Distributed information systems that support scientific and educational activities can work with various information systems. The main goal of creating a distributed information system supporting scientific and educational activities is to accelerate the pace and improve the quality of information exchange in the scientific environment. The paper considers technological methods for constructing models of information systems designed to support scientific and educational activities. The model under consideration is that the developed model of an information system for working with scientific materials should solve the problems of long-term storage of information, organizing data search by attributes, accumulating and replacing metadata. Based on the analysis of typical scenarios of information servers, the tasks that should be solved when organizing an access control system for distributed information resources are formulated. Within the framework of this technology, three access control models are discussed, which differ in the degree of integration of information server functions with the Z39.50 technologies. The creation and support of distributed information systems and electronic libraries that integrate heterogeneous information resources and operate in various software and hardware environments require special approaches to managing these systems. If the resources or data themselves can be managed locally, even for distributed information systems, then the task of managing access to distributed resources cannot be solved within the framework of local administration. The justification of the last thesis can be seen when considering typical scenarios of the information server, which we will describe below.

The paper deals with general issues of organizing access to electronic documents in the framework of scientific and educational activities. Large volumes of already existing information, its continuous growth, the heterogeneous nature of storage and distribution, the lack of a unified way of working with it create many difficulties when using it. Awareness of these difficulties, qualitative changes in the field of information technology and telecommunications have led to the need to solve the problem of finding new approaches to the creation of repositories of information resources, their structure, and the development of tools necessary for users. Currently, such approaches are called "digital" or "electronic" libraries. According to the preliminary concept, an intelligent scientific and educational Internet resource will be an information system accessible via the Internet, providing systematization and integration of scientific knowledge, data, and information resources into a single information space, meaningful and effective access to them, as well as support for their use in solving various scientific and educational tasks. Another problem of the organization of effective information support for scientific and educational activities is that, due to its diversity and multidimensional nature, scientific and educational information resources are dispersed on remote pages of many sites and in distributed electronic libraries and archives. To solve this problem, it is necessary to solve the problem of bringing such resources related to one area of knowledge into a single information space, and also, no less important, to support their logical integrity. Without solving these two related tasks, it is impossible to solve the main task - to provide all participants of scientific and educational activities with meaningful access to integrated information resources and means of their analysis. The support of information systems in the field of scientific and educational activities is relevant, since the need for information always exists. In order to satisfy this need, it is necessary to organize access to various resources.

With the daily increase in document flow, as well as the transition to paperless document management around the world, the demand for electronic document management systems is increasing. This significantly requires optimization of these systems in terms of quality document information retrieval and document management. However, research based on statistical methods cannot effectively handle large amounts of data extracted from electronic documents. In this regard, machine learning methods can effectively solve this problem. This paper presents an approach to building a model of an intelligent document management system using machine learning techniques to ensure efficient employee performance in organizations. The authors have solved a number of problems to optimize each of the document management subsystems, resulting in the development of an intelligent document management system model, which can be effectively applied to enterprises, government and corporate institutions. The feasibility and effectiveness of the proposed model of intelligent document management system based on machine learning and multi-agent modeling of information retrieval processes provides maximum reliability and reduced time of work on documents. The obtained results show that with the help of the presented model it is possible to further develop an intelligent document management system that will allow an electronic document to qualitatively go through the whole life cycle of a document, starting from the moment of document registration and finishing with its closing, i.e. execution, which will greatly facilitate the daily work of users with large volumes of documents. At the same time, the paper considers the application of topic modeling methods and algorithms of text analysis based on a multi-agent approach, which can be used to build an intelligent document management system.

The relevance of the topic, in particular, if to take one of the information flows, whether it is the action of a human factor or a specific object, then it is true that special processing of the machine learning language and automatic information output significantly optimize human life. With the help of neural networks and their chest radiography is one of the most accessible radiological studies for screening and diagnosis of many lung diseases a special machine learning language is to study the flow of information about it and the same object in real time using neural networks. The article describes the terminology of the problem of X-ray recognition using machine learning methods and algorithms, examines the relevance of the problem, and analyzes the current state of the problem in the field of X-ray recognition. The aspects of the problem being solved, identified during the analysis, in the form of solved problems, approaches, methods, information technologies used, tools and software solutions to the problem are noted. The paper is devoted to the description of a modified method of fuzzy clustering of halftone images, which at each iteration performs a dynamic transformation of the source data based on a singular decomposition with automatic selection of the most significant columns of the matrix of left singular vectors. The results of experimental studies were obtained by processing X-ray images. As a result of testing a neural network model, in the output layer of which a sigmoidal activation function was used to activate neurons, and an algorithm was used as an optimization method, the best values of accuracy and completeness were obtained: accuracy - 94,2. During testing, the neural network showed an accuracy of pneumonia recognition equal to 94,27 %.