Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

The world is currently facing one of the biggest problems related to health and the quality of healthcare. According to the goals outlined by WHO in the blueprint for sustainable development (SDG3), one of its objectives is to achieve universal health coverage and ensure a healthy lifestyle. In this regard, it is important to monitor and track the impact of applications that help address this problem. This systematic review provides an analysis of the impact of the 5G network on the use of apps to improve healthcare. An analysis of 343 articles was performed, obtaining 66 relevant articles, the articles were categorized into research conducted with fiber optic backbone network as well as future research. The main medical applications were identified as: telesurgery, mobile ultrasound, biosensor technology, robotic surgery and connected ambulance. In addition, it is classified and answer questions such as the most used to improve medical care and health quality, 5G-based applications used in media to improve medical care and health quality, databases and programming languages in telemedicine are the most used in 5G-based applications, the functionality available for telemedicine based on the use of 5G-based applications.

Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus.

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.

Traditional learning in the chemistry course has often been seen as a boring course among students due to the use of static, non-dynamic, and interactive learning materials. The implementation of digital media in education has provided multiple advantages, the implementation of technologies such as augmented reality promotes effective learning because it makes it possible for students to interact with virtual elements in our real environment. The present research was delimited in the development of an application with augmented reality for the learning of chemical elements in 5th grade of elementary school students, to provide a support tool for both teachers and students, to help achieve a better understanding of the chemical elements. For the development of the augmented reality application called "Atomik-3D", the Mobile-D methodology was used, which is focused on the development of mobile applications; once the application was developed, functional tests were carried out to analyze surface recognition characteristics and ease of use, to analyze the performance a total of 20 tests were carried out giving us positive results. Through the analysis of the results, it was possible to identify that, for the recognition of flat surfaces, the mobile application has a good performance on illuminated surfaces with more than 30 lux. Likewise, the Marker technology used in this project has a very efficient recognition time of a flat area, being mostly no more than 1.5 seconds. Finally, 85% of the participants surveyed agreed that the application developed was easy and very easy to use.

It is during the primary education stage that children begin to awaken their interest in science and, in turn, have new mathematical, geographical, and scientific knowledge, which are the basis for understanding astronomical aspects. This research focuses on developing an Augmented Reality Mobile Application based on the Mobile-D methodology for the teaching-learning process of astronomy in 4th and 6th grade students. The random selection design of an experimental group applied to a sample of 60 students was used, subdivided into groups of 30 students each. Finally, it can be concluded that the use of an Augmented Reality mobile application for the teaching-learning process significantly influences elementary school students in the subject of astronomy.