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.
A system based on the conceptual framework Node of Knowledge (NOK) enables the recording of natural language sentences and questions in the NOK relational database, as well as the retrieval of answers to those questions. Large Language Models (LLMs) are designed for the same purpose: to provide answers to questions. Therefore, comparing large language models with a system built using the Node of Knowledge conceptual framework is an important research question. This paper addresses this by comparing these two systems. Among large language models, GPT was selected for comparison as it is one of the most widely used models. The comparison was conducted in two parts. First, the process models of the two systems were compared. Second, an analysis was performed on the answers produced by the NOK-based system and by ChatGPT, representing large language models, for a selected natural language sentence and set of questions. All comparison elements revealed similarities and differences between the systems, which are presented in this paper.
Abstract The purpose of this paper is to present 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. In the analyzed company, the treatment of sales related documents was made manually, although these documents represented the starting point of several important operational processes. According to the internal analysis, approximately 11,000 documents are processed every year, and each of them required, before the implementation of the system, about 10 minutes of human work for identification, classification and ERP registration. Thus, the manual process consumed around 1,833 man-hours per year and had a measured error rate of 4.6%, errors that were reflected in production delays, missed commercial opportunities and administrative frictions. maAIGENT was built as a hybrid architecture, in order to combine the reasoning capacity of a LLM with the security requirements of an industrial client. The local application is deployed inside the customer’s LAN and performs the operations that are connected with sensitive data and communication with the ERP system. The cognitive component of the system uses Azure OpenAI GPT-4o, accessed through a private subscription, while only the sanitized textual payload is sent outside the local environment. In this way, the original emails, attachments and operational records remain under the control of the organization. An important element of the implementation is represented by the explainability of the classification. For each document, the model returns not only the class, but also a short rationale in Romanian language, based on the content of the email and on the text extracted from attachments. This explanation was used to support faster human validation and to make the automatic decision more transparent for the operators. The results obtained after the stabilization of the system show a reduction of the average processing time from about 10 minutes to 1–2 minutes per document, which represents approximately 90% reduction and an estimated saving of 1,650 hours per year. This reduction includes both the automatic classification and the transmission, through API, of the routing decision to the ERP system, replacing the previous end-to-end manual workflow. In the validation made on 650 operational documents, maAIGENT obtained 98.62% model-only accuracy and a macro-F1 score of 0.986, compared with Human-in-the-Loop validated labels, while human corrections were necessary in 1.38% of the cases.
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.
Jasur Davletov, G. Toirova, Mukhriddin Fayziyev et al.· International Conference on...· 0 citations
During the recruitment process, it is possible for CVs to appear well-organized. However, it is not always straightforward to compare them. The same competence may be denoted by different designations, and the levels of competence are not universally employed in the same manner. Natural Language Processing (NLP) methodologies can extract these data points; however, ensuring the consistency of this data across multiple CVs remains a challenge. In a multitude of cases, the comparability of two profiles remains ambiguous. In the present study, an ontological approach is adopted to solve this issue. The concept under discussion is that of the extraction of entities from CVs and their subsequent representation in a more structured form, utilizing RDF and an ontology aligned with ESCO—the multilingual classification of European Skills, Competences, and Occupations. Subsequently, the rules of SHACL are applied to verify the semantic coherence of the data; the validated data are transmitted to a model for classification. At this stage, the dataset becomes smaller, but semantically cleaner, more traceable, and enriched with validation indicators that can be used by the classification model. The proposed system is implemented as a set of microservices. A Spring Boot component coordinates the flow, whilst the Python services, implemented using Python 3.10.12 are responsible for the primary processing stages including extraction, validation and classification. A same-corpus ablation was conducted to separate ontology-guided profile selection from the contribution of the validation-derived quality features. On the same 35,770 filtered CV–job pairs, adding these features increased external benchmark accuracy from 0.794 to 0.809, recall from 0.760 to 0.865, F1-score from 0.749 to 0.786, and ROC-AUC from 0.881 to 0.887. A p-value of 0.00540 paired with a 1.54 effect ratio from McNemar’s test showed a statistically significant paired difference between the two configurations. However, precision decreased from 0.739 to 0.720 while Average Precision compressed from 0.851 down to 0.844. 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.
V. Anghel, Theodor Borangiu, S. Raileanu et al.· Applied System Innovation· 0 citations
The exponential growth of textual data, in the form of corporate documents, reports, and research papers, in the current digital era has increased the need for intelligent systems that can automatically comprehend documents. Analysing these documents by hand is ineffective and time-consuming. In order to extract valuable insights from documents, this study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches. Users can submit text or PDF files to the system, which then extracts content, generates succinct summaries, identifies keywords, and enables interactive question-answering. Python is used to build the architecture, which is then serverless deployed on Amazon Web Services (AWS) utilising Amazon S3 and Amazon EC2, and for the question-answering system, GEMINI is used. By reducing reading time and providing immediate access to pertinent information, the suggested solution increases productivity. It is affordable, scalable, and suitable for business, education, and research.
Radhika Sharma, Devraj Gautam· Revolutionary Advances in Co...· 0 citations
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.