Improving the Accuracy of Oncology Diagnosis: A Machine Learning-Based Approach to Cancer Prediction

Cancer ranks among the most lethal illnesses worldwide, and predicting its onset can be a crucial factor in enhancing people’s quality of life by taking preventive measures to improve treatment and survival. This study conducted comparative research to determine the machine learning model with the highest accuracy for tumor type classification, distinguishing between malignant (cancer) and benign tumors. The models evaluated include decision tree (DT), naive bayes (NB), extra trees classifier (ETM), random forest (RF), K-means clustering (K-means), logistic regression (LR), adaptive boosting (AdaBoost), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) to identify the one with the best accuracy. The models were trained using a dataset of 569 records and a total of 32 variables, containing patient information and tumor characteristics. The study was structured into sections, such as related studies, descriptions of the models, case study development, results, discussion, and conclusions. The models’ performance was evaluated based on metrics of precision, sensitivity, accuracy, and F1 score. Following the training, the results positioned the XGBoost model as having the best performance, achieving 98% precision, accuracy, sensitivity, and F1 score

5G Technology for Innovation Education (Sustainable Development Goals 4): A Systematic Review

Access to quality education remains a significant global challenge today. In line with the goals outlined by the World Health Organization (WHO) in its 2030 agenda, ensuring access to education is a fundamental objective. Consequently, it is imperative to undertake an investigation into the influence of technological innovation on educational practices. This study examines the impact of incorporating the 5G network into educational settings to improve learning experiences. The analysis covered 134 articles, 62 of which were deemed relevant, classifying the research as ongoing projects or pilot studies for future exploration. The main digital tools identified were artificial intelligence, the Internet of Things, virtual reality, and machine learning. The use of the 5G network appears to have a more significant impact on higher education and universities. Research in this field is mainly concentrated in Europe, America, and Asia. In addition, it is clear that the adoption of 5G technology is influencing pedagogical methods, emphasising immersive learning, e-learning platforms, and flipped classrooms. This study argues for further research into the integration of technology in education, advocating a careful examination of the implementation of 5G infrastructure and its potential to improve access to high-quality education

Development and evaluation of a didactic tool with augmented reality for Quechua language learning in preschoolers

"It is important to preserve our cultural identity through the preservation of our mother tongue, contributing to its dissemination. Augmented reality (AR) is a great ally of education that provides efficiency, and productivity and increases the interest of students in their academic activities. An AR application was developed for learning Quechua in preschool children, thus improving their learning, satisfaction, and preference compared to traditional teaching. Previously, learning styles were identified for better coverage of the application; the design thinking methodology was applied for the development of the application, then the respective tests were conducted where it was obtained that the children's performance improved by 28.3% more compared to traditional teaching, with an average satisfaction of 89% of the classrooms, and 81% of students' preference. It was concluded that the proposed application considerably favors the written and audiovisual learning of the Quechua language in preschool students.

Changing Mathematical Paradigms at the University Level: Feedback from a Flipped Classroom at a Peruvian University

The university-level mathematics teaching adopted by many professors is still a traditional classroom, and many students’ perception of mathematics is that it is a complicated subject. The operationality of the flipped classroom proposal implemented at a university has a poten tial that can be used to change the perception that university students and teachers have towards the mathematics course, as well as to change the methodology of many teachers on how they teach their courses in the classroom. This research is the result of the implementa tion of the flipped classroom methodology in the basic mathematics course that is part of the professional careers of the engineering faculty of a Peruvian university. The aim of this study was to analyze the impact of applying the flipped classroom on academic results and atti tudes towards mathematics, with an experimental group of 227 students and a control group of 215 students. The academic results were measured at each of the stages indicated in the course syllabus, T1, partial exam, T2 and final exam; attitudes towards mathematics were also assessed at cognitive, procedural and affective levels at the end of the university semester. The Kolmogorov-Smirnov normality test was applied and yielded a value of p = 0.00, indicating that the grades obtained by the students did not follow a normal distribution. With the data obtained, the Mann-Whitney U test was performed, obtaining a p = 0.00 value (α = 0,052 tails). p < α makes us conclude that there are statistically significant differences between the scores of the experimental group compared to the control group. The results show a significant improvement in the academic performance and positive attitudes of students who took the course using the flipped classroom compared to those who did not use this methodolog

We Can Rely on ChatGPT as an Educational Tutor: A Cross-Sectional Study of its Performance, Accuracy, and Limitations in University Admission Tests

