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.
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.
<span lang="EN-US">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 "</span><em><span lang="EN-US">La Ciudad y los perros</span></em><span lang="EN-US">" 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.</span>
This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.
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.
The pandemic is currently forcing several countries to take certain restrictions on public transportation to prevent the spread of the virus, Peru is in the aforementioned phase, so many users who continue to use public transportation on a daily basis to get to work, home, supermarkets, among other activities, must stay informed to comply with these requirements. In this context, the mobile application was developed to help the proper management of information of the sanitary measures of the Covid-19 in the area of public transport for the city of Lima, and this is compatible with Android and iOS; likewise, the Mobile-D methodology, helped to make such mobile application and to know the phases to proceed with the implementation, which has an impact on time, information management and user satisfaction. The results of the present document show that the level of user satisfaction increased to 67.5% of a sample of 200 people as the experimental group. It was concluded that the application made it possible to automate the management of information on Covid-19 sanitary measures in the field of public transportation.
The world is currently facing the problem of the lack of education in basic education for students with intellectual disabilities. Therefore, it is important to follow up and monitor the various learning software that helps to address this problem. The study carried out is a review of scientific literature, which gathers research and studies, through a search in several databases: Dialnet, EBSCO, ERIC, IEEE Xplore, Redalyc, SAGE, ScienceDirect, Scopus, and Wiley. Likewise, according to certain previously defined inclusion and exclusion criteria, a total of two hundred (200) scientific articles were systematized, showing the digital technologies that facilitate the control, follow-up, and monitoring of the education of these students.
Citizen insecurity is a social problem that has increased considerably around the world. To combat it, in this research a mobile application based on IOT has been developed with the objective of mapping crimes and incident alerts to improve citizen security. Scrum methodology was used and a significant improvement can be seen with respect to the following indicators: number of reports of dangerous places, with an increase of 102.7%; the second indicator: number of reports by type of crime, with an increase of 25.34%; and the indicator: response time to attention, with an increase of 23.5%. It is determined that there is a significant positive influence of the mobile application developed to improve citizen security.
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