Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3293
Title: TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates
Authors: Legrand, T.R
Bandara, K.M.R.A.I
Stefania Crishani, J.A.D
Uvindu, L.W.P
Amarasena, N
Kasthurirathna, D
Keywords: TRIPORA
Intelligent Machine Learning
Learning Solution
Sri Lanka
Touring Access
Updates
Issue Date: 9-Dec-2022
Publisher: IEEE
Citation: T. R. Legrand, K. M. R. A. I. Bandara, J. A. D. Stefania Crishani, L. W. P. Uvindu, N. Amarasena and D. Kasthurirathna, "TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 24-29, doi: 10.1109/ICAC57685.2022.10025139.
Series/Report no.: 2022 4th International Conference on Advancements in Computing (ICAC);
Abstract: Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.
URI: https://rda.sliit.lk/handle/123456789/3293
ISBN: 979-8-3503-9809-0
Appears in Collections:4th International Conference on Advancements in Computing (ICAC) | 2022
Department of Computer Science and Software Engineering

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