Conference Agenda

This Systematic Literature Review (SLR) aimed to analyze the applications of machine learning (ML) in the security of the Industrial Internet of Things (IIoT ), identifying current advances, challenges and gaps. To structure the research questions, the PICO methodology was first used, which allowed the review to be oriented towards risks, applied strategies, comparisons with traditional methods and improvements achieved. Subsequently, the PRISMA protocol was applied for the process of selection and refinement of studies, obtaining a total of 31 scientific articles from the Scopus and Web of Science. The results show that ML has significantly improved the detection of threats such as ransomware, zero-day attacks and APTs, outperforming traditional strategies in accuracy, adaptability and efficiency. Strategies such as neural networks, federated learning, hybrid models and edge architectures were identified. However, limitations such as poor validation in real environments, lack of interpretability and vulnerability to adversarial attacks persist. In conclusion, machine learning represents a key advance in the protection of IIoT infrastructures, although further applied research, development of explainable solutions and adoption of common standards are required to strengthen its effective implementation.

This systematic literature review (SLR) aims to
analyze the impact of the use of intelligent systems in
preliminary diagnosis and appointment management in
healthcare settings with low technological adoption. The PICOC
method and the PRISMA protocol were applied, selecting 26
relevant studies from an initial total of 658 articles. The results
show that AI significantly improves diagnostic accuracy,
optimizes appointment scheduling, and promotes equity in
access to care, even in settings with limited infrastructure. Six
key types of solutions were identified, including predictive
models, automated triage systems, and conversational
assistants, which are more efficient and adaptable than
traditional methods. The analyzed technologies have been
shown to reduce bias, accelerate clinical processes, and expand
access in rural or low-tech areas. In conclusion, this SLR
validates the transformative potential of AI in decentralized
healthcare, provided it is implemented with ethical criteria,
contextual governance, and adequate staff training.

The growing need to optimize processes in the industrial sector has driven the adoption of new approaches based on data analysis, systematization, and interpretation. This systematic literature review aims to analyze how machine learning and the DMAIC method have been developed to improve production processes in the industrial sector. To this end, the PICOC method was used to identify search keywords and questions for subsequent analysis. The search was conducted in Scopus and Web of Science, and inclusion and exclusion criteria were applied. The PRISMA method was used to systematize the screening system, and 42 articles were selected. The results show a decrease from 16,066 to 119 DPOM and an accuracy of 98.8% in the coefficient of determination (R²) by the K-Nearest Neighbors (KNN) model. Advantages such as process standardization, fault prediction, and quality improvement were identified. Despite this, organizational, technical, and social challenges were also reported, such as organizational resistance to change, lack of trained personnel, and the need to ensure data security. It was concluded that the integration of Machine Learning with DMAIC is an effective strategy for continuous improvement, provided that it is accompanied by technologies such as IoT and sensors, as well as the proper use of tools such as FMEA, VSM, and control charts.

This study evaluated the effect of digital support provided by the Padlet platform on the mathematics learning of students enrolled in the Linear Algebra course in the first cycle of the Electrical Engineering program. Two non-equivalent groups participated: a control group (2024A) with 40 students who received standard teaching and an experimental group (2024B) with 40 students who performed collaborative tasks using Padlet. Mathematical achievement was measured from three perspectives: concept learning, algorithm learning, and the degree of mathematical thinking, using a reliable and validated questionnaire consisting of 15 Likert-type items. The data collected from the post-test results show statistically significant differences (p < 0.05) in all dimensions and in favor of the experimental group. The use of Padlet was important for students' active learning, visual learning, and reflection on the steps, three components that are important when learning abstract material. Asynchronous digital tools such as Padlet, when integrated into pedagogy, can improve mathematical learning in higher education.

Commercial architecture seeks to balance functionality, aesthetics, and user experience in commercial spaces, where the application of innovative strategies is essential to optimize visitor interaction and satisfaction. This systematic analysis, aimed at identifying the architectural elements that enhance the user experience in shopping centers, evaluated scientific studies published between 2019 and 2024 in the academic repositories Scopus and Web of Science, using the PRISMA methodology, selecting 25 relevant studies from an extensive set of 4,070 publications. The results indicate that in 2021 and 2023 there was significant scientific production in this field, with a higher concentration of publications in China, where commercial development is high. Furthermore, lighting, spatial design, and the incorporation of natural elements were identified as key factors in improving user perception and comfort in commercial environments. Regarding the implementation of these strategies, the literature highlights a 36% increase in user satisfaction in commercial spaces with an optimized design. It is concluded that the integration of user-centered architectural approaches contributes to increasing the functionality and attractiveness of commercial establishments, promoting a more comfortable and efficient experience.

In the absence of an adequate technological solution, catalog management processes were manual and inefficient, making it difficult to consult and update them. To solve this problem, a web-based catalog management system optimized with parallel processing was implemented, with the aim of improving operational efficiency, data accuracy, and employee satisfaction. The study was applied, with a quantitative approach and pre-experimental design, and involved 32 employees directly involved in catalog management.

Key indicators such as system response time, data update accuracy, operating cost reduction, and user satisfaction were evaluated. The results were highly positive: response time was reduced by 69.95%, data update accuracy improved by 20.25%, operating costs decreased by 32%, and user satisfaction increased by 97.93%. These advances reflect a notable improvement in efficiency, reliability, and workplace well-being.

The agile Scrum methodology was essential for flexible and effective management, while validation with users and experts strengthened the usability and technical robustness of the system, surpassing results obtained in previous research.