The aim of this research was to evaluate the performance of ChatGPT in answering multiple-choice questions without images in the entrance exams to the National University of Engineering (UNI) and the Universidad Nacional Mayor de San Marcos (UNMSM) over the past five years. In this prospective exploratory study, a total of 1182 questions were gathered from the UNMSM exams and 559 questions from the UNI exams, encompassing a wide range of topics including academic aptitude, reading comprehension, humanities, and scientific knowledge. The results indicate a significant (p &lt; 0.001) and higher proportion of correct answers for UNMSM, with 72% (853/1182) of questions answered correctly. In contrast, there is no significant difference (p = 0.168) in the proportion of correct and incorrect answers for UNI, with 52% (317/552) of questions answered correctly. Similarly, in the World History course (p = 0.037), ChatGPT achieved its highest performance at a general level, with an accuracy of 91%. However, this was not the case in the language course (p = 0.172), where it achieved the lowest score of 55%. In conclusion, to fully harness the potential of ChatGPT in the educational setting, continuous evaluation of its performance, ongoing feedback to enhance its accuracy and minimize biases, and tailored adaptations for its use in educational settings are essential

Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

"Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.

Contributions of the 5G Network with Respect to Decent Work and Economic Growth (Sustainable Development Goal 8): A Systematic Review of the Literature

Decent work and economic growth are fundamental elements for the sustainable development of a society, with Sustainable Development Goal 8 (SDG8) being one of the key objectives of the United Nations’ 2030 Agenda. The 5G network has great potential to contribute significantly to the achievement of SDG8, offering faster and more reliable connectivity, which opens up new possibilities for innovation, operational efficiency, and job creation. The present study aimed to investigate the role of 5G technologies concerning decent work and economic growth (SDG8). As part of the method, 265 articles extracted from main databases such as Scopus, IEEExplore, and ScienceDirect were analyzed using the PRISMA methodology, resulting in 74 relevant articles after applying the inclusion and exclusion criteria. As a result, a greater contribution to the use of the 5G network was identified in sectors such as manufacturing, health, and transportation, generating greater economic growth and job creation. It was also found that the technological applications with the greatest contributions are “Internet of Things” and “Artificial intelligence”. Finally, it was concluded that the results of this review are useful for future research on technologies that support 5G networks, contributing to economic growth and equitable and sustainable decent work in a wide range of sectors and rural areas

Text prediction recurrent neural networks using long shortterm memory-dropout

"Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem ""La Ciudad y los perros"" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.

Augmented reality for innovation: Education and analysis of the glacial retreat of the Peruvian Andean snow-capped mountains

Mountain glaciers are considered great reservoirs of water, and their importance lies in the fact that many of our ecosystems and numerous communities depend on them; Peru has one of the largest extensions of Andean snow-capped mountains, which have been affected by the decline in their glacier coverage and that is warned, will disappear due to environmental conditions and alterations in the current global temperature. This problem has increased due to ignorance, misinformation, indifference, and lack of solidarity on the part of the population who favors this discouraging situation. Taking advantage of the current technological immersion, in which we live, the development of a mobile application was proposed as a pedagogical resource to raise awareness among educational institutions about the glacial retreat of the Peruvian Andean snow-capped mountains, showing the current situation of some of the snow-capped mountains of the Andes that have suffered a greater impact, implementing augmented reality technology to obtain an interactive link. To provide greater detail of the situation, previous studies were carried out on glacial retreats in two Peruvian snow-capped mountains over the last 40 years, where it was found that, of the snow-capped mountains considered, Chicon had a decrease of 32.5% of its glacier cover, and Pumahuanca had a decrease of 56.9%. Such results are exposed within the application to provide realistic data on the glacial conditions of both Peruvian snow-capped mountains, as well as the consequences and conservation techniques to mitigate and cope with deglaciation. Taking into consideration that environmental education from an early age turns out to be key to forming an informed and participatory society about climate change

Techniques and algorithms to predict the outcome of soccer matches using data mining, a review of the literature

El resultado de un deporte se ha convertido en una necesidad para los competidores, así como para los fanáticos que siguen a sus equipos favoritos. Sin embargo, la predicción de los resultados de un partido de fútbol (PSMR) es muy variada debido a los diversos modelos existentes. La investigación es una revisión sistemática de la literatura (SLR) basada en manuscritos publicados en IEEE Xplore, Scopus, Science Direct y Springer. Se utilizó la metodología Prisma para el análisis y sistematización. El objetivo de esta investigación es ofrecer una guía para haciendo uso de técnicas de machine learning (ML). Los resultados mostraron que las técnicas de ML más utilizadas son el aprendizaje supervisado (SL) y el aprendizaje no supervisado (UL) y el algoritmo de ML más frecuente para predecir el resultado de un partido de fútbol es Random Forest (RF), teniendo en cuenta su gran contribución en la precisión de la predicción. Además, tras el estudio se propone un modelo novedoso y eficiente para predecir el resultado de los partidos de fútbol, apoyado con Data Mining (DM) y centrado en ML