OPTIMIZATION OF THE INFORMATION SYSTEM IN THE AUTOMATED PACKAGING PROCESS OF PRODUCTS USING NEURAL ALGORITHMS

This study investigates the optimization of information systems in automated packaging processes using neural algorithms. The research focuses on integrating artificial neural networks into data acquisition, processing, and decision-making modules to enhance system performance in real-time industrial environments. By leveraging machine learning techniques, the proposed approach improves predictive accuracy, fault detection, and process control across multiple stages of packaging operations. The study also examines the role of neural models in handling large-scale heterogeneous data generated by sensors and production equipment. Experimental analysis demonstrates that neural algorithm-based systems outperform traditional rule-based methods in terms of adaptability, efficiency, and reliability. The findings confirm that the application of neural networks contributes to reducing operational costs, minimizing errors, and improving overall productivity in automated packaging systems, supporting the development of intelligent and sustainable manufacturing solutions.