Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

The application of machine learning techniques in the field of tourism is experiencing a
remarkable growth, as they allow to propose efficient solutions to problems present in this …

Purpose Utilising the stimulus-organism-response (SOR) theoretical framework, this study
investigated how destination attachment and overall satisfaction mediate the linkage …

Accurate tourism demand forecasting can improve tourism experiences and realize smart
tourism. Existing spatial–temporal tourism demand forecasting models only explore pre …

The pandemic caused by the SARS-CoV-2 virus (COVID-19) has transformed the tourism
sector to an unprecedented extent, creating new challenges and new development paths …

Current research highlights the efficacy of decomposition and ensemble algorithms in
enhancing forecasting accuracy; however, the investigation of robustness associated with …

Purpose This study aims to examine the impact of the structural linkages between
memorable tourism experiences, destination attachment, tourists' satisfaction and customer …

Seasonal variations in the tourism industry consist of alternating patterns of overuse and
underuse of touristic potential and resources, which correspond to overexertion in the peak …

In the hospitality industry, revenue management is vital for the sustainability of the business.
Powering this strategic concept is the occupancy rate (OR) forecast. Predicting occupancy of …

The relentless progression of advanced technologies has driven the seamless integration of
Internet of Things (IoT) services into the fundamental framework of contemporary tourism …