A problem of combining elementary sectors of an airspace region is considered, in which a minimum number of combined sectors must be obtained with restrictions on their load and feasibility of combinations such as the requirement of the space connectivity or the membership of a given set of permissible combinations. Computational methods are proposed and tested to be used for solution  of general problems of airspace sectorization. In particular, two types of combinatorial algorithms are proposed for constructing partitions of a finite set with specified element weights and graph-theoretical relationships between the elements. Partitions are constructed by use of a branch and bound method to minimize the number of subsets in the final partition, while limiting the total weight of elements in the subset. In the first type algorithm, ready-made components of the final partition are formed in each node of the branch and bound tree. The remaining part of the original set is further divided at the lower nodes. In the second type algorithm, the entire current partition is formed in each node, the components of which are supplemented at the lower nodes. When comparing algorithms performance, the problems are divided into two groups, one of which contains a connectivity requirement, and the other does not. Several integer programming formulations are also presented. Computational complexity of two problem variants is established: a bin packing type problem with restrictions on feasible combinations, and covering type problem.

Integral transformations on a finite interval with a singular basis wavelet are considered. Using a sequence of such transformations, the problem of nonparametric approximation of a function is solved. Traditionally, it is assumed that the validity condition must be met for a basic wavelet (the average value of the wavelet must be zero). The paper develops the previously proposed method of singular wavelets when the tolerance condition is not met. In this case Delta-shaped functions that participate in Parzen – Rosenblatt and Nadaray – Watson estimations can be used as a basic wavelet. The set of wavelet transformations for a function defined on a numeric axis, defined locally, and on a finite interval were previously investigated. However, the study of the convergence of the decomposition on a finite interval was carried out only in one particular case. It was due to technical difficulties when trying to solve this problem directly. In the paper the idea of evaluating the periodic continuation of a function defined initially on a finite interval is implemented. It allowed to formulate sufficient convergence conditions for the expansion of the function in a series. An example of approximation of a function defined on a finite interval using the sum of discrete wavelet transformations is given.

It is shown that the choice of the time sampling parameter of the digital model of a continuous dynamic system with chaotic modes based on its dynamics makes it possible to control the characteristics of the output sequence, including avoiding short cycles and periodic behavior modes. On the example of the Lorentz system, the analysis of the law of motion of a chaotic system, linearized in the vicinity of points of stable and unstable equilibrium, is carried out, on the basis of which the parameters of the mathematical model of the generator of pseudo-random numbers are selected. The output sequence of numbers generated in proposed way is subjected to statistical and correlation analysis. Based on the results of the tests carried out, we can say that the obtained pseudo-random sequences based on continuous chaotic systems have statistically random properties and can be used in steganographic and cryptographic systems.

This paper presents the results of an experimental study and the development of tools for automatic analysis and recognition of histological images in order to obtain quantitative estimates of the presence and degree of aggressiveness of prostate cancer in the commonly used Gleason and ISUP scales. The input data consisted of 10 616 whole-slide histological images with the size of the largest side up to 100 000 pixels and

22 089 of their image tiles of 256×256 pixels in size. Two solutions were chosen as the final ones. The first solution is based on sequential analysis of image fragments and includes feature extraction using the ResNet50 network and the subsequent generalization of particular recognition results using a small convolutional network. The second solution is based on the simultaneous analysis of the selected informative sections, presented in the form of an intermediate pseudo-image, and its subsequent recognition using an ensemble of four variants of convolutional networks with the EfficientNetB0 architecture. Being independently tested on an unknown image dataset that was not available for authors, these approaches achieved the prediction accuracy of 0,9277 according to the ISUP scale.

The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.

A mathematical model of Internet search and lexical-semantic processing of analogs of the decisions made in various subject areas is proposed. The search is carried out on queries synthesized from descriptions of problem situations. The synthesis process is carried out in two stages. At the first stage, sentences from the text containing a description of the problem situation are classified taking into account the informativeness of the verbal association between them. At the second stage, the information content of each class is calculated. The most informative of the generated classes, after their indexing, are used as queries. In lexical-semantic processing, the found analogs of solutions are examined for sentiment. When assessing sentiment, linguistic dictionaries of tone-colored vocabulary are used, which are formed on the basis of special tone-colored thematic corpora of texts. In the extreme case, two types of dictionaries are created. The first type is intended for the analysis of the positive sentiment in the descriptions of the decisions made, and the second is intended for the analysis of the negative sentiment, that is, in the process of lexical-semantic analysis, mainly positive and negative aspects of the decisions made are considered. The results of the analysis are presented to the user (decision-maker).

The positions of theRepublicofBelarusin the field of ICT, the levels of medical data store and collection, HL7 FHIR standard are considered. International standard HL7 FHIR for the exchange of medical data, designed to improve the interaction of independent medical information systems and reduce the cost of developing interfaces. FHIR introduces the term Resource as the main unit of meaningful information for transferring and storing data, describes a set of information resources and a protocol for interacting with the resource store through a REST service or messaging. A fixed basic set of information resources is designed to cover 80 % of information scenarios. This standard is used in the national system of electronic prescriptions in theRepublicofBelarus.

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.