Conference Agenda

Abstract– This article shows the relationship between digital transformation and work performance in the elaboration of technical-legal files of a State Ministry; it is a correlational research, determining the relationship of the variables, under a quantitative non-experimental design. The study was applied to 86 specialists of a Ministry, taking as a sample 71 specialists, for data collection. The results found show a positive and remarkable relationship between both variables, highlighting that the digitalization of a process allows to improve the quality of information, generates an improvement in decision making and reduces operating times. Digital transformation becomes a central axis that can boost the productivity of a given institution. In addition, it has allowed the identification of dimensions that affect work performance: process automation, cooperative work in digital environments and access to information in real time.

Disease detection in apple trees represents a critical challenge for modern agriculture, involving production losses and excessive use of agrochemicals. This study developed a mobile application that compares two artificial intelligence approaches: convolutional neural networks (CNN) and self-organizing maps (SOM), to identify diseases in apples. Through a comparative analysis of more than 5,000 field images, it was shown that the SOM neural network achieved 94% accuracy vs. 92% accuracy of CNN with processing times of less than 10 seconds, while maintaining greater robustness in varying conditions. Although CNN showed advantages in computational efficiency, its lower accuracy determined the final selection of the SOM architecture for the application. The implemented solution operates on standard mobile devices, offering small producers an accurate tool that reduces post-harvest losses, which represents a significant advance in the democratization of low-cost agricultural technologies.

Organizations seek to improve their operational efficiency by addressing a key challenge in the plastics industry: low equipment efficiency, particularly in injection molding processes. This research analyzes the causes of high mold setup and disassembly times, as well as increased machine downtime due to electrical failures in the injection molding process. It also examines the high rate of defective products due to the temperature instability of the injection molding machine and its performance, which currently operates at an efficiency of only 71.68%. To address this problem, an improvement project is proposed that integrates Lean Manufacturing methodologies such as SMED, Andon, and FMEA, as well as the use of digital twins and statistical process control (SPC). The objective is to optimize the effectiveness of the injection molding machine, measured by the Overall Equipment Effectiveness (OEE) indicator, and improve the company's competitiveness within the sector.

This paper presents the design and implementation of an intelligent safety system for freight elevators based on computer vision, aimed at reducing workplace accidents caused by improper use of such equipment. The proposed system relies on an SSD-MobileNet convolutional neural network, trained with a dataset of 1,050 labeled images under varying lighting conditions and deployed on a low-cost ESP32-CAM microcontroller. The system detects the presence of individuals at the elevator entrance and, through communication with a Siemens PLC S7-1200 and a variable frequency drive (VFD), determines whether to enable or block motor activation. Validation was conducted in a controlled laboratory environment using a three-level platform with a 25 kg load. The experimental results yielded an F1-score of 93.13%, a recall of 90.63%, and a specificity of 96.00%. The system is presented as a functional proof of concept with future potential for deployment in real industrial environments, highlighting its low cost, effective integration, and preventive approach to occupational safety.

Abstract-. The present study analyzes the relationship between attitudes towards artificial intelligence (AI) and critical thinking in university students of a University of Metropolitan Lima. Under a quantitative approach and correlational design, validated instruments were applied to a sample of 100 undergraduate students, including the scale of attitudes towards AI and a critical thinking questionnaire composed of two dimensions: analytical ability and argumentative ability. The results of Spearman's analysis revealed weak and non-significant positive correlations between the variables: general critical thinking (ρ = 0.177; p = 0.078), analytical ability (ρ = 0.105; p = 0.298) and argumentative ability (ρ = 0.139; p = 0.166). These findings suggest that, in this sample, attitudes toward AI are not statistically significantly related to the development of critical thinking. Therefore, the need for future research including mediating variables such as AI literacy, technical knowledge and pedagogical design arises, with the aim of delving deeper into the factors that might influence the link between the two constructs. This study provides empirical evidence for the understanding of the formative role of AI in higher education.

Keywords: artificial intelligence, critical thinking, university students.

En la era digital, el comportamiento del consumidor es clave para diseñar estrategias de marketing centradas en experiencias personalizadas. Este estudio realiza una Revisión Sistemática de la Literatura (RSL) bajo el método PRISMA 2020, analizando 42 estudios (2019–2024) extraídos de Scopus. Se observa una creciente incorporación de tecnologías como inteligencia artificial, machine learning y privacidad de datos, empleadas para personalizar ofertas y optimizar la interacción con el usuario. Las herramientas identificadas cumplen funciones de evaluación, predicción, clasificación y modelado, evidenciando un enfoque técnico en la gestión de la experiencia del consumidor. Europa, especialmente España, Reino Unido y Ucrania, lidera en producción científica. En resumen, el marketing digital ha evolucionado hacia un modelo centrado en el consumidor, basado en tecnologías avanzadas que mejoran la personalización y eficacia en entornos digitales.

This article analyzes the current state of the incorporation of emerging technologies—computer science, robotics, and artificial intelligence—into the Industrial Engineering curricula at sixteen Peruvian universities. Using a qualitative documentary analysis methodology, a partial and disjointed integration was identified, with an emphasis on computer science and a clear gap in AI and robotics. Based on this diagnosis, a pedagogical and curricular intervention model is proposed based on five dimensions: curriculum redesign, pedagogical innovation, teacher training, educational infrastructure, and entrepreneurship. This model, aligned with international frameworks such as the educational approach of the university in Northern Lima where this study was developed, seeks to promote ethical, active, and humanistic education in line with the principles of Industry 5.0.