This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the learning of numbers, with additional data from literature studies. For the development of the system, the cascade method was used, consisting of three stages: identification of the population, design of the artificial intelligence architecture, and implementation of the recognition system. The method of the system sought to replicate a mechanic that simulates a game, whereby the child trains the artificial intelligence algorithm such that it recognizes the numbers that the child draws on a blackboard. The system is expected to help increase the ability of children in their interest to learn numbers and identify the meaning of quantities to help improve teaching success with a fun and engaging teaching method for children. The implementation of learning in this system is expected to make it easier for children to learn to write, read, and conceive the quantities of numbers, in addition to exploring their potential, creativity, and interest in learning, with the use of technologies.
Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional sources. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network.
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, the effects of the pandemic caused by the Coronavirus disease discovered in 2019 are the subject of numerous studies by experts in labor, psychological issues, educational issues, etc. The universities, for their continuity, have implemented various technological tools for the development of their activities, such as videoconference platforms, learning management systems, etc. This experience has led the educational sector to propose new educational models, such as hybrid education, that focus on the use of information technologies. To carry out its implementation, it is necessary to identify the adaptability of students to a technological environment and what the factors are that influence learning. To do this, this article proposes a data analysis framework that identifies the factors and variables of a hybrid teaching environment. The results obtained allow us to determine the level of influence of educational factors that affect learning by applying data analysis algorithms to profile students through a classification based on their characteristics and improve learning methodologies in these educational models. The updating of educational systems requires a flexible process that is aligned with the needs of the students. With this analysis framework, it is possible to create an educational environment focused on students and allows for efficient change with the granular analysis of the state of the learning.
Currently, e-learning has revolutionized the way students learn by offering access to quality education in a model that does not depend on a specific space and time. However, due to the e-learning method where no tutor can directly control the group of students, they can be distracted for various reasons, which greatly affects their learning capacity. Several scientific works try to improve the quality of online education, but a holistic approach is necessary to address this problem. Identifying students’ attention spans is important in understanding how students process and retain information. Attention is a critical cognitive process that affects a student’s ability to learn. Therefore, it is important to use a variety of techniques and tools to assess student attention, such as standardized tests, behavioral observation, and assessment of academic achievement. This work proposes a system that uses devices such as cameras to monitor the attention level of students in real time during online classes. The results are used with feedback as a heuristic value to analyze the performance of the students, as well as the teaching standards of the teachers.
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