METHOD OF FORMING AN AI PLATFORM FOR EMBEDDED COMPUTING SYSTEMS USING COGNITIVE TECHNOLOGIES

Abstract

The article considers the procedure for forming a cognitive artificial intelligence (AI) platform that serves as the foundation for the development of embedded computing systems. It is emphasized that the creation of such a platform is a complex and strategically important process, since it involves the analysis of vast amounts of poorly structured and unstructured data, which significantly complicates information processing. The authors argue that the efficiency of applying modern information technologies depends not only on the volume and quality of available information but also on the level of interaction between different technological components. This interaction predetermines the network orientation of the platform, while in the system environment, tools that integrate information resources from diverse fields of knowledge must be applied.

To enhance the efficiency of processing information flows, the article proposes the use of cognitive information technologies incorporating elements of artificial intelligence. A special emphasis is placed on the role of monitoring information processes in embedded computing systems as a basis for generating output data. Within this framework, a new method for forming an AI platform using cognitive technologies is presented. This method ensures the structuring of information processes, the creation of ontologies, and the ranking of these processes to determine the most rational option for their processing.

The scientific novelty of the proposed approach lies in integrating cognitive models into embedded systems, thereby enabling adaptive decision-making and more flexible responses to dynamic environments. The practical significance is in the potential application of the AI platform to a wide range of embedded systems, including information-analytical, industrial, and control systems, where high-speed data processing and intelligent analysis are crucial.

The article concludes that the proposed method provides a systematic basis for developing embedded computing systems with higher levels of autonomy and cognitive adaptability. Future research will focus on practical implementation, testing the AI platform in real embedded environments, and expanding its use for interdisciplinary applications, thus contributing to the advancement of intelligent computing technologies.