Conference Agenda

La evolución del uso de la IA está señalizando un antes y un después en la inclusión de tecnologías, siendo aún difícil predecir el futuro inmediato, ya que se ha convertido en un instrumento esencial para los desafíos científicos; se advierte que se requiere crear soluciones tecnológicas sostenibles y con indicadores respetuosos con el medio ambiente, ya que la IA viene generando impacto en la esfera medioambiental. El objetivo de la presente investigación fue describir el impacto ambiental por el uso creciente de la IA, teniendo en cuenta los diferentes efectos en los recursos naturales y determinando algunas acciones preventivas o correctivas para minimizar el impacto de esta disciplina. Se tuvo en cuenta un enfoque exploratorio del nivel descriptivo, con un diseño no experimental. Se concluyó que el consumo energético, la huella hídrica, los residuos electrónicos y el uso de minerales son los principales factores de impacto. La investigación revela que la industria de la IA se dirige hacia una crisis energética, con un aumento exponencial en la demanda de electricidad que podría superar la capacidad de suministro en los próximos años. Es importante sensibilizar a la sociedad sobre el impacto ambiental de la IA y promover la educación sobre prácticas sostenibles en el uso de esta tecnología.

The incorporation of technologies in organizations is essential to optimize processes and reduce costs, especially in the context of electronic invoicing. This technology not only facilitates the management of documents and data, but also ensures the authenticity and protection of information. In Peru, the adoption of electronic invoicing has shown progress, with SUNAT promoting its implementation as a key tool to improve tax efficiency. However, lack of awareness and informality remain persistent challenges. In this context, CD TECH S.A.C. faces the need to modernize its accounting processes to adapt to tax requirements. Electronic invoicing, by replacing physical documents with digital ones, reduces operating costs, improves transparency and efficiency in tax collection. This study focuses on evaluating how the implementation of an invoicing system impacts the accounting management, analyzing the benefits and challenges faced by the company during this process. The general objective of the study is to determine the impact of electronic invoicing on the company's accounting management, through a series of specific objectives that include analyzing the current situation of accounting management before and after the implementation of the invoicing system. The central hypothesis is that the adoption of this technology will have an impact on the company's accounting management before and after the implementation of the electronic invoicing system.

This research developed a system to enhance efficiency in industrial data processing and analysis, addressing the need for better productivity and strategic decision-making. By employing data analysis and visualization techniques, the project converted CSV production reports into clear, visual formats. Utilizing a Python-based framework, the system integrated libraries such as tkinter, pandas, smtplib, scikit-learn and matplotlib, facilitating data management, graphical representation, and user interaction. The inclusion of email functionality further enabled easy distribution and collaborative analysis of reports, making the function of IoT a more flexible and efficient for the analysis of the machine data. Significantly making the process more efficiently the automation of industrial data analysis, the system streamlined the merging of reports, provided visual data comparisons, and generated detailed analyses through an accessible interface, thus aiding in the interpretation of data even for people who aren’t experts in data analysis, reducing manual analysis time, and supporting evidence-based decision-making.

For approximately twenty-five years, machine learning methods have been used to develop predictive models applied to construction materials. Concrete in particular is widely studied as it is the core of this industry, seeking to improve its properties to comply with both safety standards and market demands for more competitive products. There are major challenges in this area, one is the need for reliable data for the correct training of models, and other is understanding the choices made by computational methodologies to achieve such accurate models. To increase confidence in these useful tools, for example, when deciding to change a formulation and estimate its mechanical profile, it is necessary to evaluate the behavior of the model. For this, explainable artificial intelligence methodologies are beginning to be used. In this paper we review problems and advances in the area, hoping to contribute to the decision-making of construction engineers.

Traffic crashes on rural roads pose a significant safety risk due to a disproportionate number of fatalities. To address this issue, a driving simulation study tested an in-vehicle speed monitoring display (SMD) as an advanced driver assistance system (ADAS), both with and without voice recording. The simulation was based on PR-114, a two-lane rural highway in western Puerto Rico, and included scenarios with and without active ADAS alerts for speeding and roadway hazards. Results indicated that image alerts are associated with speed reductions.

The advancement of technology has led to a remarkable development in smart homes, where remote and automated management of connected devices transforms the user experience. This article systematically reviews the applications of Machine Learning and Deep Learning in the development of smart homes through the Internet of Things (IoT) technology. The objective of this research is to carry out a systematic review on the application of Machine Learning and Deep Learning in smart homes connected to IoT, focusing on energy efficiency, security and comfort, as well as identifying trends, challenges and opportunities for improvement to serve as a guide for researchers and developers. The PRISMA method was used to gather 68 relevant articles. The results show that Machine Learning and Deep Learning play a fundamental role in this field, with a greater number of investigations carried out in China and India. The most common methods in Machine Learning are Random Forest and Decision Trees, while in Deep Learning LSTM and CNN stand out. It is concluded that Machine Learning and Deep Learning are essential to improve security and Quality of Service by identifying and solving problems in advance. Deep Learning, in particular, improves surveillance and motion detection, and its combination with Machine Learning promises to transform monitoring, creating more integrated and efficient management systems.