machine learning

The article considers the problem of forecasting extreme temperature phenomena as one of the key components of ensuring the stability of the functioning of modern natural-technogenic and socio-economic systems in the face of climate change. The relevance of the study is due to the increase in the frequency and intensity of abnormally high and low temperatures, which cause significant risks to energy infrastructure, transport systems, the agro-industrial complex and public safety.

The article investigates the problem of increasing the efficiency of the data labeling process in poorly controlled environments based on Active Learning methods. The relevance of the work is due to the rapid growth of unstructured and partially labeled data, the high cost of manual annotation, the shortage of qualified experts, and the negative impact of noise labels on the quality of machine learning models.

This paper investigates modern approaches to the application of artificial intelligence (AI) models in prosthetic systems from the perspective of information technologies. The aim of the study is to provide a systematic analysis and comparative evaluation of contemporary AI models used in prosthetics, with a focus on control algorithms, biosignal processing methods, and the integration of intelligent solutions into real-time prosthetic devices.

This article presents an expanded experimental comparison of the effectiveness of machine learning methods for the task of three-class sentiment classification (negative, positive, neutral). The research focuses on the specific domain of Ukrainian-language citizen appeals to city administration bodies, which is a relevant and practically significant task for the development of modern e-Governance and decision support systems.

The article addresses the problem of optimizing linear regression models under conditions of high dimensionality and multicollinearity, which are typical for modern machine learning applications. The relevance of the study is обусловлена the need to ensure a balance between model generalization ability and interpretability, especially when dealing with noisy and limited datasets.

The article presents an information system that automatically detects military objects in images or video streams, determines their exact geographic coordinates, and tracks their future movements through data visualization. The development of such a system is an extremely relevant task in the context of modern threats, as it can significantly enhance military units' situational awareness and improve operational planning. During the research, existing approaches to object detection, geolocation methods, and target tracking algorithms were analyzed.

Legal prediction is one of the most critical subfields in Natural Language Processing. The researchers use state-of-the-art machine learning and artificial intelligence methodologies to predict specific judicial facets, such as the judicial outcome. For this research, we have built a web text crawler to extract homicide data cases from Brazilian electronic legal systems.

The results of research into the process of grouping authors of printed text messages in social networks are presented. The hypothesis about the possibility of grouping authors based on the results of the classification of their text messages has been confirmed. For this purpose, the virtual robot builds an intelligent monitoring agent for grouping the authors of social network text messages. The peculiarity of these messages is that they are short texts.