Search

The COVID-19 pandemic has generated a global health crisis, and having tools that allow the disease to be efficiently managed is of vital importance. In this context, artificial intelligence offers a unique opportunity to analyze large volumes of medical data and obtain valuable information that can contribute to medical decision-making and improve management of the pandemic. In this work, artificial intelligence techniques are applied to model the results obtained from COVID-19 databases in Mexico. [ABSTRACT FROM AUTHOR]

The academic performance of students in Mexico has a great impact on the social and economic development of the country. Early detection of students at academic risk is necessary to improve educational quality and reduce school dropouts. This work presents a proposal that uses a predictive model based on Logistic Regression to identify students at high risk of academic failure and its usefulness to provide proactive and personalized support to those who need it. In addition, an overview of the impact of Artificial Intelligence and Machine Learning in education is presented, especially in predicting student dropout and supporting academic performance, allowing us to take an important step towards a more promising and successful educational future for students. students. [ABSTRACT FROM AUTHOR]

The analysis of human emotions is of great interest in data analysis, as it allows us to identify patterns and behaviors in people. Different techniques are used, such as linguistic rule-based approaches. Natural language processing (NLP) is a branch of AI that seeks to make machines understand language like humans do. It combines computational linguistics with machine learning and deep learning models. In emotional prosody, spoken words convey linguistic and paralinguistic information, where the emotional context influences the interpretation of the words. Alexithymia refers to difficulty identifying and expressing emotions. AI and NLP offer powerful tools for their study and application, which is why it was possible to develop an AI for the detection of emotions through natural language, resulting in a system that offers sentiment analysis for patients. [ABSTRACT FROM AUTHOR]

Artificial intelligence has developed at a very fast pace in various areas of knowledge, which is why we are already seeing worrying results regarding regulations governing the development of Artificial Intelligence applications. In this work we review some strategies that are being analyzed in various research groups in order to initiate a regulatory framework that gives us a panorama of greater security in future developments of Artificial Intelligence and its applications. [ABSTRACT FROM AUTHOR]

Sentiment analysis stands out as one of the most dynamic and pivotal areas in the field of natural language processing. In this work, a range of machine learning strategies has been proposed, applied, and benchmarked for sentiment analysis, with a specific focus on supervised machine learning techniques. Various algorithms have been considered and applied to texts extracted from Twitter. Furthermore, the results are compared with works that applied unsupervised machine learning techniques to the same dataset. [ABSTRACT FROM AUTHOR]