Analysis of current trends in control automation with a focus on artificial intelligence, neural networks and machine vision

Abstract

Modern trends in the field of automation management show a significant increase in interest in integrating artificial intelligence (AI), neural network, and machine vision technologies. The main objective of the study is to analyze the latest achievements in applying these technologies to enhance management efficiency in various industries such as manufacturing, transportation, healthcare, and others. To achieve the research goal, a systematic review of scientific literature was conducted, including articles, reports, and data from leading databases. Key approaches to implementing artificial intelligence, neural network architectures, and machine vision algorithms were analyzed. Particular attention was paid to comparing theoretical developments with practical implementations in real-world conditions. The prospects for integration with existing automation systems were also considered. The analysis revealed that AI and neural network technologies provide vast opportunities for modernizing management systems. For example, machine vision algorithms are successfully used in quality control systems in manufacturing, vehicle autopilots, and medical diagnostics. Key barriers to their widespread adoption were identified: the lack of high-precision data for training, the complexity of interpreting neural network models, and issues of data ethics and confidentiality. Examples of successful pilot projects demonstrating the effectiveness of proposed solutions were presented. The results confirm a high potential for the use of these technologies but require further studies to optimize their performance, reduce computational costs, and improve adaptability. Possible ways to address issues related to ethical aspects and the resilience of automation systems were also examined. Thus, the study confirms that integrating artificial intelligence, neural networks, and machine vision technologies significantly enhances the capabilities of automated management systems. However, their widespread adoption requires systematic efforts aimed at addressing current limitations and ensuring security.