Chetana Pattem
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chetana Pattem has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Muthumanickam Gunasekaran
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
1 shared publication
Latest Publications
- Open Access
- 1 January 2023
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 2655 (1)
An effective system to improve restaurant reviewaccuracy based-on customers facial expressions using improved support vector machine in comparison with simple convolution neural network in unmanned restaurants
The study aims to build an efficient feedback system for automated and unmanned restaurants using customers facial expressions by training the model with six different expressions and produce accurate results. The proposed system uses an improved support vector machine for recognizing various expressions of human face in the unmanned restaurants based on a gradient boosting approach. FER2013 dataset has been used for the implementation. This study consists of two groups and the sample size for each group is 392. This research study adapts a novel gradient boosting with kernel trick approach for efficient classification. It is observed that the proposed system obtained 82.14% accuracy while comparing with the existing system which has only 63.88% of accuracy. The result proves that the improved support vector machine with a gradient boosting approach for the feedback system of unmanned and automated restaurants obtained better accuracy than the existing system. This feedback system can also be used as a resort feedback system in order to improve the customer experience.
- 1 January 2023
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 2655 (1)
Open AccessAn effective system to improve restaurant reviewaccuracy based-on customers facial expressions using improved support vector machine in comparison with simple convolution neural network in unmanned restaurants
The study aims to build an efficient feedback system for automated and unmanned restaurants using customers facial expressions by training the model with six different expressions and produce accurate results. The proposed system uses an improved support vector machine for recognizing various expressions of human face in the unmanned restaurants based on a gradient boosting approach. FER2013 dataset has been used for the implementation. This study consists of two groups and the sample size for each group is 392. This research study adapts a novel gradient boosting with kernel trick approach for efficient classification. It is observed that the proposed system obtained 82.14% accuracy while comparing with the existing system which has only 63.88% of accuracy. The result proves that the improved support vector machine with a gradient boosting approach for the feedback system of unmanned and automated restaurants obtained better accuracy than the existing system. This feedback system can also be used as a resort feedback system in order to improve the customer experience.