класифікація

The article presents an analytical study of the technologies used in the production of children’s toy books as a special type of publishing product that combines artisticaesthetic, educational, playful, and instructional functions. The need for a systematic analysis and the development of a generalized classification of existing types of toy books at the present stage is emphasized, since the lack of unified approaches complicates the processes of design, editing, and print production.

The problem of automated error detection in Ukrainian texts is becoming particularly relevant in the context of the growth of digital content. A mathematical model of a decision support system for detecting errors in Ukrainian-language texts has been developed. The process of error identification has been studied as a multi-class classification task at the token level, considering the context of the text. The use of probabilistic models has been proposed to determine the type of error depending on the environment of tokens in the text.

typical technology for manufacturing paper/cardboard cups in the shape of a truncated cone are presented.

The article is dedicated to the study of pentagonal castles in early modern Halychyna, their classification, and comparative analysis. It describes methodological challenges related to the specificity of source materials and the lack of targeted research. The preserved monuments (Berezhany, Pniv, Chortkiv, and Brody Castles) are examined with a focus on their location, structural features, and the building materials used.

The paper investigates computer diagnostic systems, their architectures, methods, and algorithms used in their work to diagnose cancer, including breast, lung, brain, and other tumors.

Traditional and neural network methods for image segmentation and classification are analyzed and compared, and diagnostic tools in medicine are analyzed.

The key approaches to medical image processing are investigated, in particular, the analysis of segmentation methods based on U-Net networks and classification using convolutional neural networks.

The article explores the process of creating interfaces for security systems in apartment buildings using the persona method and user classification through the random forest method. The persona method allows for segmenting users based on demographic, psychographic, and behavioral characteristics, providing deeper insight into the needs of different groups and enabling the creation of personalized interfaces for effective interaction with the system. The random forest method is used to select key criteria that influence user classification and optimize the system according to their needs.

This article presents a historical overview of the development of kinetic architecture starting from the 20th century. - the first attempts to implement the futuristic ideas of Thomas Hein, Angelo Invernitz, Buckminster Fuller Jon Friedman, Cedric Price. Two main reasons for the appearance of kinetic architecture are argued. This is the creation of a new architectural environment as a response to changes in the paradigm of world perception - a dynamic and adaptive to new needs environment using the latest computerized and mechanized systems and devices.

With the rapid development of information technology, which is widely used in all spheres of human life and activity, extremely large amounts of data have been accumulated today. By applying machine learning methods to this data, new practically useful knowledge can be obtained. The main goal of this paper is to study different machine learning methods for solving the classification problem and compare their efficiency and accuracy.

The National Cybersecurity Cluster of Ukraine is functionally oriented towards building systems to protect various platforms of information infrastructure, including the creation of secure technologies for detecting deepfake modifications of biometric images based on neural networks in cyberspace. 

This space proposes a conceptual approach to detecting deepfake modifications, which is deployed based on the functioning of a convolutional neural network and the classifier algorithm for biometric images structured as “sensitivity-Yuden index-optimal threshold-specificity”.