New Smart Map for Tourism using Artificial Intelligence

Wahyono, Irawan Dwi; Asfani, Khoirudin; Mohamad, Mohd Murtadha; Aripriharta, A.; Wibawa, Aji Prasetya; Wibisono, Waskitho

doi:10.1109/eeccis49483.2020.9263435

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…As for the results, research demonstrates that gamified features help visitors gain some more elevated motivational purposes and boost their concentration in the vacation destination. Meanwhile, research in [25] has developed an AI-integrated innovative map to aid the tourism industry. Brilliant map works based on visitors' wishes; after being processed by an AI algorithm, the system will construct several recommendations for suitable tourist attractions.…”

Section: Related Workmentioning

confidence: 99%

AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

Nguyen,

Tran,

Nham

et al. 2023

Applied Computer Systems

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Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.

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Section: Related Workmentioning

confidence: 99%

AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

Nguyen,

Tran,

Nham

et al. 2023

Applied Computer Systems

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“…The paper [8] presents a web platform for mobile platforms based on deep neural networks for autonomous identification of historical monuments. A study [9] developed a smart map to identify tourist attractions based on tourist desires.…”

Section: Related Workmentioning

confidence: 99%

Concept of the Intelligent Guide with AR Support

Lipianina-Honcharenko¹,

Savchyshyn²,

Sachenko³

et al. 2022

IJC

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In order to save time and money for tourists, as well as taking into account their limited mobility in the conditions of Covid, the concept of an intelligent guide based on augmented reality (AR) is proposed. A UML method diagram and algorithm for communication of the system with the user with AR and voice control support have been developed, as well as an application that allows tourists to immerse themselves in the historical retrospective of recreational places. On the example of the central part (downtown) of Ternopil, Ukraine, AR locations are offered, which gives an opportunity to get acquainted in more detail with the information about the tourist object. At the same time, the shortest tourist route is illuminated of historical monuments.

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“…Currently, the real-life impact of areas such as artificial intelligence, soft computing, computational intelligence, and neural networks, with special emphasis on deep learning, is undeniable [28][29][30][31][32][33]. Recently, computational intelligence techniques are being used to determine the tourists' attitudes towards technology [34], to develop intelligent facilities [35][36][37], to search for hot destinations [38,39], to analyze the impact of smart destinations in tourism [40][41][42], and for customer segmentation [43].…”

Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains.

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New Smart Map for Tourism using Artificial Intelligence

Cited by 5 publications

References 16 publications

AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

Concept of the Intelligent Guide with AR Support

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

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