Conference Agenda

This analysis is guided by a literature review of various articles with the objective of analyzing the uses of artificial intelligence in the training of engineering students between the years 2022 and 2023. For the search for information, selection and formulation of the inquiry questions, the PICO and PRISMA methodologies were used, of which 225 articles selected from the Scopus database and 4 articles extracted from the AI ​​Elicit of which were 35 articles relevant to the research were excluded because they were not related to the objective and questions or because there was no access. The results highlight a greater incidence of the use of Chatgpt and some articles show a virtual reality reinforced with AI. In addition, positive perceptions regarding the use of AI and the specialties such as courses where it was used are evident.

This paper presents a method to optimize non-medical care in a university health center through the implementation of a chatbot that was trained with natural language processing and the Transformer model. The method consists of collecting data from the user to provide answers that are efficient and in accordance with expectations. The chatbot, designed with an intuitive interface, provides users with access to virtual and online functions. It interprets the data provided by the user in text form, generating valid recommendations according to the requesting user. Care time reduction is achieved by automating routine non-medical tasks, such as appointment management and frequent consultations, allowing healthcare staff to focus on more complex cases. The chatbot does not replace face-to-face medical care, but acts as a support tool to optimize resource allocation. The results obtained showed varied opinions regarding the decongestion and management of the application, with 81.3% having no problems using it, while the rest did. The most frequently reported problems included technical difficulties in interacting with the chatbot and errors in interpreting complex requests. Overall, this approach aims to improve healthcare services through technology to provide personalized and relevant information to users.

Water quality monitoring is essential for the protection of public health and ecosystems. This research used historical data of the physicochemical and microbiological parameters of the Rimac River basin in the city of Lima, Peru, from 2014 to 2021, and proposed a stacking ensemble model with heterogeneous algorithms for the prediction of the water quality index (NSF) in the Rimac River basin/Peru. The results show low values ​​of the mean square error (MSE) and mean absolute error (MAE) of 9.954 and 2.433 respectively. Likewise, a high level of fit with a coefficient of determination of 85.9%. The selection of the prediction model algorithms was based on the detection of stationarity and autocorrelation in the target variable - water quality index. It is concluded that it is necessary to strengthen and use the heterogeneous algorithm to predict the water quality of the Rimac basin. It was developed in a Google Colab environment and Python programming language

This Systematic Literature Review (SLR) analyzes the obstacles to the implementation of artificial intelligence (AI) models in the diagnosis of cardiovascular diseases in Latin America. The results obtained have shown a significant growth in the number of publications in 2024, reflecting a growing interest and recent advances in AI adoption. The main obstacles identified are regulatory barriers (31%), lack of trained experts (25%), high costs (19%), difficulty in clinical validation (12%) and insufficient infrastructure (13%). Likewise, it is facing several key challenges, which are: Inequalities in access to advanced technologies and medical resources limit their adoption, especially in rural areas and disadvantaged communities. The variability and quality of medical data also present significant obstacles, hindering effective integration and analysis. In addition, the lack of adequate infrastructure and trained personnel exacerbates the situation. Finally, regulatory and ethical concerns, along with the need for clear policy frameworks, further hinder the effective implementation of AI in this region.

Artificial intelligence has become a fundamental pillar for business management, its growing relevance forces organizations to adapt and integrate it into their processes, ranging from innovation and operational optimization to improved interaction with customers and prediction of needs in the supply chain. This study aims to perform a critical analysis of the influence and benefits derived from the implementation of AI in companies. The systematic literature review focuses on papers published from 2018, selecting 15 relevant articles. To ensure the quality and relevance of the included research, clear inclusion and exclusion criteria were established, allowing a comprehensive understanding of the relationship between AI and business management. The methodology used follows the guidelines of PICO and PRISMA strategies, which made it possible to filter open access documents. The results obtained validate the objective of the study, showing that the implementation of AI in different business sectors should be done gradually, in synergy with staff training. This approach not only impacts profitability, but also facilitates the path to business maturity. In addition, the countries leading these initiatives, the types of AI most used by organizations, the methodologies applied and the benefits in different business areas were identified. In conclusion, this review reinforces the importance of integrating AI as a key strategic element to drive innovation and sustainability in the global business environment.

The automatic generation of content has transformed the way information is produced and consumed, but it has also posed significant challenges in ensuring its authenticity and reliability, particularly in sectors such as education and media. Differentiating between automatically generated texts and those written by humans is crucial to prevent the spread of misinformation and ensure transparency in the use of these technologies. In this context, this paper proposes an effective approach based on traditional classification models combined with ensemble techniques and advanced Natural Language Processing (NLP) methods, using textual features such as phraseological measures, TF-IDF with n-grams, and perplexity to capture distinctive patterns. The methodology was evaluated on datasets from the COOLING 2025 workshop, including corpora in English, Arabic, and multilingual datasets, covering different sizes and complexities. The Stacking Classifier model achieved an F1-macro of 0.9273 on the large English corpus and 0.9131 on the multilingual corpus, demonstrating its effectiveness in diverse scenarios. Additionally, Logistic Regression and XGBoost achieved perfect performance on smaller and more homogeneous datasets in English and Arabic, respectively. These results highlight the robustness of the proposed approach, which combines key textual features with robust models, offering an effective tool to tackle the challenges of automatic content generation in multilingual and complex contexts

This research aimed to develop a web application incorporating Data Analytics to improve the control of services provided by a construction company in Trujillo in 2024. The construction company faces the need to implement a technological solution to offer higher-quality services and manage reports effectively. The current processes rely on outdated systems that frequently malfunction and lack Data Analytics capabilities. One of the main issues identified was the manual preparation of all reports, which leads to significant time inefficiencies for the company.

The research methodology was applied, with a quantitative approach and a quasi-experimental design. The study population consisted of 30 employees. Data collection techniques included surveys and observation guides, using instruments such as questionnaires and observation sheets. Based on the results, the implementation of a web-based system was proposed to enhance document control within the construction company.