Integration of data technology for analyzing university dropout

Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the data provided by the Engineering departments of the University of Mumbai, in India, determine that the variables that best explain a student's dropout are the socioeconomic factors and the income score provided by the University Admission Test (UAT). According to the decision tree technique, it is concluded that the retention is 78.3%. The quality of the classifiers allows to ensure that their predictions are correct, with statistical levels of ROC curve are 76%, 75%, and 83% successful for Bayesian network classifiers, decision tree, and neural network respectively

Data mining and social network analysis on Twitter

The emergence of a networked social structure in the last decade of twentieth century is accelerated by the evolution of information technologies and, in particular, the Internet has given rise to the full emergence of what has been called the Information Age [1] or the Information Society. Social media is yet another example of people’s extraordinary ability to generate, disseminate and exchange meanings in collective interpersonal communication with a massive, real-time networked system where everything tends to be connected. The analysis of the climate of opinion on Twitter is presented around the Common Core State Standards (CCSS), one of the most ambitious educational reforms of the last 50 years in USA

Classification of digitized documents applying neural networks

The exponential increase of the information available in digital format during the last years and the expectations of future growth make it necessary for the organization of information in order to improve the search and access to relevant data. For this reason, it is important to research and implement an automatic text classification system that allows the organization of documents according to their corresponding category by using neural networks with supervised learning. In such a way, a faster process can be carried out in a timely and cost-efficient way. The criteria for classifying documents are based on the defined categories

The RSU a new model for social management from the alma mater

A medida que pasa el tiempo muchas organizaciones o entidades tratan de entablar una serie de compromisos o deberes con la sociedad y medio ambiente. Esto es lo que hoy conocemos como Responsabilidad Social. Por lo general se relacionaba directamente a las empresas con este término siendo las más señaladas para contribuir al mejoramiento de la sociedad y medio ambiente. Las universidades siempre han tenido el papel importante de transmitir conocimientos y ser un puente entre las empresas y la sociedad. Es por esta razón, que se ha posicionado la RSU (Responsabilidad Social Universitaria) con el fin de crear en los estudiantes una conciencia social sostenible y un compromiso social al ejercer su profesión. A continuación, se presenta un estudio que tuvo por fin observar las impresiones del profesorado frete a esta tendencia y recopilar los aportes que desde su experticia pueden generarse para la promoción de la RSU.As time goes by many organizations or entities try to enter into a series of commitments or duties with society and the environment. This is what we know today as Social Responsibility. It was usually directly related to companies with this term being the most pointed to contribute to the improvement of society and environment. Universities have always had the important role of transmitting knowledge and being a bridge between companies and society. It is for this reason that the RSU (University Social Responsibility) has been positioned in order to create in the students a sustainable social awareness and a social commitment in the exercise of their profession. Next, a study was presented that had the purpose of observing the impressions of the faculty frete to this tendency and to collect the contributions that from their expertise can be generated for the promotion of the RSU

Management of occupational health and safety for the control of psychosocial risks in metalworking companies

Some companies with industrial activity at present without proper fashon management psychosocial risks inthe workplace this fundamentally for this type of business to emphasize biological and physical risks, the employees run their activities. The present study of the industrial companies of metalmechanics are managing the safety and the health in the work for the control of psychosocial risks; this a field study, supported by a quantitative imtmment in whch three categories are addressed: organizational behavior, personal characteristics and social environment. One of the conclusions is that industries that want to be productive and competitive in the market recognize the impedance of protecting the physical, mental and emotional integrity of their employees

Técnicas de minería de datos y análisis multivariado para descubrir patrones en investigaciones finales universitarias.

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%.El objetivo de este estudio es extraer conocimiento de las investigaciones finales de la Facultad de Ciencias de la Universidad de Mumbai. Se aplicaron cinco modelos de clasificación: máquinas de soporte de vectores, redes neuronales, árbol de decisión, bosque aleatorio y alimentación; considerando el diseño del experimento y las líneas de análisis multivariante. Los resultados mostraron que para la línea de diseño de experimentos, el modelo más preciso fue Random Forest con 71.48% de predicciones que son correctas con respecto al total. Con respecto a la línea de Análisis Multivariante, no hubo diferencias significativas en la precisión general, fluctuando en un 97%

Data Mining Techniques and Multivariate Analysis to Discover Patterns in University Final Researches

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%