Detection of traits in students with suicidal tendencies on Internet applying Web Mining

This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The procedure considers a Web Scraper to locate and download information from the Internet, as well as Natural Language Processing techniques to retrieve the words. To explore the information, a dataset based on Dynamic Tables and Semantic Ontologies was constructed, specifying the predictive variables in young people with suicidal inclination. Finally, to evaluate the efficiency of the model, Machine Learning and Deep Learning algorithms were used. It should be noticed that the procedures for the construction of the dataset (using Genetic Algorithms) and obtaining the knowledge (using Parallel Computing and Acceleration with GPU) were optimized. The results reveal an accuracy of 96.28% on the detection of characteristics in adolescents with suicidal tendencies, reaching the best result through a Recurrent Neural Network with 98% accuracy. It is inferred that the model is viable to establish bases on mechanisms of action and prevention of suicidal behaviors, which can be implemented in educational institutions or different social actors

COVID-19 Social Lethality Characterization in some Regions of Mexico through the Pandemic Years Using Data Mining

In this chapter, an analysis of the data provided by the Federal Government of Mexico related to the COVID-19 disease during the pandemic years is described. For this study, nineteen significant variables were considered, which included the test result for detecting the presence of the SARS-CoV-2 virus, the alive/deceased people cases, and different comorbidities that affect a person’s health such as diabetes, hypertension, obesity, and pneumonia, among other variables. Thus, based on the KDD (Knowledge Discovery in Databases) process and data mining techniques, we undertook the task of preprocessing such data to generate classification models for identifying patterns in the data or correlations among the different variables that could have influence on COVID-19 deaths. The models were generated by using different classification algorithms, were selected based on a high correct classification rate, and were validated with the help of the cross-validation test. In this way, the period corresponding to the five SARS-CoV-2 infection waves that occurred in Mexico between March 2020 and October 2022 was analyzed with the main purpose of characterizing the COVID-19 social lethality in the most contagious regions of Mexico

Analyzing Typical Mobile Gestures in mHealth Applications for Users with Down Syndrome

Mobile technology has provided many advantages for all members of the Information Society. Communication, Organization, Transportation, Health, and Entertainment are just a few areas of mobile technology application. Nevertheless, there are still some people who find difficulties using it. Although there are a lot of applications of mHealth available for almost any kind of mobile device, there is still a lack of understanding and attending users’ needs, especially those of users with disabilities. People with Down syndrome have the potential to function as active members of our society, taking care of themselves and their own, having jobs, voting, and so on, but their physical limitations prevent them from handling correctly technological tools that could enhance their performance, including mobile technology. In this paper, we had analyzed how suitable the mHealth applications are for users with Down syndrome. We tested 24 users and analyzed their physical performance in fine-motor movements while developing a set of tasks over a mHealth application. Results showed that the design of a mHealth application for users with Down syndrome must center its interaction with simple gestures as tap and swipe avoiding more complex ones as spread and rotate. This research is a starting point to understand the fundamentals of people with Down syndrome interacting with mobile technology

Comunicar : revista científica iberoamericana de comunicación y educación

Título, resumen y palabras clave en español e inglésResumen basado en el de la publicaciónSe presenta un modelo de análisis de datos en Internet basado en Minería Web con el objetivo de encontrar conocimiento sobre grandes cantidades de datos en el ciberespacio. A fin de probar el método propuesto, se analizaron páginas web sobre el suicidio como caso de estudio con la intención de identificar y detectar rasgos en estudiantes con tendencias suicidas. El procedimiento considera un Web Scraper para localizar y descargar información de Internet, así como técnicas de Procesamiento de Lenguaje Natural para la recuperación de los vocablos.ES

Detection of traits in students with suicidal tendencies on Internet applying Web Mining

This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The procedure considers a Web Scraper to locate and download information from the Internet, as well as Natural Language Processing techniques to retrieve the words. To explore the information, a dataset based on Dynamic Tables and Semantic Ontologies was constructed, specifying the predictive variables in young people with suicidal inclination. Finally, to evaluate the efficiency of the model, Machine Learning and Deep Learning algorithms were used. It should be noticed that the procedures for the construction of the dataset (using Genetic Algorithms) and obtaining the knowledge (using Parallel Computing and Acceleration with GPU) were optimized. The results reveal an accuracy of 96.28% on the detection of characteristics in adolescents with suicidal tendencies, reaching the best result through a Recurrent Neural Network with 98% accuracy. It is inferred that the model is viable to establish bases on mechanisms of action and prevention of suicidal behaviors, which can be implemented in educational institutions or different social actors