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Predicting Hotel Performance in Oman with AI-Driven Predictive Analytic
R. S. Al Jassim, Karan Jetly, Shqran Al Mansoory, Muna Al-Balushi, Hilal Al Maqbali

Predicting Hotel Performance in Oman with AI-Driven Predictive Analytic

Science & Mathematics Published 2023 Verification Pending

Overview & Synopsis

The primary objective of this study is to assess the performance of hotels in Oman by developing an AI based model using a new approach that we refer to as Linear Genetic Programming for Optimization Decision Tree (LGPDT). The LGPDT algorithm seeks to optimize decision trees, automatically select relevant input attributes, and adjust hyperparameters to improve prediction accuracy. The research findings demonstrate promise after testing the model with datasets from literature and the tourism sector. This approach has the potential to improve the assessment of hotel performance in Oman by providing accurate predictions of customer satisfaction, empowering managers to enhance their services and meet customers’ demands more effectively.

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Document Information

Document Title Predicting Hotel Performance in Oman with AI-Driven Predictive Analytic
Author / Organization R. S. Al Jassim, Karan Jetly, Shqran Al Mansoory, Muna Al-Balushi, Hilal Al Maqbali
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2023
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-176832
Original Source Link Visit Source Page on hal.science »