Conference Agenda

The present research focused on the development of artificial intelligence (AI) tools within the mining sector, specifically through predictive regression models, with an emphasis on the copper industry. This study adopted a quantitative and exploratory approach, enabling the development of models using various features of artificial neural networks (ANN) through Machine Learning algorithms, based on historical data in both cases. The main objective of this study was to develop predictive regression models in the copper mining industry using artificial neural networks (ANN). In this context, forecasts were made for the future price of copper and the capital cost of open-pit copper mines.
The results indicate that it was possible to estimate, with an acceptable degree of accuracy, both the future price of copper and the mining capital cost for open-pit copper mines. In the first case, a model was developed using the TensorFlow-based Deep Learning ANN technique for the "REDPC_17" test. A 7-day forecast was achieved with an acceptable margin for the three networks—LSTM, GRU, and CNN—resulting in an MSE of 2.6621e-04, 5.8286e-04, and 3.0642e-04, respectively. This led to an average percentage error of 7.9342% for the CNN network, which proved to be the best performer in the test.
In the second case, a model was developed using the Scikit-Learn MLPRegressor ANN technique for the "REDES_20" test. This model achieved the best performance in estimating CAPEX, with a determination coefficient (R²) of 0.00960 and an MSE of 0.8674.

The research is based on the need to improve efficiency and precision in mineral prospecting in hard-to-reach regions, taking advantage of advanced technologies. Its main purpose is to apply remote sensing methods, with a focus on the analysis of ASTER images, to locate areas with potential mineral. The methodology used is applied and not experimental, analyzing phenomena without manipulating variables. A transversal approach was used to interconnect variables related to mineralization, and indices such as NDVI and NDWI were applied to minimize atmospheric and vegetative interference. Likewise, techniques such as: band combination, band mathematics, band ratios and the Spectral Angle Mapper (SAM) method were used in order to remotely detect the greatest coincidence of anomalies for the identification of each target. The results include the identification of 5 exploratory targets, each with significant spectral anomalies. These findings highlight areas with potential for mineral prospecting. In conclusion, the study shows that remote sensing is an effective tool for the exploration and detection of mineral resources, allowing a detailed preliminary analysis of large areas, which saves time and resources in future exploration campaigns.

The implementation of a Datamart application for decision making in export companies in La Libertad, Peru. With the recent inauguration of the Chancay Multipurpose Port, an increase in competition and market complexity is expected, which requires more efficient management of export-related data. The lack of tools that centralize and present key information makes it difficult for companies to make decisions, reducing their competitiveness in a dynamic global environment. In this context, the implementation of Datamart is proposed as a key component within a Business Intelligence (BI) strategy. The study uses a quasi-experimental design to measure the impact of Datamart on the efficiency, reliability, and accuracy of decision making. The results show a significant improvement in the efficiency of time to obtain information, as well as in the reliability and accuracy of the data used for strategic decision making.

The implementation of Siemens SITRANS LR560 radar sensors in industrial silos, combined with a predictive system based on machine learning, has optimized the control of polypropylene resin levels. A 75% reduction in maintenance costs and an 87.5% reduction in downtime were achieved, improving operational efficiency. A Random Forest model was used to predict failures, validated with class balancing techniques and K-Fold cross-validation, reaching an accuracy of over 95%. The use of TIA Portal to integrate the SCADA system enables real-time monitoring and the generation of alerts for critical events. The results were compared with previous studies, demonstrating that the use of artificial intelligence and IoT in Industry 4.0 improves the reliability of granular material storage and distribution. Future improvements are recommended, including optimizing communication infrastructure in industrial environments, real-time data processing, and enhancing the decision-making process.

The objective of the present Systematic Literature Review (SLR) is to analyze the impact and applications of Artificial Intelligence (AI) in the agro-export sector. This review examines how AI has evolved in recent years, highlighting its potential to integrate multiple technologies in strategic decision-making and improve the competitiveness and sustainability of the sector. The methodology used focused on the Scopus database, where 240 articles were identified. After applying inclusion and exclusion criteria, 53 relevant studies were selected for analysis. AI-driven automation not only increases operational efficiency, but also contributes to a safer and more satisfying work environment by reducing the burden of repetitive and error-prone tasks. However, for the successful implementation of these technologies, a holistic approach that considers strategic, technical, and collaborative factors is necessary. Interdisciplinary cooperation between scientists, engineers and industry professionals is essential to develop solutions tailored to the specific needs of the agro-industrial industry. The conclusions of this research highlight that AI offers concrete solutions to address the challenges in the quality of agro-export products, guaranteeing food safety, meeting consumer demands and reducing food waste. Technologies such as deep learning and neural networks are standardizing production processes and adding value to the supply chain. In addition, automation not only improves operational efficiency, but also enhances the productivity of human capital and promotes more sustainable work environments

The study analyses how artificial intelligence (AI) has innovated in medicine between 2019 and 2024, focusing on diagnosis, treatment, prevention and care. 109 articles were reviewed, of which 19 met the inclusion criteria. The findings highlight applications such as robotic surgery, predictive analysis and telemedicine, with improvements in accuracy, cost reduction and control in various medical areas, especially general medicine and cardiology. The methodology employed a systematic review based on PICO and PRISMA. Despite advances, ethical, technological and adoption challenges persist, underlining the need for collaboration between physicians and developers to optimize implementation.

Student dropout in universities is a complex phenomenon influenced by academic, socioeconomic, demographic and institutional factors. This systematic review aims to analyze how data mining has been applied to predict and mitigate this problem, identifying relevant patterns in academic and demographic data. A total of 117 original articles found in databases such as Scopus, Dialnet and Ebsco were evaluated, of which 30 met the inclusion criteria. The most commonly used techniques included Random Forest, decision trees and XGBoost, standing out for their high accuracy in predicting attrition. The most effective predictive models identified at-risk students with an accuracy of over 90%, allowing personalized strategies to be designed and institutional resources to be optimized. Thus, data mining is an effective tool for anticipating and mitigating dropout, but its impact can be maximized by integrating it with qualitative approaches that consider psychosocial factors, which would favor more inclusive and effective interventions.