Analysis of the Impact of the Pandemic on the Growth, Use, and Development of E-Business: A Systematic Review of the Literature

The COVID-19 pandemic has affected various sectors in multiple countries, among them the economic sector has been one of the most affected, so the search for tools or measures for the continuation of sales and processes became recurrent, finding in e-business and its components precise tools to counteract the situation. Therefore, the present research aims to analyze the impact of the COVID-19 pandemic on the use, growth, and development of e-business by conducting a systematic literature review using the PRISMA methodology, collecting scientific articles covering the period of the pandemic from databases such as IEEE Xplore, ScienceDirect, Scopus, EBSCO, and IOPScience. Despite the limitations in access to scientific articles, it could be concluded that within the main characteristics identified, e-business tools in general allowed many businesses to continue subsisting and making sales thanks to the increase in online users due to the COVID-19 lockdowns. Although it was identified that the adoption of these tools lacked policies, limitations, and supports from governments, the perception of their use was positive in that they were considered safe and efficient

Benefits of Metaverse Application in Education: A Systematic Review

The COVID-19 pandemic has brought about significant changes in people’s lifestyles, with the educational sector being one of the most reliant on technology to facilitate the teaching and learning process. In this literature review, a search for articles related to the metaverse in education, published in 2022 and 2023, has been conducted across six databases: Scopus, EBSCO Host, ScienceDirect, Taylor & Francis Online, IEEE Xplore, and Springer. The PRISMA methodology was used to analyze and systematize the manuscripts found. The aim of this research was to examine how integrating the metaverse into education can enhance educational accessibility and equity by enabling students to utilize virtual learning resources and opportunities. In addition, they can engage in interactions with others to learn and create interactive content during the teaching and learning process. This requires a commitment from the student because a connection between the student and the machine will be established through the use of emerging technologies. These technologies offer unique opportunities to enhance teaching quality, broaden access to education, and prepare individuals for an increasingly digital and evolving world. The analysis identified 14 emerging technologies: artificial intelligence, cloud computing, big data, Internet of Things, blockchain, augmented reality, extended reality, virtual reality, 5G, EON-XR, digital twins, 3D virtual reality, and immersive virtual reality. These technologies offer immersion (simulation of a real world in a virtual world), interactivity (interaction with different people), improvement of the educational environment (innovative presentation of content), and motivation for learning (capturing attention). When it comes to the different types of learning, there are six categories: experiential (based on experience), collaborative (involving a guide to lead the process), cooperative (involving teamwork), significant (building on existing knowledge), explicit (self-directed learning), and emotional (involving the regulation of emotions)

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

Improving Environmental Sustainability: A Geolocation-Based Mobile Application to Optimize the Recycling Process

Environmental pollution caused by human activities is a global concern, and recycling is an effective strategy to reduce waste and minimize its negative impact on the environment, as these pollutants can have detrimental impact on ecosystems, human health, and the quality of life in general. Recycling avoids the accumulation of waste in landfills, which can contaminate soil, water, and air as well as reducing the production of new products. The objective of this work is to implement a mobile application to improve waste management by recycling companies. The Mobile-D methodology was used for the development of the project because it focuses on optimizing the efficiency and performance of mobile applications since it allows working in 5 phases which are: Exploration, Initialization, Production, Stabilization and Testing. In the first indicator (KPI-1), an improvement in customer retention was observed, with an increase of 114.29% in positive responses in the post-test. In the second indicator (KPI-2), there was a 39.92% decrease in response time, indicating a faster response in the collection service. In the third indicator (KPI-3), a significant increase of 86.86% in the volume of waste for recycling was observed. The results showed improvements in all indicators, indicating a positive impact of the implementation of the mobile application on waste management by the companies in the sector

Mobile Application with Augmented Reality as a Support Tool for Learning Human Anatomy

Learning the anatomy of the human skeletal system presents several challenges in understanding the complexity of the human body. One of the most common issues is the absence of effective and accessible learning methods that enable students to gain comprehensive knowledge. Therefore, the use of technologies such as augmented reality (AR) aims to address this issue and facilitate its resolution by enabling students to engage with three-dimensional anatomical models, fostering hands-on, visualization-based learning. The aim of this study is to enhance the learning of human skeletal anatomy through the use of AR technology. The study employed a quantitative approach and a pre-experimental design, in which the experiment was conducted according to the research plan and involved 60 students. Mobile-D was used to develop the mobile application. The findings revealed that 93.3% of participants agreed that the use of augmented reality is a valuable for learning human anatomy, as it enables interactive visualization of various parts of the human body. The study also indicated that 28.3% of the students scored “Outstanding,” while 68.3% scored “Predicted.” In addition, 65% of students expressed interest in using augmented reality technology to learn anatomy

"Breast Cancer Prediction using Machine Learning Models"

Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction

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

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

Intelligent agent for incident management

Un agente inteligente (AI) utiliza la inteligencia artificial (IA) para dialogar con los usuarios; las incidencias son interrupciones que surgen y que impiden a los usuarios hacer uso de las tecnologías de la información (TI); América Latina tiene un 15. 5% de las respuestas a incidencias de clientes. En Europa, cada año las incidencias de seguridad de TI se han visto incrementado desde 2019 en un 41%, las cuales se clasifican como de gravedad Alta y Muy Alta. El propósito de este estudio fue implementar un AI para mejorar la Gestión de Incidencias (GI), reducir el número de incidencias no resueltos, reducir el tiempo de resolución y aumentar la satisfacción de los usuarios. Para lograr este objetivo, se siguió un enfoque cuantitativo y un diseño preexperimental; se utilizó cuestionarios para el recojo de datos y, a continuación, todos los datos se sometieron a un análisis estadístico para validar la hipótesis. expertos verificaron la validez de los instrumentos, luego se obtuvo el índice de confiabilidad de los instrumentos utilizados. Además, se utilizó el marco de trabajo Scrum para el desarrollo de la solución inteligente. El logro de la implementación se obtuvo a través de la incorporación de diversas tecnologías como Dialogflow, Webhook, PostgreSQL; finalmente, se obtuvo un AI capaz de atender los incidentes reportados por los usuarios, asignando la tarea de manera automatizada. El desarrollo de este estudio permitió minimizar el número de incidencias no resueltas al día en un 14%; se redujo el tiempo de resolución de incidencias en un 63% y aumentó la satisfacción de los usuarios a “Satisfecho” al 43,3% y a “Muy Satisfecho” al 57,7%. Finalmente, se puede concluir que este trabajo proporciona una importante contribución para futuras implantaciones de diseños o desarrollos relacionados con la automatización de la GI mediante agentes inteligentes

Design of a Mobile Application to Improve the Therapy Process in Children with Autism

Autism is a spectrum disorder that affects communication and social interaction. It has increased in recent decades, especially among children, and has had a significant impact on their lives, necessitating attention and appropriate support. A prototype mobile application was developed using the Scrum methodology, which allows for flexibility, adaptability, incremental delivery, and quality, as well as continuous improvement. The result obtained was a prototype with a design and features that facilitate patient and specialist access to healthcare areas. The quality of the prototype was evaluated by experts, who assessed its efficiency, usability, design, and functionality and obtained an average score of 4.61. This indicates that, according to the established quality range, it is high. In conclusion, the prototype enhances the therapeutic process for children with autism. It is efficient, easy to use, and has good functionality and an attractive design. This provides a solution that facilitates patients’ access to health services for their well-being