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

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