Smart hospitality using IoT enabled integrated face recognition, machine learning, and fuzzy AHP for analyzing customer satisfaction measurements

The hospitality industry is rapidly evolving, necessitating advanced systems beyond traditional Hospitality Management Systems (HMS), which mainly handle financial matters. This paper presents an innovative Intelligent System (IS) designed to increase operational efficiency, decrease costs, and enhance guest experiences by using the Internet of Things (IoT) enabled Face Recognition (FR) technology. The system facilitates secure and contact-free guest check-ins, checkouts, and room access using a Smart Access Control Mechanism (SACM). Besides these operational enhancements, the system uses Machine Learning (ML) models, including Deep Neural Networks (DNN) and Random Forests (RF), to evaluate customer satisfaction. These models are integrated with a Fuzzy Analytical Hierarchy Process (FAHP) framework, which evaluates satisfaction based on various factors, like reception services, room amenities, and restaurant experiences. By integrating these technologies, the proposed IS gives a complete approach to customer satisfaction modelling, permitting hotels to deliver better services. The proposed work highlights the effectiveness of the ML models by using performance metrics such as Mean Square Error (MSE) and Root Mean Square Error (RMSE). Lastly, the proposed work is validated and compared with existing studies based on training accuracy (%) and validation accuracy (%).