Research- Web app for android

Application development for mobile devices has evolved substantially in recent years. Creating apps for mobile systems is becoming a standard in order to create better solutions to meet the market demand. Currently, half of the US population own Smartphones. This market comprises 150 million people, and 28% of these people consider mobiles their primary way of accessing the Web (Hales, 2013). A common feature of most websites tailored for mobile devices is that they are reduced versions of the desktop site. Several factors that must be considered when making this transition from desktop sites to mobile devices are: identifying elements from the desktop website that should be displayed on the mobile device screen; the amount of information the institution (BSU in this case) wishes to provide and designing an interface that will please the users;

Making Predictions with Textual Contents

Forecasting real-world quantities with basis on information from textual descriptions has recently attracted significant interest as a research problem, although previous studies have focused on applications involving only the English language. This document presents an experimental study on the subject of making predictions with textual contents written in Portuguese, using documents from three distinct domains. I specifically report on experiments using different types of regression models, using state-of-the-art feature weighting schemes, and using features derived from cluster-based word representations. Through controlled experiments, I have shown that prediction models using the textual information achieve better results than simple baselines such as taking the average value over the training data, and that richer document representations (i.e., using Brown clusters and the Delta- TF-IDF feature weighting scheme) result in slight performance improvements.