neumonia has become the respiratory disease that continuously causes deaths in the world; as a response to this serious problem, a literature review is performed to identify Deep Learning classification models for pneumonia detection with an accuracy higher than 95%. For the identification of the models, different architectures such as InceptionV3, MobileNet, MobileNetV2 Xception, VGG16, VGG19, DenseNet201, NasnetMobile, CNN, and LSTM were evaluated. Although they all show very acceptable accuracy indicators, which justifies their evaluation for model identification, the datasets were evaluated with chest X-ray images in different categories. As a result, it was determined that ResNet152V2 achieved an accuracy of 99.22%, which is considered one of the best models for pneumonia detection.

The implementation of chatbots in multiple platforms with which man interacts has allowed automating processes and being able to respond to requests, one of its applications is the educational field, where they are still experimenting with good results in some areas and in others analyzing their feasibility, the deployment of this technology has been used both in basic education and in higher education, the former mainly addresses the resolution of simple queries facilitating the work of the teacher, and the latter seeks to go further by offering itself as a learning assistant. This work focuses on understanding the impact that chatbots have on education and their integration in some areas of the educational process, analyzing the results offered by other research and real applications. Chatbots have proved to be effective and increase user efficiency by providing information and being a...

The evolution of data science and the constant challenge of carrying out different processes using a few resources with simultaneous personalization has promoted interest in the development of voice cloning. Nowadays, different machine learning techniques are used, given their efficiency in generating relationships across multiple parameters. In this regard, we evaluated the best-performing models and the different process optimization strategies within this sector, where through neural network models separated modularly by their functionality, it is possible to generate independent processes taking into account the most significant number of linguistic factors in the generation of the voice, thus obtaining significant results of a clear improvement in the whole process of synthesizing the voice of a target speaker.

The objective of this work is to implement the logistic regression technique in datasets of gyms to identify and make a respective analysis for a correct segmentation of clients and, in this way, maximize the possibility of retaining clients; this will also allow us to rule out wrong decisions and incorrect assumptions, in addition to optimizing management according to customer data, which will be vital for a correct loyalty of gym users. The precision calculated the loyalty by the logistic regression algorithm considering important factors such as the rate of abandonment.