In the modern world, the problems arising in the field of traffic are of great importance. In order to solve existing problems, various intelligent systems are being developed, one of which is the Smart City system. This work is devoted to the development of an information and analytical system (IAS) for controlling an intelligent traffic light. The presented system consists of two levels, each of which contains a set of specific operations. The first level is responsible for detecting objects, in particular pedestrians and cars at the intersection, and the second level calculates the operating time of traffic light signals for the control signal that is transmitted to the device. For comparative analysis, the combined method (HOG+SVM) Histogram of oriented gradients was chosen, based on counting the number of gradient directions on individual image areas and Support Vector Machines, which are used to construct hyperplanes in n-dimensional space in order to separate objects belonging to different classes. The results of an experimental study, during which the recognition of objects in images was carried out, showed the superiority of the developed information and analytical system over existing methods. The average accuracy of detecting pedestrians and cars through the IAS was 69.4%. In addition, according to the experiment, it was concluded that the accuracy of detecting objects in images is directly proportional to the distance from the video camera to the object.

This paper addresses the issue of automatic annotation of training datasets in the field of computer vision using machine learning methods. Data annotation is a key stage in the development and training of deep learning models, yet the process of creating labeled data often requires significant time and labor. This paper proposes a mechanism for automatic annotation based on the use of convolutional neural networks (CNN) and active learning methods.

The proposed methodology includes the analysis and evaluation of existing approaches to automatic annotation. The effectiveness of the proposed solutions is assessed on publicly available datasets. The results demonstrate that the proposed method significantly reduces the time required for data annotation, although operator intervention is still necessary.

The literature review includes an analysis of modern annotation methods and existing automatic systems, providing a better understanding of the context and advantages of the proposed approach. The conclusion discusses achievements, limitations, and possible directions for future research in this field.

The development of an application based on machine learning methods for automatic annotation of web pages according to the Microdata standard is described, with the possibility of extension to other standards and injecting data to JSX files. Datasets were collected and prepared for training Machine Learning (ML) models. The ML model metrics were collected and analyzed.

The article describes the main stages of the application of information technologies for scientific research in geology. A number of digital technologies of the near future, which currently have prospects for application in geology, are considered. The first results of the work at the GGM RAS showed the prospects of applying the next steps in the development of OT technologies in the creation of information and computing systems to support geological research.

The aggregation of scientific information is essential for a comprehensive analysis of geological objects. This paper explores the potential and possibilities of semantic search to select thematically similar publications in the geological domain. Various language models are examined in the context of identifying similarities and differences in texts describing mineral deposits. After additional training of language models, a significant improvement in search results is demonstrated. Two web services are presented, based on a method for calculating the semantic similarity between texts and providing a quantitative assessment of their similarity.

A new approach to the development of a tool aimed at simplifying the workflow of a game designer is presented. The requirements are elaborated, the work scenario is developed and the main parameters for the developed tool are specified. The main objective of the tool is to speed up and facilitate the selection of proper game mechanics without the need to spend valuable time on lengthy analysis of other videogame projects.

To provide more effective work of game designers in the selection of game mechanics, we analyzed a variety of approaches to the classification of game mechanics. In the process of the research various methods of classification of game mechanics were considered, the analysis revealed which classifications are more suitable for decomposition of game mechanics. The results of the research allowed us to identify key aspects of game mechanics, which will serve as a foundation for the development of the tool.

This research represents an important step in creating a tool that will optimize the game design process and increase the speed of videogame development.