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.

The understanding of the physics course (PC) at the university level faces many challenges, highlighting the gap in mathematical knowledge and conceptual phenomenological analysis that affect the assimilation of more advanced physical concepts. The synergistic integration of artificial intelligence (AI) and gamification into the teaching and learning process transforms this process into an engaging and collaborative experience, improving interaction and personalization of learning and incorporating game elements to increase student participation and motivation. The objective of this research was to measure the influence of the integration of ChatGPT, AI, and gamification on the academic results and motivation of students in the physics 1 course. A total of 188 students participated in this study: 98 students in the experimental group and 90 students in the control group. The grades obtained in the standardized evaluations were compared in both the partial exam and the final exam of the physics 1 course, and a Likert-type questionnaire was used for motivation. The results show a better academic performance (AP) of the students in the experimental group than the control group, which is statistically evidenced (p < 0.01). It also shows that students who developed the course using ChatGPT AI and gamification developed a more positive attitude towards interest, usefulness, self-efficacy, active participation, and personal satisfaction in the physics 1 course.

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.

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 < 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.