Latest Communications of International Proceedings articles

The growing prevalence of infostealer malware poses a significant threat to both individual users and organizations, leading to the theft of login credentials, cryptocurrency wallets, multi-factor authentication tokens, browsing history, and other sensitive data. Despite the widespread use of the Windows Data Protection API (DPAPI)…

Progressive urbanization and increased air pollution emissions pose a significant challenge for modern safety engineering, especially in terms of forecasting and mitigating environmental risks1. This paper presents a machine learning-based approach to modeling PM2.5 particulate matter concentrations using open environmental and meteorological data from the…

The rapid growth of distributed ledger technologies (DLT) and new regulatory obligations (GDPR, DORA, NIS2, AI Act) are forcing organisations to rethink how they govern data as a strategic and auditable asset. However, existing data-governance models and standards only partially address trust, accountability and interoperability…

Optimization in job-shop production systems is a very complex task, requiring simultaneous analysis of many factors. This article presents just this type of production in which the problem was to schedule job-shop tasks with significant setup times. In this case, the use of reinforcement learning…

The high competitiveness and rapid pace of development of the manufacturing industry requires companies to constantly improve, both in terms of efficiency and quality of production. It is these parameters that directly affect not only the reputation of the company, but also the costs it…

The transition to Industry 4.0 has continued to evolutionise maintenance strategies, shifting from reactive approaches toward new, data-driven predictive maintenance systems. This study evaluates three machine learning approaches—Random Forest, XGBoost, and Long Short-Term Memory networks—for equipment failure prediction using live sensor data from rotating machinery.…

Forecasting production demand is one of the key elements of effective supply chain management in small and medium-sized enterprises. The paper presents a comparative analysis of different neural network architectures used in production demand forecasting. The study includes the evaluation of neural networks: unidirectional (MLP),…

The growing prevalence of diet-related diseases such as obesity, diabetes, and cardiovascular disorders is closely linked to the excessive consumption of unhealthy food products and overeating. Identifying harmful food items based on their chemical and nutritional composition has therefore become an important research direction in…

This study explores the application of machine learning methods to predict football (soccer) match outcomes. Football, as a highly dynamic and data-rich sport, provides a valuable source of information for predictive modeling. The research focuses on evaluating and comparing the performance of several machine learning…

This study investigates the use of machine learning methods to assess and predict the level of athletes’ preparation for e-cycling competitions based on their training data. A three-month dataset of activities collected from selected competitors of the Polish Open E-cycling Championships was processed to extract…