Prospects for Applied Mathematics and Data Analysis

In this paper, a program for analyzing potential events and designing preventive measures to ensure safe operation of electrical power systems was developed, which was published in the Journal of Al-Baath University. The new visual software system performs all the functions of the previous program, in addition to a set of developed options, studies and comparisons. It also features a graphical method for entering electrical network data and conforms to software engineering standards, with an analytical study based on an approved methodology, the Unified Modeling Language (UML). The spiral software model was chosen during the research, which made it possible to obtain a reliable software product that is characterized by the highest possible standards. In this paper, the parameters of the electrical power system on a 6-busbar network were analyzed and studied, and the effect of changing the power factor on the possibility of finding the optimal solution, whether by changing generation or by changing generation and decreasing loads, was studied. The program's effectiveness, flexibility and accuracy of results have been proven.

In this paper, we investigate some operations of algebraic structure of neutrosophic matrices related to indeterminacy real numbers.  

Interest in artificial intelligence has recently increased, because of its proven competence and effectiveness in addressing many outstanding issues and problems, as it is a modern science that derives its concepts from simulating the style of thinking and analysis in humans. Genetic algorithms are a branch of this science, which requires that their determinants be selected according to the problem at hand. In this paper, we examined the effect of changing some of the determinants of genetic algorithms, namely mutation probability and population size, on the accuracy of results for three problems of different frequency spectrum. The effect of the election algorithm in obtaining accurate results was also studied, by comparing the roulette wheel algorithm and Elitism algorithm.

The world has always suffered and from diseases and epidemics, and the coronavirus is one of the most dangerous viruses that threatened human life that requires the use of all scientific methods and means to respond to it and reduce its spread by early detection of infections and taking necessary measures In view of the significant role that artificial intelligence plays in most fields of science, it has become one of the most important scientific methods used to resolve complex issues and has been harnessed in medical diagnosis, one of the most complex areas. Many AI and machine learning algorithms have been used to diagnose and detect diseases in general and coronavirus in particular. The support vector machine (svm) machine algorithm was one of the most important algorithms in this area and is one of the most effective compilations used in the knowledge extraction process In spite of all this, the results they present remain incomplete because classification issues do not deal with cognitive uncertainties such as ambiguity, neutrality and inconsistency associated with perception of human thinking, This adversely affects the work of a classic support vector machine and affects the accurate diagnosis of the disease To solve this problem, we have done this research using a Neutrosophic Support Vector Machine because it takes into account all possible cases during the study of the sample and it reduces the impact of extreme values. This increases the accuracy of the results when diagnosing coronavirus symptoms. The study was conducted according to the following steps: 1.       We extract features from chest radiographs based on GLCM 2.       We form a neutrosophic dataset. 3.       We train Neutrosophic Support Machine N-SVM on new data. 4.       We record the results. Comparing the results, we got using the upgraded N-SVM algorithm with the classic SVM algorithm results we found that it gives a more accurate diagnosis of the disease.

In this paper, we introduce infra bi-topological structure which is a more general structure than infra-topological spaces. This new space make us enable to increate a new sub-classes of sets, called  infra bi-open (bi-closed) sets,  pairwise infra-open (closed) sets. also we define infra bi-closure, pairwise infra bi-interior and their basic properties are presented. The relations of these concepts with their counterparts in infra-topological space s are given and many examples are presented.