The development of a recommendation module for a system for selecting measures in hazardous natural situations is described. An overview of software products aimed at developing adaptation measures to climate risks is provided. A review of methods for automating text classification, document processing, and data structuring is conducted. A multi-agent approach is proposed for the development of the module. The architecture and functional capabilities of the system's agents are described. Their performance is evaluated, and methods for improving the quality of query classification to increase it are used. Large language model technology is used for automatic text analysis. The knowledge base includes 210 case studies from 12 countries, covering situations such as droughts, floods, and heat waves, as well as corresponding adaptation measures such as constructing drainage systems and introducing drought-resistant crops. A user interface for interacting with the agents has been developed. An example of how recommendations are generated is provided.
This article is about the development of a fuzzy cognitive map using a local large language model, and the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor and a fuzzy cognitive map is trained and evaluated.
This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, referred to as APEX-LLM, which is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education.
This work introduces a trust based adaptive reranking model- ATM (Adaptive Trust Model) that allocates computational resources according to file level uncertainty, instead of assigning a fixed number of reranker calls per query, which focuses computation only where ranking confidence is low.
Jenny Kalaiarasi.S· Journal of Intelligent Decis...· 0 citations
This work designs a workflow that prompts LLMs to output elements of an influence diagram and resolves issues through verification and regeneration and applies LAMDA to discussions by groups of disease control experts on a hypothetical pandemic to demonstrate its real-world applicability.
This paper proposes TMCAS, an efficient large language model-assisted topic modeling framework for civil aviation safety reports that achieves superior clustering and interpretability while substantially reducing inference cost compared with document-wise LLM baselines.
It is concluded that SIRSL is functionally viable and has the potential to make systematic reviews more integrated, organized, and traceable, however, further validation across different research fields and documentary datasets is still required.
A. Santos· Revista de Estudos Interdisc...· 0 citations
