Analysis of integrated real-time decision support systems based on neural networks and low-structured data

The study aimed to analyse and substantiate effective methods for analysing inefficiently structured data using neural networks to provide operational decision support in complex environments. The focus was on the use of artificial neural networks to analyse inefficiently structured data, such as sensor streams, to ensure efficiency, accuracy and adaptability in a dynamic environment. The research is aimed at creating innovative models and technologies that will improve the efficiency of management in complex situations, such as emergency response, process automation in critical industries and decision-making based on predictive analytics. The study investigated conceptual approaches to the development of integrated real-time decision support systems based on the analysis of poorly structured data using neural networks. The study proposed methods of adaptive learning that allow neural networks to process data efficiently in the face of constant changes. The research methodology included modelling a real-time architecture using a microservice approach and streaming data processing platforms such as Apache Kafka and Apache Flink. The study highlighted the role of neural networks in processing streaming data, in particular, convolutional networks for processing visual information, recurrent networks for sequence analysis, and transformers for multichannel analysis. Architectural solutions were developed that allow the processing of large amounts of data with minimal delays, ensuring the accuracy and adaptability of systems. The study presented approaches to the implementation of adaptive training of neural networks that minimise the risks of losing model relevance in a dynamic environment. The use of modern technologies, such as artificial neural networks, adaptive learning and integration with the Internet of Things, was used to create effective systems for rapid response to emergencies. The proposed methods help increase the efficiency of management in difficult conditions and create new prospects for innovation in various industries