machine learning

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.

The paper uses modern artificial intelligence methods to investigate models and methods for determining grape disease. The existing methodologies for classification and recognition by images of grape diseases using neural networks are analyzed. Several problems for improving recognition results are highlighted.

This study explores the correlation between the popularity of open-source repositories and their quality, as assessed using static code quality metrics. The primary focus is on defining key indicators for two distinct paradigms, namely functional and object-oriented programming, and developing a code search method to systematically process repositories retrieved during the search process.

The article examines the ways artificial intelligence is influencing the penetration testing procedure. As technology advances and cyber threats grow more com- mon, conventional testing methods are insufficient. Artificial intelligence aids in automating processes like vulnerability detection and real-world attack simulation, leading to quicker, more precise results with reduced dependence on human input. Machine learning is a game-changer in identifying hidden security flaws by analyzing past attacks and abnormal patterns.

The article presents possible strategies and approaches to address the growing cybersecurity threat landscape, new trends and innovations, such as artificial intelligence and machine learning for cyber threat detection and automation. The paper presents well-known machine learning classifiers for data classification. The dataset has been taken from a report by the Center for Strategic and International Studies. The presented model accuracy assessment study has been significant variation among algorithms based on different network intrusion detection systems.

The article explores the application of neuro- symbolic approaches utilizing artificial neural networks for diagnosing neurological disorders among individuals with autism spectrum conditions. It demonstrates how these networks can identify and enhance distinctive strengths, such as advanced pattern recognition and systematic reasoning, facilitating their integration into professional environments.