Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT - Sinan Andan Diwan (Alexandria Engineering Journal)
The hospitality industry is experiencing significant growth due to rapid economic expansion. Traditional hotel supervision systems (HSS) focused primarily on financial aspects and are now insufficient for comprehensive information management. Hotels need Intelligent Systems that enhance efficiency, reduce operational costs, and provide better amenities with adequate service to stay competitive. Integrating Face Recognition Technology using the Viola-Jones algorithm with Haar Cascade Classifier for guest check-in and access control within an Internet of Things (IoT)-enabled framework offers a robust solution. The proposed IoT-enabled HSS utilizes face recognition for check-ins, room access, and guest analytics. Developed using Cascading Style Sheets (CSS), Bootstrap, and Java Swing, and linked to a MySQL database, it provides an integrated and centralized hotel system that addresses reservations, checkouts, and settlements, streamlining room management and modernizing operational practices. Additionally, this article explores Machine Learning (ML) models like Deep Neural Networks (DNN) and Random Forests (RF) within a Fuzzy Analytical Hierarchy Process (FAHP) framework to evaluate various crucial aspects during any hotel stay. This smart hospitality framework considers inputs like reception facilities, room amenities, and restaurant services, providing a comprehensive customer satisfaction model. The results demonstrate improved precision and accuracy of the proposed RF model, evidenced by significantly lower Mean Square Error (MSE) and Root Mean Square Error (RMSE) as compared to the DNN. These advancements align with smart city initiatives, contributing to the evolving landscape of IoT in Smart Hospitality (IoT-SH).