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Tourist Spot Recognition Using Machine Learning Algorithms

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dc.contributor.author Roy, Pranta
dc.contributor.author Setu, Jahanggir Hossain
dc.contributor.author Binti, Afrin Nahar
dc.contributor.author Jahan, Nusrat
dc.date.accessioned 2024-06-12T05:53:46Z
dc.date.available 2024-06-12T05:53:46Z
dc.date.issued 2023-01-23
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12735
dc.description.abstract Tourism plays significant role for enhancing economic potential worldwide. The natural beauty and historical interests of Bangladesh remarked as a major tourist destination for the international tourists. In this study, we target to propose a deep learning-based application to recognize historical interests and tourist spots from an images. Making use of on-device neural engine comes with modern devices makes the application robust and Internet-free user experience. One of the difficult tasks is to collect real images from tourist sites. Our collected images were in different sizes because of using different smartphones. We used following deep learning algorithms—convolution neural network (CNN), support vector machine (SVM), long short-term memory (LSTM), K-nearest neighbor (KNN) and recurrent neural network (RNN). In this proposed framework, tourists can effortlessly detect their targeted places that can boost the tourism sector of Bangladesh. For this regard, convolutional neural network (CNN) achieved best accuracy of 97%. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Tourism en_US
dc.subject Deep learning en_US
dc.subject Convolutional neural en_US
dc.subject network Recurrent en_US
dc.subject neural network en_US
dc.subject Tourist spot en_US
dc.title Tourist Spot Recognition Using Machine Learning Algorithms en_US
dc.type Article en_US


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