In Mexico, the community of people with hearing disabilities faces significant communication barriers due to the limited availability of interpreters and the lack of supportive technological tools. This paper presents the development of a sign language system using artificial intelligence (SLSAI), a mobile application that uses artificial intelligence (AI) for the recognition of Mexican sign language (MSL). Using machine learning techniques and image processing, SLSAI translates signs from the alphabet in real-time, facilitating interaction between deaf and hearing individuals. Additionally, the tool features a module where students can learn sign language through interactive games, creating a fun and engaging learning environment. The development methods, obtained results, and social implications of the tool are discussed.

Human beings go through several periods in life where it is crucial to learn how to adapt to their own needs—biological and physical. Added to these are social conditions, which may involve participation in society, education, care, and impact. This last aspect is a central topic that supports many other situations; hence, emotional intelligence (EI) is addressed. EI underpins the way individuals handle their emotions, helping them develop various skills, such as understanding and managing those emotions for effective interaction with others. Emotions will be examined by taking into account a dataset, and the classification and prediction will be based on the paradigm of artificial intelligence. Machine learning will be used, specifically convolutional neural networks (CNNs). This paper analyzes emotion prediction using two types of machine learning networks to identify emotions: CNNs and recurrent neural networks (RNNs), aiming to determine which of these two yields better predictive performance.