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.
Abstract
This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the star rating of the review -an outcome outside the model's inference scope -with its predicted satisfaction.
Comparisons of large language models with a system built using the Node of Knowledge conceptual framework revealed similarities and differences between the systems, which are presented in this paper.
Martina Ašenbrener Katić, Marina Rauker Koch, A. Jakupović· Zbornik Veleučilišta u Rijec...· 0 citations
The design, implementation and results obtained through maAIGENT, an Explainable AI solution developed for a manufacturing organization, with the role of classifying enterprise communications and supporting their integration with the ERP system, show a reduction of the average processing time and an estimated saving of 1,650 hours per year.
R. (LAZAR), Mircea Florescu· Proceedings of the Internati...· 0 citations
This article presents an intelligent system designed for objective and effective assessment of student assignments in the educational process. The proposed approach has the function of automatic processing and text extraction of documents in PDF and DOCX formats, and evaluates the semantic similarity between assignment conditions and student responses through deep learning models based on the Sentence Transformer architecture. In addition to semantic proximity metrics, the system also analyzes multidimensional linguistic features such as content coverage, internal consistency, and structural connectivity. A probabilistic classification mechanism is implemented to determine the probability of content generated by artificial intelligence. A decision-making system based on fuzzy logic theory is used to combine all assessment components into a single final score, which ensures interpretability and transparency of assessments. The result of this research makes a practical contribution to the field of automated educational technologies and creates a reliable basis for supporting decisions in large-scale educational environments.
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Rather than scaling performance uniformly across the entire evaluation suite, the ontology layer acts as a targeted traceability and semantic refinement filter that contributes information beyond filtered-profile selection alone and produces a metric-dependent change in classifier behaviour at the validation-selected threshold.
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This study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches that is affordable, scalable, and suitable for business, education, and research.
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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.