Conference Agenda

The accurate estimation of sales and their respective costs is essential for strategic decision-making in companies, so optimizing its calculation through advanced techniques is key to improving financial management. This research applies machine learning algorithms in the prediction of sales of financial products and unit costs of sale, using classification and regression models based on Random Forest. Through data preprocessing and hyperparameter optimization with GridSearchCV, a significant improvement in the accuracy of predictions is achieved, reducing time and effort compared to traditional methods

Abstract- The objective of this research was to identify the socioeconomic variables that have the greatest impact on government stability and develop a mathematical model that allows describing and predicting their behavior in Latin American countries, with a particular focus on Honduras and other selected countries. The research highlights the importance of making Honduran government stability visible, highlighting that, compared to other countries such as Brazil, data from Honduras show lower precision in the model due to variability in key factors, such as corruption, law and order or foreign investment. This suggests that low or inconsistent indices of certain indicators affect the predictive capacity of the model for the country. The sample data comes from reliable sources such as the International Country Risk Guide (ICRG) and the WITS (World Integrated Trade Solution) platform, and the statistical analysis demonstrated the high effectiveness of the model in several regions. However, Honduras presented the lowest results in terms of precision, which shows the need to strengthen its socioeconomic indicators to improve its government stability. This study provides a starting point for government public policies focused on improving the critical factors that influence government stability, promoting more sustainable and stable development in a region.

The present research focuses on applying artificial vision and the artificial neural network, for the identification of pests in lucuma leaves (SP, PG, ER) in the field of the district of Villa El Salvador through a mobile system with 4500 images obtained in the field, managing to do it from a mobile application being easy to use, as well as the results will be reliable, since the results are statistically demonstrated (confusion matrix), where 99.32% of accuracy, an f1-score of 99%, 100% and 0.99% for SP, ER and PG disease respectively. Thus, it was also possible to achieve an effectiveness rate of 99.32%, implying that the adjustments in the configurations necessary for the SOM neural network model are ideal and the algorithms based on computer vision used (sobel, medfield and orientation) are ideal, therefore the first specific objective is met, in the same way it was possible to reduce the pest per crop area by 1.5% (visually) due to the care taken in identifying the pest on the leaf, therefore, it can be said that Considering the limitations of nature, it meets the second specific objective. Considering the level of simplicity of using the Smartphone, where 33% are extremely satisfied, 45% are very satisfied, 22% are somewhat satisfied and 0% are not so satisfied with not at all satisfied.

This study conducts a systematic review of the literature on tools based on artificial intelligence (AI) to improve social skills in children with Autism Spectrum Disorder (ASD), covering the period 2019-2024. The information search was conducted in the Scopus database, where 40 articles were identified and selected using the PRISMA and PICO method, specifically its PIO variant.

Subsequently, a bibliometric analysis was performed to obtain a deeper insight into research in this field. Tools such as VOSviewer were used to visualize cooperation between countries in related scientific production; Bibliometrix to graphically represent the countries with the highest scientific production and Power BI to generate a keyword cloud (WordCloud).

The study investigates various AI methods, including analysis of brain images (MRI, fMRI), electroencephalographic (EEG) signals and behavioral data, with the aim of developing more accurate and efficient diagnostic systems. Different machine learning algorithms (SVM, CNN, neural networks, etc.) are compared and feature selection techniques are analyzed to improve the accuracy of predictive models. Finally, applications of social robotics in the interaction and learning of children with ASD are explored

This study investigates the integration of circular economy strategies and artificial intelligence (AI) in the sustainable management of groundwater in the mining sector. In response to growing concerns about sustainability and water pollution, an approach is proposed that promotes the efficient use of this vital resource. Through AI-based predictive models, the quality and availability of water are anticipated, allowing for more effective and responsible management. The results indicate that the implementation of reuse systems not only improves the quality of treated water but also reduces operational costs compared to conventional methods. Phytoremediation using constructed wetlands is highlighted as an effective solution for pollutant removal, aligning with the principles of the circular economy. In conclusion, the combination of these strategies offers significant environmental and economic benefits, driving more sustainable and responsible mining practices.

The research addressed the relationship between the use of artificial intelligence (AI) and the effectiveness of scientific research in Science and Technology (S&T) teachers in Metropolitan Lima in 2024. Using a quantitative approach, data from a sample of 218 teachers were processed using descriptive statistics and SPSS version 26 software. A 50-item questionnaire was developed, validated with the Hernandez-Nieto statistic, reaching a validity per item of 0.996 and a Cronbach's alpha of 0.949. The applied survey yielded a Spearman correlation coefficient of 0.662 between the use of AI and effectiveness in inquiry, indicating a positive correlation. In addition, moderate and high correlations were found for variables such as frequency of AI use (0.395) and quality of information provided by AI (0.666), respectively. The results confirmed the trends observed in previous studies, both national and international, concluding that AI significantly enhances research effectiveness in the S&T field.