IRAOI JOURNAL OF STATISTICAL SCIENCES

Studying and forecasting Particular matter (PM10) is necessary to control and reduce the damage of environment and human health. There are many pollutants as sources of air pollution may effect on PM10 variable. This type of dataset can be classified as anon-linear. Studied datasets have been taken from climate station in Malaysia. Multiple Linear Regression (MLR) is used as a linear statistical method for PM10 forecasting through its influencing by corresponding climate variables, therefore it may reflect inaccurate results when used with nonlinear datasets. Time stratified (TS) method in different styles is implemental for satisfying more homogeneity of datasets. It includes ordering similar seasons in different years together to formulate anew variable smoother than their original. To improve the results of forecasting, Recurrent Neural Network (RNN) has been suggested to be used after combining with MLR in hybrid MLR-RNN method in this study. In general, the results of forecasting were the best with using time stratified approach. In addition, the results of hybrid method were outperformed comparing to MLR model. As conclusion in this study, RNN and TS can be used as active approaches to obtain better forecasting results with nonlinear datasets in which PM10 is to dependent variable.

It is known that message passing has become one of the most popular parallel programming paradigms because of its ease of use, so it was necessary to know or study the applications that were adopt message passing in their work.
Programming models are for the most part classified by how memory is utilized. In the shared memory model, each cycle gets to a shared location space, yet in the message passing model, an application runs as an assortment of self-ruling cycles, each with its own local memory. The principle preferences of setting up a message-passing standard are convey ability and convenience. In a circulated memory correspondence climate in which the more significant level schedules as well as reflections are based upon lower level message-passing schedules the benefits of normalization are especially evident. Moreover, the usage of a message passing, for example, that proposed here, gives sellers a plainly dined base arrangement of schedules that they can actualize in days of yore, or at times for which they can give equipment uphold, consequently improving adaptability.

Particular matter (PM10) studying and forecasting is necessary to control and reduce the damage of environment and human health. There are many pollutants as sources of air pollution may effect on PM10 variable. This type of dataset can be classified as nonlinear. Studied datasets have been taken from climate station in Malaysia. Multiple linear regression (MLR) is used as linear statistical method for PM10 forecasting through its influencing by corresponding climate variables, therefore it may reflect inaccurate results when used with nonlinear datasets. To improve the results of forecasting, recurrent neural network (RNN) has been suggested to be used after combining with MLR in hybrid in this study. Wavelet analysis is proposed filtering the result of MLR-RNN method for more improving of forecasting results through RNN-Wavelet hybrid method based on MLR model. In general, the best results of forecasting were for using RNN-Wavelet method. . In addition, the results of hybrid methods were outperformed comparing to MLR model as traditional method. As conclusion in this study, Wavelet analysis can be used after hybridizing with RNN based on MLR as active approach to obtain better forecasting results with nonlinear datasets in which PM10 is to dependent variable.

The backpack problem or the multidimensional investor is an important and well-known hard (discontinuous) constrained combinatorial optimization problem in operations research and optimization. Nowadays, algorithms inspired by nature have become extremely important in solving many mathematical problems, including the problem of the investor's portfolio. In order to reach the best solutions, in this research, three algorithms were used to solve this problem. The marine predator algorithm, which is a very modern algorithm, outperformed the weed algorithm and the black hole algorithm in obtaining the best solution and the least possible time. While the black hole algorithm came in the third place, although it does not need to specify any parameter of the algorithm before its work

:In this paper, we deal with one of the most important nonlinear regression models widely used in modeling statistical applications, which is the binary logistic regression model, and then estimating the parameters of this model using statistical estimation methods. In this paper, some ordinary estimation methods are employed to fit the estimation of the parameters of this type of non-linear regression model, and then we compare these estimation methods. Further, the comparison includes some of the important estimation methods, which are the ordinary estimation methods that included the Weighted Least Squares Method (WLS), and the Bayes Method (BM). In order to choose the best method for estimating, by taking several models and different sample sizes and using the statistical standard mean error squares (MSE) for the logistic model estimations for the purpose of comparison. Among the preferred methods for estimating model parameters, and it was generally concluded that the WLS method provides the MSE of estimators compared to the other methods. On the practical side, this model was also used to model data for people with diabetes (that we obtained from Azadi hospital in Duhok) and to estimate parameters using the best methods, and it was reached by comparing patients with diabetes. A census of diabetes with those who did not have diabetes with the appropriateness of the model in modeling this type of data and extracting the main cause of diabetes incidence, which is insulin, as well as the accuracy of the methods in estimating the model parameters

This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion, the predictive distribution under assumption that the shape parameters are known. The prior distribution about the model parameters is represented by non-informative information, as well as the simulate on the generated data from the model by a suggested way based on different values of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. It found that the posterior marginal probability distribution of the location matrix θ and the predictive probability distribution is a matrix-t distribution with different parameters, the posterior marginal probability distribution of the scale matrix Σ is proper distribution but it does not belong to the conjugate family, Through the Bayes factor criterion, it was found that the sample that was used in the generation process was drawn from a population that does not belong to the generalized modified Bessel population.

In this research, an optimal model will be created to control the storage in the blood bank in Nineveh Governorate by studying the continuous review system for storage in light of the ambiguity of random demand. Data were withdrawn from the blood bank and on three inputs (order quantity, damaged quantity and number of donors), where the data distribution was verified and the normal distribution was also linearly followed. At first, we fogged the data using the ready-made tool in the program ((matlab) and after obtaining the results we entered it on the neural network (RNN).The best result obtained is the use of the fuzzy neural network as compared to the classical method

The research aims to build dynamic models represented by the transfer function and State Space Models of a single input variable and a single output variable, The input and output variables are represented by the temperatures of the water before the filtration process and after the filtration process to convert it into potable water As a transfer function model will be built for a single input variable and a single output variable for real data and Fuzzy data, building transfer function models and state space models, finding predictive values and comparing results.