Conference Agenda

Esta Revisión Sistematica de Literatura esta enfocada en el aprendizaje de las optimas buenas practicas de implementar o realizar un cambio tecnologico en las empresas PYMES. Con el fin de traer mejores cambios dentro del mercado adaptandose a la competitividad comercial.

Limited healthcare accessibility represents a major challenge for healthcare systems globally, with more than 70% of facilities in developing countries lacking adequate technological infrastructure. The limited evidence on the effective implementation of generative AI-based chatbots exacerbates this problem, limiting service optimization. This systematic review analyzes how generative AI chatbot platforms optimize healthcare. A review was conducted using PICO components and the PRISMA protocol, selecting 40 articles from Scopus, Web of Science, and EBSCOhost (2020–2024). The results indicate that limitations were addressed through relational agents, gamified chatbots with OMO strategies, culturally appropriate adaptive systems, and empathetic frameworks. Effectiveness was validated through experimental studies, demonstrating improvements of 95% in continuous availability, 90% in geographic coverage, 85% in response time, and 78% in adherence versus the traditional 60%. Mental health emerged as the most effective sector, with 30% of successful implementations. In conclusion, generative AI chatbots are effective tools for overcoming traditional access barriers, with variations by sector. The quality of implementation outweighs the specific type of chatbot, with cultural adaptation and customization determining sustainable success. Large-scale studies in low- and middle-income countries and integration with medical IoT technologies are suggested.

Earthquakes cause significant losses, which demands more efficient strategies for early detection and damage assessment. Given the limitations of traditional methods, this Systematic Literature Review (SLR) aimed to analyze Machine Learning (ML) models applied to seismology to strengthen urban seismic risk management. A rigorous search was conducted in Scopus and Web of Science, yielding 335 articles. After applying inclusion/exclusion criteria and filters, 32 final articles were selected. The results revealed that algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, Long Short-Term Memory networks (LSTM), and Artificial Neural Networks (ANN) show great potential in improving the accuracy of early detection of seismic events (P-waves, hypocentral parameters) and in the estimation of structural damage, thereby optimizing response efficiency. However, challenges were identified regarding data availability and quality, as well as model generalization. In conclusion, ML models are a promising tool for urban seismic management, and it is crucial to address existing barriers and explore future research directions to maximize their impact.

This systematic literature review (SLR) analyzes the impact of machine learning on the early detection of learning difficulties in school settings. Using the PICO methodology and the PRISMA protocol, four key questions were articulated regarding types of difficulties, applied algorithms, comparison with traditional methods, and intervention improvements. A search of the Scopus database identified 306 documents, from which 36 relevant studies were selected after applying rigorous inclusion and exclusion criteria. The findings show that algorithms such as Random Forest, SVM, deep neural networks, and ensemble models allow the identification of complex patterns in academic, behavioral, and neuropsychological data, far exceeding the accuracy and agility of conventional methods. The main applications include the detection of dyslexia, dysgraphia, dyscalculia, and ADHD, as well as the prediction of academic risk and pedagogical personalization. Furthermore, it is observed that sensory and adaptive artificial intelligence tools strengthen educational inclusion for students with cognitive disabilities. However, significant challenges remain, such as the need for high-quality data, limited validation in real-life school settings, and the poor interpretability of some complex models. In conclusion, the use of machine learning represents an effective solution for improving the early detection of learning difficulties, overcoming the limitations of traditional approaches and opening up new opportunities for more accurate, timely, and inclusive educational interventions.

_{The integration of advanced analytics in public sector decision making is emerging as a key strategy to improve institutional performance and service delivery. The general objective is to explore how advanced analytical techniques are used in decision-making processes within public management. To this end, 53 articles published in the last five years, selected from databases such as Scopus and Web of Science, were reviewed. The PICO strategy and PRISMA methodology were applied to organize the findings around three main points: the problems identified, the techniques employed and the benefits obtained. Among the main challenges identified were: strategic decision making without analytical support, operational inefficiency, limited crisis response capacity, limited focus on citizen equity and weak data governance. The most commonly used techniques were machine learning, advanced statistical analysis and text mining. The integration of advanced analytics in public sector decision making is emerging as a key strategy to improve institutional performance and service delivery. The general objective is to explore how advanced analytical techniques are used in decision-making processes within public management. To this end, 53 articles published in the last five years, selected from databases such as Scopus and Web of Science, were reviewed. The PICO strategy and PRISMA methodology were applied to organize the findings around three main points: the problems identified, the techniques employed and the benefits obtained.}