Mathematical Models of Effective Topology of Computer Networks for Electric Power Supply Control on Railway Transport

The paper is devoted to analysis of modern directions of innovation-investment formation of intelligent computer networks that control the fast-moving technological processes of electricity supply. It is based on the conclusion that the problem of increasing the productivity of information exchange between information resources and consumers is dominant. A method for increasing the efficiency of information exchange is proposed as a search for the rational location of a new node and the organization of such a set of its connections among the whole set of nodes of the computer network, which provides a minimum average topological distance. Mathematical models of effective topological organization of connections in computer network of power consumption control at the level of traction substations, electric power distances and the railway in general are proposed.

Adaptive algorithms for researching problems in a variable computer environment

The paper considers tools for studying computer models of problems in modeling physical and technical processes. Adaptive algorithms for studying structural and mathematical properties and solving problems in a variable computer environment are presented. The proposed innovative functionality is integrated into the intelligent computer mathematics system.

CONTRIBUTION OF AI TO THE CONSTRUCTION WORLD

Artificial intelligence is a department of computer science and information technological know-how, involved in the research, layout, and application of intelligent computer. Conventional techniques for modeling and optimizing complicated structure systems require big amounts of computing assets, and artificial-intelligence-primarily based solutions can frequently provide treasured alternatives for successfully solving problems inside the civil engineering. This paper summarizes currently evolved methods and theories within the growing path for programs of synthetic intelligence in civil engineering, such as evolutionary computation, neural networks, fuzzy systems, professional machine, reasoning, type, and learning, in addition to others like chaos theory, cuckoo seek, firefly algorithm, know-how-based engineering, and simulated annealing. The primary studies tendencies are also talked about in the end. The paper presents an overview of the advances of synthetic intelligence carried out in civil engineering.

Intelligent soft package for modeling optimization processes for managing the activities of the Bank's retail unit

Currently, one of the main directions in the field of banking process automation is the creation and implementation of integrated management decision support systems. In the context of growing competition and general digitalization of the economy, the issue of improving the efficiency of bank management is most acute. Most of the automated systems used in this area are aimed at identifying "gaps" in existing business processes and further optimizing their individual parts. Moreover, such systems are not based on economic and mathematical models and algorithms for their solution. This article presents a description of an intelligent computer software package that allows you to simulate the optimization of software and adaptive management of specific business processes - managing the number of personnel and the sales system of the retail block of a commercial bank. The basis of the developed software package is a discrete dynamic economic and mathematical model of the investigated business processes and the developed optimization algorithms for software and adaptive control of these processes. The process of making decisions on the recruitment/reduction of the staff of various categories of employees of the Retail block of a commercial bank, as well as on the management of the sales system provided by the relevant employees. The paper presents the main stages of creating the proposed controlled dynamic model with a vector quality criterion. Based on computer modeling with the help of the developed intelligent computer software complex, the results of optimal solutions for various options for practical examples were obtained. The results are graphically illustrated and analyzed. Based on the proposed dynamic model, it is possible to solve other problems of optimizing software and adaptive management of processes that determine banking activities and develop automated information systems for implementing support for managerial decision-making in this area.

The engineering skills training process modeling using dynamic bayesian nets

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.