Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.
Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.
Currently, medium and small businesses face a significant change in the way consumers purchase their services, products, or goods. This change is fundamentally due to the pandemic caused by the 2019 coronavirus disease, during which people were forced to use information and communication technologies to satisfy their needs and interact with other people. After the pandemic, people’s dependence on technology increased exponentially, to such an extent that the Internet has become the channel through which any product can be purchased in an agile and varied way, from the comfort of home, and regardless of schedules. Therefore, for companies, moving from the traditional market to eCommerce is a necessity, but the change must take place efficiently. Therefore, identifying the factors that influence consumers to access a brand, a service, or a product is a characteristic of eCommerce. This paper presents an analysis of the factors that influence the use of electronic commerce. For this, a review of similar works was carried out for the design of surveys and the identification of the critical points considered by consumers. These data were analyzed in a granular way with tools used in business intelligence to improve decision making in the migration to a digital market.
Currently, social networks have become one of the most used channels by society to share their ideas, their status, generate trends, etc. By applying artificial intelligence techniques and sentiment analysis to the large volume of data found in social networks, it is possible to predict the personality of people. In this work, the development of a data analysis model with machine learning algorithms with the ability to predict the personality of a user based on their activity on Twitter is proposed. To do this, a data collection and transformation process is carried out to be analyzed with sentiment analysis techniques and the linguistic analysis of tweets. Very successful results were obtained by developing a training process for the machine learning algorithm. By generating comparisons of this model, with the related literature, it is shown that social networks today house a large volume of data that contains significant value if your approach is appropriate. Through the analysis of tweets, retweets, and other factors, there is the possibility of creating a virtual profile on the Internet for each person; the uses can vary, from creating marketing campaigns to optimizing recruitment processes.
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