Conference Agenda

The phenomenon of missing people has negative consequences for the individuals affected, their families, and society at large. This issue has become increasingly prominent in recent years, partly due to the influence of social media, which has emerged as a vital tool in the search for missing persons. Given the growing importance of addressing this challenge, this research contributes to the expanding field of machine learning applications for social issues. Supervised machine learning models were applied to open data on missing persons in Ecuador between 2021 and 2024 to predict the status of individuals as either "Found" or "Deceased." Using personal, social, and event-specific variables, two models Random Forest (RF) and Support Vector Machine (SVM) were implemented and evaluated. The models were assessed using key performance metrics, including accuracy, precision, recall, F1 score, and confusion matrices, to determine their effectiveness. The analysis revealed that the RF model achieved superior performance on the test data compared to SVM, with an accuracy of 89%, demonstrating its suitability for the dataset. These findings provide valuable insights into the factors influencing the outcomes of disappearance cases, allowing decision-makers to optimize resource allocation, improve search strategies, and support evidence-based decision-making. Predicting the status of a missing person offers an innovative approach to addressing this critical social issue.

The advancement of artificial intelligence has profoundly transformed the creation of modern educational platforms, offering new ways to share knowledge. This review investigates its impact on the design and development of such platforms, highlighting the tools that artificial intelligence makes available. A selection of 2021 articles was obtained through the Scopus platform and manual searches, prioritizing dates from 2019 to the present, subsequently, 35 articles were chosen as they met the established criteria. The results indicated that in the academic field, computer science dominates with 40%, compared to social sciences, which occupy 23.3%, and engineering, with 13.3%. Indicating that artificial intelligence exerts a notable influence on contemporary education, resulting in advances in learning personalization, accessibility, economic effectiveness and overall operational efficiency. In addition, the most commonly employed language types include natural language (NLP), machine learning, and intelligent tutoring systems are the most common. The most prominent countries in generating research on AI in education are China and the United States, evidencing that they have begun to actively incorporate the new technologies. It was concluded that artificial intelligence in educational platforms has been shown to be beneficial in terms of learning personalization, efficiency and accessibility. However, challenges such as data privacy, equity of access, and the need for continuous human oversight are faced. These results underscore the transformative potential of AI in modern education, yet also highlight the importance of addressing its ethical and technical limitations and challenges.

This study examines the implementation of Machine Learning techniques in sales forecasting, highlighting the impact of Big Data integration in transforming business strategies. Large-scale companies like Amazon, Google, and Microsoft are leading this shift, using Machine Learning to enhance the accuracy and efficiency of trend forecasting, thereby strengthening their competitiveness. However, small and medium-sized enterprises (SMEs) face significant challenges in adopting these advanced technologies due to limitations in infrastructure and expertise, restricting their ability to leverage predictive sales analysis. Predictive analysis allows for demand forecasting and process improvement, but SMEs encounter obstacles when trying to implement complex techniques such as Random Forest or neural networks, primarily due to data complexity and variable selection. This study evaluates the main Machine Learning techniques used in sales forecasting and the specific challenges SMEs face. The methodology follows PRISMA guidelines and uses the PICO framework to organize the search in the Scopus database. The analysis addresses strategies applied by large corporations and the barriers SMEs face in implementing similar technologies. Additionally, technological solutions that improve sales forecasting efficiency and accuracy are explored, overcoming obstacles such as scalability and data quality. This approach provides a comprehensive overview of the current state of research in sales forecasting, highlighting challenges and proposing strategies for SMEs to adopt Machine Learning and Big Data, helping to reduce the competitive gap with large companies.

Chatbots play a crucial role in business digital transformation, offering efficient solutions for customer interaction and process optimization. This study provides an overview of intelligent chatbots for customer assistance. Additionally, it focuses on developing a customized chatbot for a fresh produce company to enhance customer service and expand its retail business. A system was implemented using Python, OpenAI's API with the GPT-3.5-turbo model, and libraries such as LangChain and Gspread to process queries, generate contextual responses, and record sales and complaint data. The results showed a 92% accuracy rate in responding to business-specific and general queries. A comparison with chatbots from leading companies highlighted strengths in generating personalized responses but also revealed challenges in identifying user intent. Future improvements focus on integrating advanced natural language processing models, applying continuous learning techniques, and incorporating multimedia capabilities to enhance contextual understanding and adaptability. In conclusion, chatbots provide a valuable opportunity for businesses, particularly SMEs, to optimize operations and enhance customer satisfaction in a highly competitive digital landscape.

Artificial intelligence (AI) has advanced significantly in medicine, impacting the detection and treatment of diseases by storing data and analyzing repetitive patterns. Currently, medical diagnoses often involve long waiting times and, in some cases, can generate false results, further prolonging the wait and negatively impacting treatments. The study systematically reviews the literature on the impact of AI on the diagnosis and treatment of diseases and health conditions. Initially, 1211 articles were obtained by searching the Scopus database, then, applying the inclusion and exclusion criteria, 14 articles most relevant to the study were identified. At the methodological level, the guidelines established by the PRISMA method were applied. The results show that AI can provide diagnoses with an accuracy of 84.7% to 98% and specificities of 50% to 100%. In addition, AI has significantly improved the accuracy of medical diagnoses, reducing the need for unnecessary procedures and enabling continuous monitoring of patients through IoT devices. This facilitates the work of healthcare professionals and contributes to more efficient and effective management of diseases and health conditions.

This report describes a project in which Machine Learning technology is used to predict the propensity for e-commerce consumption of a banking entity's customers. The objective of the project is to increase two key business indicators: the percentage of clients who consume digitally in the month and billing, that is, the total amount consumed by clients; To this end, Machine Learning models were developed to predict which customers are the most prone to e-commerce consumption and which are the least prone, thus enabling the business to use this valuable information to redesign, improve and optimize its products. incentive strategies and launch campaigns to clients.