An Ambient Intelligence responds to user requests based on several contexts. A relevant context is related to what has happened in the ambient; therefore, it focuses a primordial interest on events. These involve information about time, space, or people, which is significant for modeling the context. In this paper, we propose an event-driven approach for context representation based on an ontological model. This approach is extendable and adaptable for academic domains. Moreover, the ontological model to be proposed is used in reasoning and enrichment processes with the context event information. Our event-driven approach considers five contexts as a modular perspective in the model: Person, temporal (time), physical space (location), network (resources to acquire data from the ambient), and academic events. We carried out an evaluation process for the approach based on an ontological model focused on (a) the extensibility and adaptability of use case scenarios for events in an academic environment, (b) the level of reasoning by using competence questions related to events, (c) and the consistency and coherence in the proposed model. The evaluation process shows promising results for our event-driven approach for context representation based on the ontological model.

The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain.

Social networks have become an important source of information in recent years, offering a platform for visualizing people's opinions on various industries and research topics. These opinions may be expressed in Spanish, providing fresh and updated insights that can be analyzed using computational approaches. This research aims to understand the sentiment conveyed in ideas or opinions within Spanish text. As a result, various methods are evaluated to identify the most effective approach. Computational techniques, especially Natural Language Processing (NLP), have become essential for automating this analysis. While traditional machine learning algorithms have been employed for sentiment analysis, the rise of large language models since 2017 has introduced significant challenges in assessing their impact on various NLP tasks. This paper presents a comparison between machine learning approaches and calibrated large language models for sentiment analysis in Spanish texts, measuring their performance. The calibration process consists of two stages: a coarse calibration using exploratory methods and a fine calibration that involves an algorithm for searching hyperparameter values. The evaluation process showed that the most effective machine learning approach combines unigrams and bigrams with the Bayes algorithm, along with exploratory parameter tuning and feature selection, achieving an accuracy of 72.72% and an F1 score of 72.81%. Furthermore, by applying LoRA, a technique that optimizes the fine-tuning of pre-trained models, it was found a best model that we call and store as twitter-xlm-roberta-base/SentUAM, which reached an accuracy of 71.99% and an F1 score of 71.78% in the sentiment analysis of Spanish texts.

While social media platforms are primary vectors for misinformation, automated detection systems remain largely confined to English. This paper presents a transferable, three-stage framework for fine-tuning transformer models to detect domain-specific deceptive content in Spanish. The pipeline comprises: (1) corpus unification, merging fragmented datasets into a 61,674-article resource mapped into three classes (Real, Fake, Satire) to prevent stylistic confounding; (2) systematic model optimization, extensively benchmarking classical metaheuristics against eight transformer architectures (including mBERT, XLM-RoBERTa, and BETO) using strong regularization to mitigate overfitting; and (3) production deployment, encapsulating the optimized model as a containerized web application for real-time inference. Through rigorous experimentation, the Spanish-specific BETO encoder emerged as the strongest model for this task, achieving 89.18% overall accuracy. The model attains a near-perfect in-source F1-score on the satire class; however, a strict source-held-out test reveals that this performance is highly source-dependent—recall on satire from an unseen outlet drops to 0.08—indicating that single-source class construction leads the model to recognize the source rather than a generalizable category. We report this finding as a central methodological result: corpus design, and in particular the source diversity of each class, is the primary determinant of whether the framework generalizes. Adversarial robustness tests using named-entity masking and typo injection provide complementary evidence on the model’s reliance on semantic versus surface cues. The methodology is designed to be adaptable across domains: by substituting the training corpus, the same framework may in principle be retargeted to other digital threats, such as investment scams and phishing, provided that suitable labeled corpora are constructed and validated for each new domain. The complete framework, dataset, and application are released as open-source resources to support reproducible research and practical countermeasures against online misinformation.

In this paper, we delve into the techniques used to extract information from medical texts written in Spanish. Our study focuses on fine-tuning Large Language Models by training them on Spanish medical texts and adjusting various hyperparameters. We also explore traditional machine learning algorithms like support vector machines, decision trees, and nearest neighbours. Our analysis aims to evaluate the tool's ability to identify entities and relationships between them. Our results show that the support vector machine algorithm outperformed Large Language Models in entity identification, achieving a 78.72% F1 score compared to 68.04%. However, Large Language Models demonstrated superior performance in relationship identification, achieving a 57.15% F1 score compared to 35.5% for machine learning algorithms.

An Intelligent environment can respond to the necessities of the users according to the context, this is so that the individuals can have the ideal climatic conditions in order to go about their activities, these conditions are related through a series of special norms. The events that we describe in this paper are in relation to the events of the environment (temperature, humidity, brightness, and presence), also involved are different variables like time, space, or person, such are important in order to be able to model what is occurring in a determined place. In this project, we propose a personalized ontological design for the academic dominion. The ontological model is utilized for the identification of environmental events according to the data acquired from the environment through the simulation of intellectual agents. Also, our ontological model is used to rationalize with the information obtained from the identified events. The model of ontologies based on events considers four contextual questions like a perspective modular: person, seasonality (weather), spatiality (location), network (resources in order to acquire environmental data) and event (academic events). And the detector is based on rules obtained from the standards of optimum climatic conditions of a physical space.