Browsing by Author "Navya, R.S."

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Automated Evaluation of Attendance and Cumulative Feedback using Face Recognition
(Institute of Electrical and Electronics Engineers Inc., 2018) Shalini, S.; Navya, R.S.; Neha, M.; Ramteke, P.B.; Koolagudi, S.G.
Face recognition is an important technological development of this era. It is being widely used in biometric systems, gaming as well as to tag people on social media. It is also being used for attendance because the manual system is tedious and time-consuming. This paper proposes an automated attendance and cumulative feedback system based on facial expression recognition. The proposed automation system recognizes students from a recorded video of the class and captures their attendance. Local Binary Pattern Histograms (LBPH) and Eigen Face recognizers have been used for face recognition with an accuracy of 97% and 95% respectively. This paper addresses another issue of feedback of the professor by deducing genuine and cumulative feedback based on facial expressions of the students. Two methods have been proposed for deducing the feedback. One is the algorithmic method based on face recognition using confidence measure for expressions detection and the other one uses Speeded up robust features (SURF) and Support Vector Machines(SVM). The proposed methodology is observed to be in correlation with the conventional method of feedback evaluation. Copy Right Â© INDIACom-2018.
Implementation and validation of random exponential marking (REM) in ns-3
(2018) Tarte, I.; Joshi, A.R.; Navya, R.S.; Tahiliani, M.P.
The problem of bufferbloat has reignited interest in studying Active Queue Management (AQM) algorithms. Significant efforts have been taken by AQM and Packet Scheduling Working Group at IETF to bring more awareness about the performance benefits of deploying AQM algorithms in the Internet. However, experimental analysis of these algorithms is necessary prior to real time deployment. Network simulators like ns-3 are useful tools to perform such preliminary studies. Random Exponential Marking (REM) is one of the popular AQM algorithms. It decouples congestion measure from performance measure, and aims to stabilize the performance measure around the target queue length regardless of the number of users. This paper presents the implementation of a new model for REM in ns-3. The correctness of the proposed model has been validated by comparing the results obtained from it, to those obtained from the ns-2 model developed by authors of REM. � 2017 IEEE.
Implementation and validation of random exponential marking (REM) in ns-3
(Institute of Electrical and Electronics Engineers Inc., 2018) Tarte, I.; Joshi, A.R.; Navya, R.S.; Tahiliani, M.P.
The problem of bufferbloat has reignited interest in studying Active Queue Management (AQM) algorithms. Significant efforts have been taken by AQM and Packet Scheduling Working Group at IETF to bring more awareness about the performance benefits of deploying AQM algorithms in the Internet. However, experimental analysis of these algorithms is necessary prior to real time deployment. Network simulators like ns-3 are useful tools to perform such preliminary studies. Random Exponential Marking (REM) is one of the popular AQM algorithms. It decouples congestion measure from performance measure, and aims to stabilize the performance measure around the target queue length regardless of the number of users. This paper presents the implementation of a new model for REM in ns-3. The correctness of the proposed model has been validated by comparing the results obtained from it, to those obtained from the ns-2 model developed by authors of REM. Â© 2017 IEEE.