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.
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 successful deployment of solar energy systems necessitates accurate forecasting of electricity production by photovoltaic power stations (PPS) to ensure the stable operation of power supply networks. This requirement stems from the need to maintain a real-time balance between electricity generation and consumption, which is achieved through the implementation of complex hierarchical control systems governing available energy sources.
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.
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.