This article presents a methodology for the analysis of data on the Internet, combining techniques of Big Data analytics, NLP and semantic web in order to find knowledge about large amounts of information on the web. To test the effectiveness of the proposed method, webpages about cyberterrorism were analyzed as a case study. The procedure implemented a genetic strategy in parallel, which integrates (Crawler to locate and download information from the web; to retrieve the vocabulary, using techniques of NLP (tokenization, stop word, TF, TFIDF), methods of stemming and synonyms). For the pursuit of knowledge was built a dataset through the description of a linguistic corpus with semantic ontologies, considering the characteristics of cyber-terrorism, which was analyzed with the algorithms, Random Forests (parallel), Boosting, SVM, neural network, K-nn and Bayes. The results reveal a percentage of the 95.62% accuracy in the detection of the vocabulary of cyber-terrorism, which were approved through cross validation, reaching 576% time savings with parallel processing.

Este artículo 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. Con el propósito de explorar la información, se construyó un conjunto de datos basado en Tablas Dinámicas y Ontologías Semánticas, especificando las variables predictivas en jóvenes con inclinación suicida. Por último, para evaluar la eficiencia del modelo se utilizaron algoritmos de Aprendizaje de Máquina y Aprendizaje Profundo. Cabe destacar que se optimizaron los procedimientos para la construcción del dataset (utilizando Algoritmos Genéticos) y obtención del conocimiento empleando Cómputo Paralelo y Aceleración con Unidades de Procesamientos de Gráfico (GPU). Los resultados revelan una precisión del 96,28% sobre la detección de las características en adolescentes con tendencia suicida, alcanzando el mejor resultado a través de una Red Neuronal Recurrente con un 98% de precisión. De donde se infiere que el modelo es viable para establecer bases sobre mecanismos de actuación y prevención de comportamientos suicidas, que pueden ser implementados en instituciones educativas o distintos actores de la sociedad. 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.

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.

This article presents a model for the analysis of data on the Internet, using Web mining, to find knowledge about large amounts of information in cyberspace. To test the proposed method, Web pages on Cyberbullying were analyzed as a case study. The procedure integrates a Web Scraper to locate and download information from the Internet, to recover the vocabulary are used techniques of Natural Language Processing (tokenization, cleaning of words without meaning, frequency of term, inverse frequency of the document, synonyms, stemming methods). To obtain knowledge, a dataset was constructed using semantic ontologies to define the predictive variables of Cyberbullying and supervised learning to define the variable to be predicted. To evaluate the efficiency of the model, algorithms of machine learning, AdaBoost and Neural Network were used. The results reveal a percentage of 97% accuracy in the detection of Cyberbullying vocabulary, which was approved through crossvalidation, achieving a time saving of 581% with parallel processing, compared to sequential processing.