Abstract Cancer is a tumor that affects people worldwide, with a higher incidence in females but not excluding males. It ranks among the top five deadliest types of cancer, particularly prevalent in less developed countries with deficient healthcare programs. Finding the best algorithm for effective breast cancer prediction with minimal error is crucial. In this scientific article, we employed the SMOTE method in conjunction with the R package Shiny to enhance the algorithms and improve prediction accuracy. We classified the tumor types as benign and malignant (B/M). Various algorithms were analyzed using a Kaggle dataset, and our study identified the superior algorithm as logistic regression. We evaluated algorithm performance using confusion matrices to visualize results and the ROC Curve to obtain a comprehensive measure of performance. Additionally, we calculated precision by dividing the number of correct predictions by the total predictions Keywords Breast cancer, Smote, Benign, Malignant.

The widespread adoption of the QWERTY keyboard layout, designed primarily for English, presents significant challenges for speakers of indigenous languages such as Quechua, particularly in the Puno region of Peru. This research examines the extent to which the QWERTY layout affects the writing and digital communication of Quechua speakers. Through an analysis of the Quechua languages unique alphabet and character frequency, combined with insights from local speakers, we identify the limitations imposed by the QWERTY system on the efficient digital transcription of Quechua. The study further proposes alternative keyboard layouts, including optimizations of QWERTY and DVORAK, designed to enhance typing efficiency and reduce the digital divide for Quechua speakers. Our findings underscore the need for localized technological solutions to preserve linguistic diversity while improving digital literacy for indigenous communities. The proposed modifications offer a pathway toward more inclusive digital tools that respect and accommodate linguistic diversity.

The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance in the management of these. We proved algorithms with these records of research projects that have been analysed in this study, and predictions were made for each of the nine (09) models of unsupervised machine learning algorithms that were implemented for each of the 7430 records from the dataset. The most efficient way of extracting keywords for this dataset was the TF-IDF method, obtaining 72% accuracy and [0.4786, SD 0.0501] in average extraction time for each thesis file processed by this model.

Peruvian mining plays a crucial role in the country's economy, being one of the main producers and exporters of minerals worldwide. In this project, an application was developed in RStudio that utilizes statistical analysis and time series modeling techniques to understand and forecast mineral extraction in different departments of Peru. The application includes an interactive map that allows users to explore Peruvian geography and obtain detailed statistics by clicking on each department. Additionally, bar charts, pie charts, and frequency polygons were implemented to visualize and analyze the data. Using the ARIMA model, predictions were made on the future extraction of minerals, enabling informed decision-making in planning and resource management within the mining sector. The application provides an interactive and accessible tool to explore the Peruvian mining industry, comprehend trends, and make accurate forecasts. These predictions for 2027 in total annual production are as follows: Copper = 2,694,957 MT, Gold = 72,817.47 kg Fine, Zinc = 1,369,649 MT, Silver = 3,083,036 MT, Lead = 255,443 MT, Iron = 15,776,609 MT, Tin = 29,542 MT, Molybdenum = 35,044.66 MT, and Cadmium = 724 MT. These predictions, based on historical data, provide valuable information for strategic decision-making and contribute to the sustainable development of the mining industry in Peru.