Published Paper Details:

Automated and unmanned restaurant appeal has surged in recent times. Since there is no staff present, it is impossible to directly gauge what patrons think of the restaurant idea or learn about their experiences. This research provides a grading system based on pre-trained convolutional neural network (CNN) models for face expression identification. It consists of a web server, an AI server that has already been taught, and an Android mobile application. It is intended that both the cuisine and the setting be appraised. Restaurants that are both automated and staff-free are growing in popularity. It is unable to directly evaluate the opinions of the customers due to a staffing shortage, thus it is impossible to find out about their experiences with the restaurant concept. This study uses convolutional neural network (CNN) models that have already undergone training to produce a facial emotion recognition grading system. It consists of an AI server that has previously been trained, a web server, and an Android mobile application. It is intended that reviews would be left for both the food and the environment. Three expressions are now available in the grading system: neutral, dissatisfied, and satisfied.