A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning

In this paper, we present a methodology and the corresponding Python library 1 for the classification of webpages. Our method retrieves a fixed number of images from a given webpage, and based on them classifies the webpage into a set of established classes with a given probability. The library trains a random forest model build upon the features extracted from images by a pre-trained deep network. The implementation is tested by recognizing weapon class webpages in a curated list of 3859 websites. The results show that the best method of classifying a webpage into the studies classes is to assign the class according to the maximum probability of any image belonging to this (weapon) class being above the threshold, across all the retrieved images. Further research explores the possibilities for the developed methodology to also apply in image classification for healthcare applications.Comment: 4 pages, 3 figures. Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. ACM, 201

Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning

One of the technological aspects that contribute to improving the quality of life of adults, is precisely the enrichment of physical spaces with sensors, video surveillance equipment and actuators, which favor the performance of their daily life activities, which allows discover patterns of human actions generated from the movement and interaction of individuals with the environment, in such a way that they facilitate the monitoring of data and the understanding of the activity of older adults in surveillance environments, based on technology, with the purpose of automatically detecting abnormal patterns, which affect your health or could endanger your life. All these basic activities give older adults the possibility of interacting in community with the tranquility of a personalized and functional medical attention through the implementation of technology. Although the list of activities that a person can perform is extensive, this study focused on those that take place in indoor environments. The recognition of human activities is a field of research that subscribes to an investigative framework, which is the study of activities of daily life. Monitoring the human activities of daily life is a way of describing the functional and health status of a human being. The rapid population growth of older adults has caused an increase in the demand for personal care, particularly for people with affectations typical of senile dementia, due to the correlation that exists between this and the deterioration of memory, intellect, behavior and the consequent decrease in the ability to carry out activities of daily living. Therefore, the need arises to carry out this project, which establishes a predictive model of activities of daily life carried out by inhabitants in indoor environments, through the use of classification and selection techniques based on Machine Learning.Uno de los aspectos tenológicos que contribuyen a mejorar la calidad de vida de los adultos, es precisamente, el enriquecimiento de espacios físicos con sensores, equipos de video vigilancia y actuadores, que favorezcen la realización de sus actividades de la vida diaria, lo que permite descubrir patrones de acciones humanas generados a partir del movimiento y la interacción de los individuos con el ambiente, de tal manera que faciliten el monitoreo de datos y la comprensión de la actividad de los adultos mayores en entornos de vigilancia, basados en tecnología, con el propósito de detectar automáticamente patrones anormales, que afecten su salud o puedan poner en riesgo su vida. Todas estas actividades básicas les confieren a los adultos mayores la posibilidad de interactuar en comunidad con la tranquilidad de una atención médica personalizada y funcional a través de la implementación de tecnología. Aunque la lista de actividades que puede realizar una persona es extensa, este estudio se enfocó en aquellas que se desarrollan en ambientes indoor. El reconocimiento de actividades humanas es un ámbito de investigación que se suscribe a un marco investigativo, que es el estudio de las actividades de la vida diaria. Monitorear las actividades humanas de la vida diaria es una forma de describir el estado funcional y de salud de un ser humano. El rápido crecimiento poblacional de adultos mayores ha provocado un aumento en la demanda del cuidado personal, particularmente para personas con afectaciones propias de la demencia senil, debido a la correlación que existe entre esta y el deterioro de la memoria, el intelecto, el comportamiento y la consecuente disminución de la capacidad para realizar actividades de la vida diaria. Por tanto, surge la necesidad de realizar este proyecto, que establece un modelo predictivo de actividades de la vida diaria realizadas por habitantes en ambientes indoor, mediante el uso de técnicas de clasificación y selección basadas en Machine Learning