"Mobile Applications for the Implementation of Health Control against Covid-19 in Educational Centers, a Systematic Review of the Literature"

"A health crisis caused by the SARS-CoV-2 virus is still ongoing. That is why an important factor for the resumption of on-site classes is the creation of sanitary measures to help control Covid-19. The present research is a literature review, The PRISMA methodology is used and 265 articles are collected from various databases such as EBSCO Host, IEEE Xplore, SAGE, ScienceDirect, and Scopus. According to the inclusion and exclusion criteria, the most relevant articles aligned to the topic were identified, systematizing 119 articles. Showcasing digital technologies used in mobile applications that allow better control, tracking, and monitoring of the health status of students, teachers, and staff of educational centers, in addition to the parameters and quality attributes that must be taken into account for the effective sanitary control of the disease, finally, a development model is proposed.

Mobile Applications for the Implementation of Health Control against Covid-19 in Educational Centers, a Systematic Review of the Literature

"—A health crisis caused by the SARS-CoV-2 virus is still ongoing. That is why an important factor for the resumption of on-site classes is the creation of sanitary measures to help control Covid-19. The present research is a literature review, The PRISMA methodology is used and 265 articles are collected from various databases such as EBSCO Host, IEEE Xplore, SAGE, ScienceDirect, and Scopus. According to the inclusion and exclusion criteria, the most relevant articles aligned to the topic were identified, systematizing 119 articles. Showcasing digital technologies used in mobile applications that allow better control, tracking, and monitoring of the health status of students, teachers, and staff of educational centers, in addition to the parameters and quality attributes that must be taken into account for the effective sanitary control of the disease, finally, a development model is proposed.

Digital platform based on geomarketing as an improvement in micro and small enterprises

After the situation generated by the pandemic caused by COVID-19, micro and small enterprises (MSEs) faced a complex reality, having to cope with business uncertainty. This research proposes a digital platform based on geomarketing as a growth and support strategy for MSEs, with the objective of improving their labor and capital productivity, through the incorporation of the technological factor, which will have a great impact on them, helping them to continue operating and not having to close their businesses. The platform was developed under the agile Scrum methodology because it is adaptable to the constant changes in the mobile application development process, having as indicators labor productivity and capital productivity. Finally, the results revealed that labor productivity increased by 30.86 percent, meaning that, for every hour worked per person, more sales were made. As for capital productivity, it decreased by 1.47 percent, meaning that investment decreased for each value added of each product sold

Free hardware based system for air quality and CO2 monitoring

Due to the increase in air pollution, especially in Latin American countries of low and middle income, great environmental and health risks have been generated, highlighting that there is more pollution in closed environments. Given this problem, it has been proposed to develop a system based on free hardware for monitoring air quality and CO2, in order to reduce the levels of air pollution in a closed environment, improving the quality of life of people and contributing to the awareness of the damage caused to the environment by the hand of man himself. The system is based on V-Model, complemented with a ventilation prototype implemented with sensors and an application for its respective monitoring. The sample collected in the present investigation was non-probabilistic, derived from the reports of air indicators during 15 days with specific schedules of 9am, 1pm and 6pm. The results obtained indicated that the air quality decreased to 670 ppm, as well as the collection time decreased to 5 seconds and finally the presence of CO2 was reduced to 650 ppm after the implementation of the system, achieving to be within the standards recommended by the World Health Organization

Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2

Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus

Internet of things based mobile application to improve citizen security

Citizen insecurity is a social problem that has increased considerably around the world. To combat it, in this research a mobile application based on internet of things (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

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.

Mobile Application for the Management of Covid-19 Health Measures on Public Transport Lines

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

Productivity of incident management with conversational bots-a review

The use of conversational agents (bots) in information systems managed by company’s increases productivity in the development of activities focused on processes such as customer service, healthcare, and presentation. The present work is a systematic literature review that collects articles from 2019 to 2022 in the databases Scopus, Springer, Willey, Indexes-Csic, Taylor & Francis, Pubmed, and Ebsco Host. PRISMA methodology was used to systematize 47 relevant articles. As a result of the analysis, 2/19 very important benefits were obtained, which are: helping to obtain information and facilitating customer service; as for the types of conversational bots, a total of 9 types were found, of which conversational agents and chatbots with artificial intelligence (AI) are the most common; in the case of processes, 3/5 processes that optimize conversational bots were found, where the most prominent are: teaching process, health processes, and customer service processes. An architecture model for conversational bots in incident management is also proposed

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Abstract: Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out