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This work describes the implementation of machine learning (ML) techniques: Random Forest, Xtreme Boosting Gradient, Support Vector Machine, K-Nearest-Neighbor and Logistic Regression as well as the creation of an artificial neural network (ANN), which were compared to determine the technique that can learn to predict with greater accuracy, the low academic performance of university students, to improve the mechanisms of e-learning and streaming that help them raise academic performance. The e-learning methodology was established for the first time in the late 1990s, however, since the Covid-19 pandemic, it has established itself as the best alternative to traditional education, placing it as a benchmark worldwide. One of the concerns in the university environment where this study was carried out is to be able to determine the impact that virtual teaching has had compared to face-to-face teaching, since there are factors (gender, number of children, sex, age, type of study) that could influence the academic performance of students. Using the classification metrics within the comparative process, it was determined that among the implemented ML techniques, the XGBoost reached 78.4% accuracy, but was surpassed by the artificial neural network (ANN) that learned to predict with 82.4%. of accuracy. Due to the above, the use of the artificial neural network is recommended for the prediction of the academic performance of university students since, in addition, with its massive predictions can be made due to its high processing capacity. Key Words: e-learning, covid19, streaming, academic performance, machine learning, artificial neural networks

The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature.

Technology as a tool serves to encourage teaching above all in the programming area due to its complexity. The present research seeks to potentiate and motivate the students’ interest for programming, using didactic resources through Arduino shields. It will be achieved adapting the resources and the activities involved in the learning process in this field. A descriptive research, using empirical studies, was done to capture the students’ attention towards different applications of didactic materials developed in internet of things. In this way the research contributes to stimulate and innovate the teaching learning process, so that students and teachers may improve their attitude and aptitude while receiving the subject. For teachers is of paramount importance to foster the collaborative learning in an interactive environment. The results allow concluding that the teaching learning process of different themes in areas of technology, especially in relation to programming.