An Intelligent Framework Prototype for Monitoring Students in Virtual Classroom

Satapathy, Ashutosh; Livingston, L.M. Jenila

doi:10.11591/ijeecs.v12.i3.pp1151-1158

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Quantitative evaluation for computer network security [21][22][23][24][25] has critical impacts on the pro-active operation of the network protection. The existing approaches are short of a self-controllable mechanism thus an appropriate security model has been presented in this paper.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of computer network security using attack undirected geography

Jittawiriyanukoon¹

2019

IJEECS

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<span>To secure a wealth of data traversing the computer network at your fingertips is compulsory. But when attack arises at various parts of the network it is difficult to protect, especially when each incident is investigated separately. Geography is a necessary construct in computer networks. The analytics of geography algorithms and metrics to curate insight from a security problem are a critical method of analysis for computer systems. A geography based representation is employed to highlight aspects (on a local and global level) of a security problem which are Eigenvalue, eccentricity, clustering coefficient and cliques. Network security model based on attack undirected geography (AUG) is familiarized. First, analysis based upon association rules is presented then the attack threshold value is set from AUG. The probability of an individual attack edge and associated network nodes are computed in order to quantify the security threat. The simulation is exploited to validate that results are effective.</span>

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Section: Resultsmentioning

confidence: 99%

Evaluation of computer network security using attack undirected geography

Jittawiriyanukoon¹

2019

IJEECS

View full text Add to dashboard Cite

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“…Quantitative evaluation for computer network security [21][22][23][24][25] has critical impacts on the proactive operation of the network protection. The existing approaches are short of a self-controllable mechanism thus an appropriate security model has been presented in this paper.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Computer Network Security Using Attack Undirected Geography

2023

ecb

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To secure a wealth of data traversing the computer network at your fingertips is compulsory. But when attack arises at various parts of the network it is difficult to protect, especially when each incident is investigated separately. Geography is a necessary construct in computer networks. The analytics of geography algorithms and metrics to curate insight from a security problem are a critical method of analysis for computer systems. A geography-based representation is employed to highlight aspects (on a local and global level) of a security problem which are Eigenvalue, eccentricity, clustering coefficient and cliques. Network security model based on attack undirected geography (AUG) is familiarized. First, analysis based upon association rules is presented then the attack threshold value is set from AUG. The probability of an individual attack edge and associated network nodes are computed in order to quantify the security threat. The simulation is exploited to validate that results are effective

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“…Framework based works have been done too previously to monitor students in online classes, which is secure against unauthorized logins [5]. The technique used was Liveness Face Detection technique and as LFD [6] has been used so IPS has no effect on duplicate.…”

Section: Related Workmentioning

confidence: 99%

Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

Ghani¹,

Jahan²,

Khan³

et al. 2021

JSEA

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Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There's a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.

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An Intelligent Framework Prototype for Monitoring Students in Virtual Classroom

Cited by 7 publications

References 13 publications

Evaluation of computer network security using attack undirected geography

Evaluation of computer network security using attack undirected geography

Evaluation of Computer Network Security Using Attack Undirected Geography

Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

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