slugA Novel Semiautomatic Crowdsourcing Predictors for Faster Statistical Analysis Based Upon User Inputs
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 3
Abstract
Creating models from multiple data sets and deciding which data set is to be mined is now a day become more and more automated. But for selecting required data we required knowledge and experience, usually provided by domain experts. This paper gives the new approach to machine science by which first time non domain expert can create methodologies and provide values to those methodologies so that they can be useful for predicting behavioral consequence of interest. This is achieved by building a web platform in which a group of people interact with each other to give answers to questions as well as to predict behavioral consequences and to present a question to their peers. This gives a continuously improvising online survey and leads to predict the behavioral consequences of user with the help of their responses to survey questions formed by the user. Here we explain two web-based approaches to this concept: the first website predicts the user’s daily electricity consumption and other predicts body mass index. As daily increase in use of web this website gives large outputs in future.
Authors and Affiliations
Gaurav S. Barde, Ramesh S. Dhavane, Pavankumar K. Khole, Varun P. Mehta, Prof. Arindam Dasgupta
Keywords
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- EP ID EP17806
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