Application of Deep Learning in Tourist

Daniel Makala, Li Zongmin

2024

Abstract

One of the Giant sectors in Tanzania is Tourism. About 40% of the foreign exchange in Tanzania comes from this sector. It is the number one job provider to Tanzanians, about 10% of the working class is in the tourism sector. Since independence, the sector has been growing well until 2019 during the pandemic issue of COVID-19. However, since 2020 Tanzania has regained and restored the tourist income to normal and expected more tourists. Government and Authority are in the age of determining the number of tourists to come and the income associated with the tourist for better planning. Forecasting tourist inflows requires an accurate model because of the highly changing tourist data due to external factors such as political influence, security issues, or transportation issues. This study analyses and proposes the CNN to be used for the prediction of tourist arrival. CNN can handle and process multiple sequences and thus can handle data and multivariate time series. Using data from 1961 to 2022, of Tanzania's arrival, the proposed model was able to predict with more accuracy compared to ARIMA, LSTM, and CNN-LSTM by having 0.1 RMSE. However, the study is limited due to the unavailability of daily tourist income.

Paper Citation

in Harvard Style

Makala D. and Zongmin L. (2024). Application of Deep Learning in Tourist. In Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com; ISBN 978-989-758-739-9, SciTePress, pages 248-256. DOI: 10.5220/0013342500004646

in Bibtex Style

@conference{ic3com24,
author={Daniel Makala and Li Zongmin},
title={Application of Deep Learning in Tourist},
booktitle={Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com},
year={2024},
pages={248-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013342500004646},
isbn={978-989-758-739-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com
TI - Application of Deep Learning in Tourist
SN - 978-989-758-739-9
AU - Makala D.
AU - Zongmin L.
PY - 2024
SP - 248
EP - 256
DO - 10.5220/0013342500004646
PB - SciTePress