Discovering CQA Post Voting Prediction using Artificial Neural Network and Entropy Analysis
Kavita Shinde1, Sarita Patil2
Manuscript received on 08 February 2019 | Revised Manuscript received on 21 February 2019 | Manuscript Published on 04 March 2019 | PP: 356-361 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2064017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As the whole world is aware of education and its importance the knowledge in web pages also grows due to crowd sourcing. Many web portals are running because of the good amount of the investment of the user’s knowledge for free and in a well desired manner makes the portals make hefty business. Some web portals like stack overflow, yahoo and even some social media sites like twitter and all are completely relying on crowdsourcing data. Most of the time it is hard to identify the best answer from the users for a question that was raised by the other user in the portal. Some methodologies are existed to achieve this where they are using the scores that are given by the other users or likes. This many times yield in loss of precision and never cross check the validation of the answers with their contents. So this paper puts forwards an idea of identifying the bag of word technique along with the Artificial neural network and entropy analysis of for nonlinear and unplanned distribution of data. Finally, by using the Bayesian law along with the fuzzy classification model for predicting degree yields the best prediction of question and answers.
Keywords: CQA, ANN, Bayesian Probability, Entropy Evaluation, Fuzzy Logic, Bag of Words.
Scope of the Article: Regression and Prediction