The paper addresses the problem of anomaly detection in network traffic and proposes a comprehensive solution to enhance the level of cybersecurity for organizations of various scales. A comparative analysis of existing monitoring and anomaly detection systems has been carried out, including both open-source solutions and commercial products.
Digital transformation of public services in the context of modern information systems and technologies is a relevant area of research, which is due to the growing demands of society for the quality and speed of public services. In the digital economy, artificial intelligence, machine learning and big data processing technologies play an important role, which can significantly increase the efficiency of public administration.
The article explores approaches to forecasting the development trends of the IT market using machine learning methods. The relevance of the research is driven by the high dynamics of the digital economy, rapid technological changes, and the need for scientifically grounded analytical tools in the IT domain. The purpose of the study is to develop a forecasting model capable of identifying patterns in socio-economic, technological, and behavioral indicators that determine the state and prospects of IT market development.
The article presents the development of an interactive system for recognizing and classifying human hand gestures based on machine learning technologies. A new approach to gesture representation is proposed, combining spatial and temporal characteristics of the location of key points of the hand, which ensures high accuracy, noise resistance, and adaptability of the system to various conditions of use.
Accurate prediction of the remaining driving range in electric vehicles (EVs) is critical for efficient trip planning, reducing the risk of battery depletion, and improving user experience. One of the significant challenges in achieving high prediction accuracy is battery degradation, which gradually reduces battery capacity and impacts the vehicle’s range. This study uses machine learning algorithms to investigate the impact of incorporating battery degradation—expressed through the State of Health (SoH) indicator—into range prediction models.
With the advancement of blockchain technology, Solidity-based smart contracts have become essential for automating and securing digital transactions across various sectors, from finance to supply chain management. These contracts enable decentralized exchanges without intermediaries, enhancing transparency. However, their immutable nature poses security challenges: any flaw in the code becomes permanent, exposing contracts to attacks and leading to financial and reputational losses. This paper provides a comparative analysis of recent machine learning (ML) and deep l
The study investigates mathematical and computer-based modeling of butt welding of galvanized steel strips employed in the fabrication of reinforcing profiles for window frame systems. The motivation of the research lies in the necessity to improve weld quality and stabilize production processes in industrial window manufacturing. The primary aim is to establish predictive models capable of accurately estimating the structural strength of welded profiles from critical welding parameters.
This paper proposes a novel architecture of a multi-agent system and its formal specification for analyzing and adaptively correcting students' learning trajectories using software agents in digital learning environments. The proposed approach integrates artificial intelligence tools, tem- poral logic, and a multi-agent system architecture to ensure personalized adaptation of educational content.
The relevance of the research is due to the growing need to automate the processes of text analysis and correction, in particular for Ukrainian-language content, which is characterized by a wealth of morphological and syntactic structure. Due to the wide range of errors that can occur in texts, from spelling to contextual, there is an urgent need to create systems that can accurately identify errors and offer their correct corrections.
WebAssembly (WASM) has emerged as a compelling and transformative solution for executing high- performance Artificial Intelligence (AI) and Machine Learning (ML) models directly within frontend web applications. Traditionally, AI/ML model deployment has been dominated by backend servers due to significant computational demands, coupled with the performance limitations of JavaScript and the overhead of client-server communication.