Title

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Document Type

Article

Source of Publication

Neural Computing and Applications

Publication Date

1-1-2022

Abstract

A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students’ outcomes, teachers’ performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.

ISSN

0941-0643

Publisher

Springer Science and Business Media LLC

Disciplines

Computer Sciences

Keywords

CNN, Covid-19, Digital class room, Fog computing, Internet of things

Scopus ID

85122356456

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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