4th International Conference on Advancements in Computing [ICAC] 2022

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Author: search.filters.author.Amarasena, NStart date: 2022

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    TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates
    (IEEE, 2022-12-09) Legrand, T.R; Bandara, K.M.R.A.I; Stefania Crishani, J.A.D; Uvindu, L.W.P; Amarasena, N; Kasthurirathna, D
    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.
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    “Stonelia” – Prehistoric Stone Tool Identification Android App for Archaeological Researchers
    (IEEE, 2022-12-09) Perera, K. B. N.; Pathirana, M. K.; Wijayarathna, P. G. R. C.; Amarasena, N; Hewabathma, C. D.
    Prehistoric stone tools can be considered one of the oldest artifacts created by ancient humans. Lithic archeology’s study of stone tools provides important information about early humans’ technologies, agility, and mental and innovative abilities. A vital issue in lithic archeology is the identification and analysis of stone tools found at the excavation sites. Archeologists need to observe and analyze a stone tool under different aspects for a long time to verify whether it is a stone tool or a geofact, the techniques used to create it, and identify its rough relative date and functional value. This can be challenging for amateur scholars studying archeology since it requires a lot of experience and time to identify by a glance. As a solution, ‘Stonelia,’ a mobile-based android application, can be introduced to identify and analyze stone tools. The images captured through the mobile app are preprocessed using image processing. Using Convolutional Neural Network models identifies the stone artifact from a geofact, the mineral type, the rough relative date, techniques used to create the stone artifact, and its functional value. This mobile application provides prompt identification and analysis of stone artifacts within a short time and with higher accuracy.