The Use of Adaptive Artificial Intelligence (AI) Learning Models in Decision Support Systems for Smart Regions
R.Yu. Buchuk, Pavlo Fedorka, Mykhailo V. Klymenko, Andrii PETRUSHYN, Fedir Saibert
Abstract
The purpose of this study is to analyse the effectiveness of implementing adaptive AI learning models in decision support systems to optimise the functioning of smart regions. The study provides a detailed examination of the application of machine learning algorithms, deep learning, and reinforcement learning across various sectors, such as urban management, energy resources, and security. The results revealed that the implementation of these models enhances the efficiency of urban system management, reduces costs, and increases the flexibility of decision-making processes. In particular, adaptive models in energy resource management optimise decision-making processes, leading to more rational resource use and substantial cost reductions. In the security field, adaptive AI models show improvements in predicting and preventing incidents, ensuring more reliable and stable system performance. Moreover, the results include the implementation of adaptive models based on programming languages such as TypeScript and JavaScript. The study demonstrated that the use of TypeScript reduces errors and improves system scalability due to strict typing, as shown in the implementation of a reinforcement learning model. Meanwhile, the use of JavaScript enabled the effective adaptation of models to new data through dynamic updates of regression coefficients, leading to improved prediction accuracy.© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.