bayesian and a comparison between these methods by using the standard comparison of average squares of error (MSE), where the two methods were applied to real da

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati

Abstract This research investigates how activated carbon (AC) was synthesized from potato peel waste (PPW). Different ACs were synthesized under the atmosphere's conditions during carbonation via two activation methods: first, chemical activation, and second, carbon dioxide-physical activation. The influence of the drying period on the preparation of the precursor and the methods of activation were investigated. The specific surface area and pore volume of the activated carbon were estimated using the Brunauer–Emmett–Teller method. The AC produced using physical activation had a surface area as high as 1210 m2/g with a pore volume of 0.37 cm3/g, whereas the chemical activation had a surface area of 1210 m2/g with a pore volume of 0.34 c

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

Moment invariants have wide applications in image recognition since they were proposed.

The multiple linear regression model is an important regression model that has attracted many researchers in different fields including applied mathematics, business, medicine, and social sciences , Linear regression models involving a large number of independent variables are poorly performing due to large variation and lead to inaccurate conclusions , One of the most important problems in the regression analysis is the multicollinearity Problem, which is considered one of the most important problems that has become known to many researchers  , As well as their effects on the multiple linear regression model, In addition to multicollinearity, the problem of outliers in data is one of the difficulties in constructing the reg

           kindergarten teacher is one of the fundamental pillars upon which the kindergarten environment so that exposure to a number of problems that could affect its functioning in addition to any deficiencies in this environment leads to deprive a child of some activities and acquisition of concepts so the researcher studying the problems of working in kindergartens from the perspective of the parameters, so the researcher based measuring instrument for labour problems of (30) search sample was paragraph (50) parameter that was chosen at random and have been extracted Sincerity and strength tool researcher used statistical methods and discriminatory (Pearson correlation coefficient, t