Open AccessEfficient Data Mining Model for Question Retrieval and Question Analytics using Semantic Web Framework in Smart E-Learning Environment
Author(s) -
Subhabrata Sengupta,
Anish Banerjee,
Satyajit Chakrabarti
Publication year - 2022
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v17i01.25909
Subject(s) - computer science , field (mathematics) , component (thermodynamics) , data science , automation , analytics , domain (mathematical analysis) , information retrieval , artificial intelligence , mechanical engineering , mathematical analysis , physics , mathematics , pure mathematics , engineering , thermodynamics
In the field of Information recovery, the fundamental target is to discover important just as most applicable data concerning a few questions. However, the essential issue regarding recuperation has reliably been, that the request for an area is enormous so much that it has gotten very difficult to recuperate applicable information capably. In any case, with the latest progressions in profound learning and AI models, calculations, applications brilliant and computerized data recovery component matched with text examination to decide different characterizing boundaries alongside intricacy and weight-age assurance of inquiries. By focusing, the cutoff points and hardships, like CPU cost, efficiency, automation and congruity, we have assigned our information recuperation structure, particularly towards the Academic Institutional Domain to consider the interest of various association related inquiries. The aim is to make an efficient data mining and an analytical model that can automate an efficient question retrieval and analysis for complexity and weight-age determination.