Conference Agenda

The instructions give the basic guidelines for This research addresses the application of Deep Learning techniques in data automation, highlighting its benefits and areas of implementation. A comprehensive search of academic databases was conducted using the PRISMA methodology, selecting relevant studies according to specific inclusion and exclusion criteria. The analysis focused on two main categories: predictive models and convolutional neural networks (CNN). In the area of predictive models, applications such as text sentiment analysis and IoT systems for predicting school dropout were evaluated. For CNNs, methods for 3D localization and smart factory management were explored. The findings indicate that Deep Learning significantly improves accuracy and efficiency in data automation, with applications in sectors such as technology, healthcare, agriculture and energy. Despite the limitations of the study, such as time coverage and database selection, future challenges are identified, including improving model scalability and efficiency, data security and privacy, and adaptability to new contexts with limited data. These findings provide a solid foundation for future research and practical applications in the field of Deep Learning.

This study identifies the main challenges in implementing data segmentation using machine learning techniques. A systematic literature review was carried out using the PICO methodology and the PRISMA framework, which allowed the selection of 44 relevant articles. The predominant methods include Deep Learning techniques, Ensemble Learning and traditional classification approaches, applied in domains such as telecommunications, health and cybersecurity. Among the highlighted challenges are the high complexity of the data, the presence of noise, the inconsistent quality of the records and the difficulty in integrating heterogeneous sources. Despite the progress made, limitations persist in terms of scalability and the absence of standardized methodological frameworks. This study is very useful for researchers oriented to improve the precision and efficiency in data segmentation in environments with large volumes of information. The development of adaptive methodologies and the establishment of standards that facilitate the transfer of knowledge between sectors are proposed.

The growing concern about climate change and environmental deterioration has made air pollution a global problem. This article presents a systematic review on the application of Machine Learning and Deep Learning techniques in air quality assessment. The objective of this research is to evaluate the effectiveness of Machine Learning and Deep Learning techniques in air quality analysis, identifying those that show superior performance in terms of accuracy and reliability of results. The PRISMA method was applied to compile 65 relevant articles on air quality. The findings indicate that Machine Learning and Deep Learning are crucial in this area, especially in research from India and China. The most common methods in Machine Learning are SVM and Random Forest, while in Deep Learning LSTM and CNN stand out. It is concluded that Machine Learning and Deep Learning are essential to assess air quality using IoT, Machine Learning stands out for its accessibility and ease of interpretation in small data sets, while Deep Learning, despite requiring more resources and data, provides greater accuracy in the analysis.

The rapid advancement of robotic technologies has driven the development of mobile robots, which are applied in various areas such as industry, logistics, exploration, security, healthcare, and many more. The aim of this research is to analyze and compare machine learning and deep learning techniques in the design and optimization of mobile robots with the purpose of identifying methods that stand out for their accuracy and reliability, thus contributing to the development of more efficient tools and models that increase the effectiveness of robotic systems in various environments. The PRISMA method was used to compile and systematize 65 articles relevant to the study topic. The results show that the development of mobile robots has become a frequent topic of interest for researchers in China and South Korea. In Machine Learning, the most prominent methods are Random Forest (RF) and SVM. In Deep Learning, the most outstanding techniques are CNN and SLAM. It is concluded that Machine Learning focuses on key applications such as navigation and mapping, while Deep Learning deals with complex challenges such as autonomous driving and image processing. Both disciplines complement each other in mobile robotics, where Machine Learning improves functionality and efficiency, and Deep Learning fosters innovation and understanding of the environment, opening opportunities for future research.

The ecological quality of water is crucial for the protection of the aquatic environment and human health, and is affected by natural factors and, to a greater extent, by pollution resulting from industrialization, agriculture, and urbanization. This article presents a systematic review on the application of Machine Learning and Deep Learning in water quality analysis. The aim is to evaluate the effectiveness of Machine Learning and Deep Learning in water quality analysis, identifying accurate and reliable methods to develop advanced tools that facilitate the monitoring and prediction of this vital resource, thus improving its management and conservation. The PRISMA method was used to gather 65 significant articles related to water quality. The results suggest that Machine Learning and Deep Learning are fundamental in this field, particularly in studies conducted in China and India. The most common algorithms in Machine Learning are Random Forest and SVM, while in Deep Learning LSTM and CNN stand out. It is concluded that Machine Learning and Deep Learning are essential to assess water quality with IoT, the choice between the two depends on the availability of data and the objectives of the analysis, Machine Learning is preferable with limited data and limited resources, while Deep Learning is more effective with large volumes of data.

This project applies geostatistical techniques, such as ordinary Kriging and the inverse distance method, using Datamine software to estimate the ore grades of a metal deposit and optimize the Optimal Mining Plan (OMP). The research is applied, with a descriptive approach detailing the characteristics and distribution of ore grades in the deposit, without manipulating variables. Geostatistical analyses, including copper and arsenic variograms, have allowed an accurate interpretation of their spatial distribution, improving the evaluation of the project's profitability. The distribution of 8,711 arsenic samples shows low concentrations with some significant anomalies, while copper shows moderate concentrations. The variograms of copper (0.027) and arsenic (3.1) facilitate the understanding of their behavior and variability, which contributes to optimize the decision making for mining. The detailed design of the mine, including the pit, access ramps, dump and roads, ensures an efficient and profitable operation. The applied geostatistical methodology allows for accurate ore grade estimates, which optimizes the design and execution of the mining operation, minimizing risks and maximizing project profitability. In conclusion, the use of Datamine software and geostatistics has been essential to the success of grade estimation, mine planning and mineral resource optimization, allowing for a more efficient and profitable utilization of the deposit.