This paper presents a hybrid genetic algorithm (hGA) for optimizing the maximum likelihood function ln(L(phi(1),theta(1)))of the mixed model ARMA(1,1). The presented hybrid genetic algorithm (hGA) couples two processes: the canonical genetic algorithm (cGA) composed of three main steps: selection, local recombination and mutation, with the local search algorithm represent by steepest descent algorithm (sDA) which is defined by three basic parameters: frequency, probability, and number of local search iterations. The experimental design is based on simulating the cGA, hGA, and sDA algorithms with different values of model parameters, and sample size(n). The study contains comparison among these algorithms depending on MSE value. One can conc

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

The study aimed to estimate the content of lead and determine the quality of the internal coating of metal cans through electrical conductivity as well as to determine the accuracy of the information card for some types of canned food that available in local markets. The information card test showed that all of these samples contained the name of the food, trade mark, country origin, weight, and components, as was indicated by the company producing in all of them except for the C₁₂ sample which was otherwise, and the batch number was mentioned in all samples except for the C₃ and C₁₇ which was not clear and not mentioned in the C₂₁, and the validity period was observed (produce and fini

Tax fraud is following different methods of tax evasion (bypassing the laws, instructions and regulations related to tax) by not showing the real taxable income by using laws, instructions and regulations improperly, and because of the weak basic role of forensic accounting in detecting and reducing tax fraud, the problem has become more influential on the state general tax income. The main objective of the research is to identify forensic accounting and the extent to how it can be applied in the General Tax Authority to assist forensic authorities in issuing judgments in fraud cases. To achieve the objectives of the research, the descriptive analytical approach was used to reach the topic of the research, and a questionnaire (co

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p