Conference Agenda

The rapid adoption of remote work, accelerated by the COVID-19 pandemic, has underscored the critical role of soft skills in navigating digital work environments. As organizations transitioned to digital environments, workers encountered challenges such as increased levels of stress, low interpersonal interactions, and blurred work-life boundaries. These challenges revealed increasing needs for soft skills in the workplace. This research explores how emotional intelligence, assertive communication, and self-management predict productivity and well being among 131 remote workers, with a focus on digital natives. Using mixed methods (literature review and survey analysis), evidence suggests significant correlations: emotional intelligence explains 18.8% of productivity variance (R²=0.188), while assertive communication accounts for 28.7% (R²=0.287). The findings reveal that age and maturity are key determinants of soft skills proficiency, with professionals outperforming university students by 13.5% in self-management. However, despite technological fluency, digital natives show pronounced gaps in boundary-setting and emotional regulation. We propose actionable strategies for institutions and employers to bridge these gaps through structured training, mentorship, and policy reforms. The investigation confirms that in a digital-driven future, human-centric skills will differentiate organizational success, necessitating their integration into education and professional development.

This research presents the development of an automated diagnostic system for the early detection of tuberculosis (TB) in chest radiographs by comparing convolutional neural networks (CNN) and self-organizing maps (SOM), in order to evaluate which one offered better results in medical classification tasks. For the CNN model, pre-trained MobileNetV2 was used as feature extractor being fine-tuned with augmented and normalized images, achieving 98 % accuracy in binary classification (normal or tuberculosis). On the other hand, the SOM model was trained with the vectors generated by the CNN and allowed visualizing the distribution of the data in the feature space, achieving an accuracy of 95 %. Therefore, after comparing the performance of both models, it was chosen to implement the CNN as the core of the system, due to its high accuracy and generalization capability. The final model was integrated into a mobile application that connects to cloud services using Hugging Face for inference and Firestore for results storage. This solution was especially designed for resource-limited contexts, allowing health professionals in rural or hard-to-reach areas to have a support tool for the preliminary diagnosis of TB from a smartphone.

Abstract– Following the lessons learned during the COVID-19 pandemic, many education organizations decided to implement virtual classrooms for the delivery of their services, and they have proven to be a beneficial contribution to the education sector. However, this technology still presents many challenges and barriers to its proper integration. In this sense, it is important to analyze the scope and impact of virtual classrooms in this sector, as well as other technologies that, in conjunction with virtual classrooms, can help provide quality education. This systematic review provides an analysis of the impact of virtual classrooms in the education sector to improve the delivery of education through technology. A total of 437 articles were analyzed, of which 57 were considered relevant to the research. In addition, they were classified to answer questions related to the benefits of use, implementation and development trends and technologies most used in the management of virtual classrooms. In this sense, with 28.57%, it was identified that the main advantage of the use of virtual classrooms is that it improves the learning process. Likewise, LMS platforms, artificial intelligence, immersive environments and videoconferencing were the most used technologies in the educational sector found with 43.75%, 18.75%, 15.63% and 21.88% respectively. Finally, it was found that the most used trend is the use of methodological approaches with 28.57%. This trend is followed by others such as educational inclusion, evaluation of external factors and use of digital competencies in virtual environments.

As software-defined networks (SDNs) expand, so do the threats that affect them, especially denial-of-service (DoS) attacks. The ability to efficiently detect these attacks is critical to maintaining the integrity and availability of services in SDN environments. The purpose of this research is to conduct a systematic review (SR) of artificial intelligence (AI)-based detection methods used in SDN to detect DoS attacks, analyzing their technological characteristics and efficiency. To this end, 70 documents obtained from the Scopus and Web of Science databases, published between 2021 and 2025, were rigorously analyzed, including machine learning and deep learning models focused on detecting these threats. This review covers aspects such as: the characteristics of denial-of-service (DoS) attacks, the AI methods and models used, as well as the metrics and performance measures reported in the studies to evaluate the efficiency of these methods. The results show that most approaches achieve detection rates above 80% when using metrics such as accuracy, recall, and F1-score; however, limitations are identified in the detection of low-intensity attacks and in the handling of unbalanced datasets. In conclusion, it is indicated that artificial intelligence-based methods have high potential for protection in software-defined networks; however, an improvement in performance metrics and an adequate response to different attack variants are considered necessary in order to achieve greater effectiveness in real environments.

This systematic review evaluates the application of frameworks in enterprise architecture between 2021 and 2025, with the aim of identifying common practices, frameworks used, associated methodologies, and outcomes obtained across various sectors. Following the PRISMA approach and a structured search in Scopus, 31 qualitative and quantitative studies were selected using PIO criteria. The findings reveal that TOGAF was the most widely adopted framework, often combined with methodologies such as SOSTAC, ITIL, and DevOps. The most frequently used indicators included models such as TAM and DeLone–McLean, along with technical metrics like latency and traceability. Most studies focused on the public, educational, and industrial sectors. Although improvements in planning, institutional participation, and operational efficiency were reported, a lack of long-term impact evaluations was identified.

The rise of artificial intelligence (AI) has accelerated technological development, transforming sectors like cybersecurity. In this field, AI has become a key tool for detecting and mitigating cyber threats, but it is also being used to create more sophisticated and difficult-to-persuade attacks. This duality presents a significant challenge. On one hand, AI enhances defenses against vulnerabilities; on the other, cybercriminals use it to develop more complex attacks. The objective of this review is to analyze these new threats and the methods for detecting and preventing them. To do this, the PICO methodology will be used for a focused literature search, complemented by the PRISMA method for the selection and analysis of articles. The integration of AI in cybersecurity seeks to reduce the frequency and severity of attacks by improving the efficiency of threat detection. While this technology offers significant improvements, it is crucial to conduct a thorough analysis before its implementation. Companies must adapt and train their teams to effectively integrate AI into their security systems, thereby strengthening their defenses against the growing landscape of cyber threats.

This study presents a bibliometric analysis of peer-reviewed literature on STEM education in rural Latin America to identify publication trends, thematic structures, and international collaboration patterns. Despite growing policy interest in expanding scientific education in underserved areas, little is known about how this agenda has been reflected in academic production. A comprehensive search was conducted in Scopus and Web of Science, using a triple-block strategy that combined terms related to rural education, Latin American countries, and scientific disciplines. A total of 193 documents published between 1977 and 2025 were analyzed using Bibliometrix, with a keyword cleaning process to enhance thematic precision. The results reveal a marked increase in publications since 2015, led by Brazil, Colombia, and Mexico. However, international collaboration remains limited and uneven, with a small number of countries dominating both output and authorship. Thematic analysis shows strong emphasis on educational technology, curriculum design, and teacher training, while gaps remain in conceptual integration and territorial representation. This study offers an overview of the scientific landscape and underscores critical challenges related to regional asymmetries, disciplinary fragmentation, and collaboration deficits. The findings provide a foundation for future research and policy efforts aimed at strengthening inclusive science education in rural Latin America.