A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroup

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

Training has an effect on employees’ performances. Accordingly, the person who is responsible for employees’ development must figure out the most effective way to train and develop employees. Central Michigan University (CMU) has recognized the importance of providing appropriate training for employees who have a duty in advising students. The reason is that these employees have a significant impact on students’ educational performances. Thus, special attention to this category of employees is needed to improve advising quality. This research attempted to explore the impact of training on academic advising at CMU. Face-to-face interviews and online surveys were used as data collection tools for this study. The study scope c

Abstract

This study came to clarify the redemption in terms of being a method and a subject of knowledge that has its causes, controls, etiquette and scientific implications for Muslim scholars.

The science of redemption has a historical extension, starting from the era of the Companions - may God be pleased with them - and through the stages of codification and classification of works in an effort to document it and diversify its methods at the inductive, analytical, critical and argumentative levels, and to determine its causes and from the completion of the research conclude it with evidence of its scientific effects because it is a science that enriched the Islamic library in general, and hadith in particular.

The performance of a solar assisted desiccant cooling system for a meeting-hall located in the College of Engineering/University of Baghdad was evaluated theoretically. The system was composed of four components; a solar air heater, a desiccant dehumidifier, a heat exchanger and an evaporative cooler. A computer simulation was developed by using MATLAB to assess the effect of various design and operating conditions on the performance of the system and its components. The actual weather data on recommended days were used to assess the load variation and the system performance during those days. The radiant time series method (RTS) was used to evaluate the hourly variation of the cooling load. Four operation modes were employed for perform

Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results

This paper proposes a novel finite-time generalized proportional integral observer (FTGPIO) based a sliding mode control (SMC) scheme for the tracking control problem of high order uncertain systems subject to fast time-varying disturbances. For this purpose, the construction of the controller consists of two consecutive steps. First, the novel FTGPIO is designed to observe unmeasurable plant dynamics states and disturbance with its higher time derivatives in finite time rather than infinite time as in the standard GPIO. In the FTGPO estimator, the finite time convergence rate of estimations is well achieved, whereas the convergence rate of estimations by classical GPIO is asymptotic and slow. Secondly, on the basis of the finite and fast e

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