Smart attendance system using real time face recognition

Mohapatra, Badri Narayan; Dsouza, Joel; Sangoiy, Yash; Shah, Nisarg

doi:10.5958/2231-3915.2018.00001.9

Cited by 5 publications

(2 citation statements)

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“…Another example is when a teacher calls out the names of the students and stamps their presence after they have responded [2]. Manually taking and maintaining attendance records becomes quite inconvenient [3].…”

Section: Related Workmentioning

confidence: 99%

“…To address the issue of bogus attendance, a wireless fingerprint attendance control system was proposed, combining biometrics and wireless technology [3]. The enrolment of fingerprints was conducted on the server using the Digital Persona Fingerprint USB Sensor, and the verification was completed on the client utilising the network transfer of fingerprint templates.…”

Section: Related Workmentioning

confidence: 99%

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Development of Attendance Monitoring System with Artificial Intelligence Optimization in Cloud

Naen¹,

Adnan²,

Yazi³

et al. 2021

International Journal of Artificial Intelligence

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The creation of an attendance management system based on biometrics is proposed in this research. Keeping track of student attendance during lecture periods has proven to be a difficult task. Because human calculating creates errors and wastes a lot of time, the capacity to compute the attendance percentage becomes a key challenge. For this reason, a biometric-based attendance management system is being developed. This system uses a fingerprint device to take attendance electronically, and the attendance records are kept in a database. Following student identification, attendance is recorded. Artificial intelligence is also proposed as a component of the system. The system will aid in the reduction of errors and the more effective compilation of attendance data.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Development of Attendance Monitoring System with Artificial Intelligence Optimization in Cloud

Naen¹,

Adnan²,

Yazi³

et al. 2021

International Journal of Artificial Intelligence

View full text Add to dashboard Cite

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Live-Bed Scour Depth Modelling Around the Bridge Pier Using ANN-PSO, ANFIS, MARS, and M5Tree

Baranwal,

Das

2024

Water Resour Manage

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Introducing high-order response surface method for improving scour depth prediction downstream of weirs

Hameed,

Khaleel,

AlOmar

et al. 2024

Appl Water Sci

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Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.

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Smart attendance system using real time face recognition

Cited by 5 publications

References 0 publications

Development of Attendance Monitoring System with Artificial Intelligence Optimization in Cloud

Development of Attendance Monitoring System with Artificial Intelligence Optimization in Cloud

Live-Bed Scour Depth Modelling Around the Bridge Pier Using ANN-PSO, ANFIS, MARS, and M5Tree

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

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