AI Based Attendance System using Machine Learning

In our modern-digital age, monitoring attendance system in education institutions play a pivotal role for insuring accountability, optimizing resource utilization and enhancing efficiency. Methods that are being followed traditionally such as taking attendance Manually are prone to errors, very time-taking, causes lack of scalability. To over come this challenges, our research proposes an innovative AI based attendance monitoring system using Ml is designed to bring a revolutionary change in the way attendance is managed in Education System. This proposed system importance's "State of the art" computer vision techniques, Machine Learning Algorithms and modules of Deep Learning to automate the attendance tracking process. On Integrating cameras installed in universities with advance facial technology, which accurately identifies, record and stores the students attendance without requiring any type of manual intervention. Besides, the system incorporates intelligent features to deal with various scenarios such as occlusions, varying lighting conditions, and facial expressions, ensuring robust performance in real-world environments Through seamless integration with existing student data set and cloud-based platforms, this proposed solution facilitates centralized data management, empowering administrators with actionable insights for strategic decision-making and real time monitoring.

Authors

Institutions

Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/iceect61758.2024.10738937
Citations
2
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
1.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Based Attendance System using Machine Learning

Kothuri Sravani, S. K. Fathima, S. P. Balakannan, Jeyapriya Jeyapriya et al.
2 citations
Artificial Intelligence in Healthcare
1.42
article

AI Based Attendance System using Machine Learning

Kothuri Sravani, S. K. Fathima, S. P. Balakannan, Jeyapriya Jeyapriya, Rosini Rosini
article en
2 citations

Abstract

In our modern-digital age, monitoring attendance system in education institutions play a pivotal role for insuring accountability, optimizing resource utilization and enhancing efficiency. Methods that are being followed traditionally such as taking attendance Manually are prone to errors, very time-taking, causes lack of scalability. To over come this challenges, our research proposes an innovative AI based attendance monitoring system using Ml is designed to bring a revolutionary change in the way attendance is managed in Education System. This proposed system importance's "State of the art" computer vision techniques, Machine Learning Algorithms and modules of Deep Learning to automate the attendance tracking process. On Integrating cameras installed in universities with advance facial technology, which accurately identifies, record and stores the students attendance without requiring any type of manual intervention. Besides, the system incorporates intelligent features to deal with various scenarios such as occlusions, varying lighting conditions, and facial expressions, ensuring robust performance in real-world environments Through seamless integration with existing student data set and cloud-based platforms, this proposed solution facilitates centralized data management, empowering administrators with actionable insights for strategic decision-making and real time monitoring.

Kalasalingam Academy of Research and Education (IN)
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare
1.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.