Open AccessWeb image re-ranking using query specific in cloud computing
Author(s) -
U. V. Anbazhagu,
Ramkumar Padinjare Pisharath Balakrishna,
Amarjit Ram,
M. Latha
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.21.12458
Subject(s) - computer science , information retrieval , ranking (information retrieval) , naive bayes classifier , question answering , cloud computing , search engine , classifier (uml) , interpretation (philosophy) , data mining , machine learning , artificial intelligence , support vector machine , operating system , programming language
Question answering (QA) allows all users to get information in enhanced technique. In this project we suggest a system for inspiring textual answer with appropriate media data. Our system consists of three components Interpretation median picking, Inquiry propagation, Data pick and Launching. Interpretation median picking is used to select various types of answers. Inquiry propagation is used for extracting the root words from the given query. Data pick and Launching is used for selecting the appropriate answer and producing the result. We use Stemming algorithm, Naïve Bayes classifier algorithm and page ranking algorithms. Stemming algorithm is used to extract the root word from the given searched query. Naïve Bayes classifier algorithm is used for selecting the type of medium. By using the page ranking algorithm the optimal solution is got. Our approach automatically determines which media will be a best solution for the given query. It automatically harvests the data from website for getting the answer. Our approach can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. We are enhancing community contributed answers. Any user who is unaware of data can get the information promptly. Our approach is to deal with the complex questions in an effective way. Based on the generated queries, we vertically collect image and video data with multimedia search engines.