This paper presents the results from the application of a serious game called Stigma-Stop among a group of high school students with the aim of reducing the stigma towards mental illnesses. The video game features characters with various mental disorders (schizophrenia, depression, bipolar disorder, and panic disorder with agoraphobia) and provides information about these problems. Additionally, the game asks players about whether they have ever felt the same as the characters, if they believe the characters are psychologically well, and if they think they could help these individuals. Similarly, a variety of reactions are provided for players to choose from when they encounter the characters with these problems. A total of 552 students between the ages of 14 and 18 participated in the study, and they were randomly assigned to either the experimental group, which used Stigma-Stop, or the control group, which utilized a video game completely unrelated to mental health. Both video games were used for similar lengths of time. Following the application of Stigma-Stop, a statistically significant decrease was obtained in levels of stigma towards schizophrenia, both in terms of stereotypes and, to a greater extent, its potential dangerousness. However, this was not the case in the control group. Results thus demonstrate the video game’s usefulness towards eradicating erroneous notions about serious mental disorders like schizophrenia.

Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide. However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse. It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time. As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence. In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process. For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence. This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject. Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.

The paramo, plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon. This research was carried out in the province of Tungurahua, specifically the Quero district. The aim is to develop a classification of the land use land cover (LULC) in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance, for which three different approaches were applied: Pixel-Based Image Analysis (PBIA), Geographic Object-Based Image Analysis (GEOBIA), and a Deep Neural Network (DNN). Various parameters were used, such as the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI), texture, altitude, and slope. Seven classes were used: paramo, pasture, crops, herbaceous vegetation, urban, shrubrainland, and forestry plantations. The data was obtained with the help of onsite technical experts, using geo-referencing and reference maps. Among the models used the highest-ranked was DNN with an overall precision of 87.43%, while for the paramo class specifically, GEOBIA reached a precision of 95%.

In this paper we present a framework for ocean image classification based on ontologies. With this aim, we will describe how low and high level content of ocean satellite images can be modeled with an ontology. In addition, we will show how the image classification can be modeled with the ontology in which decision tree based classifiers and rule-based expert systems are represented. Particularly, the rule based expert systems include rules about low-level features (called training and labeling rules), and rules defined from the labeling (called human expert rules). The modeling with the ontology provides an extensible framework in which accommodate several methods of image classification. One of the main aims of our proposal is to provide a mechanism to share data about image classification between applications. We have developed an extensible Protégé plugin to classify images.