Machine learning-based cloud IOT platform for intelligent tourism information services

Bi, Fangfei; Liu, Haotian

doi:10.1186/s13638-022-02138-y

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…It also involves normalizing data values for further queries and analysis. To deal with oversampling and under sampling, we have considered the synthetic minority oversampling (SMOTE) technique [16] for balancing the dataset values.…”

Section: Pre-processing Of Datamentioning

confidence: 99%

“…We have considered a dataset D = {D1, D2, ………Dq} with a set of clusters C= {C1, C2, …….., Cp} and some set of membership values M = {1<P<m, 1<Q<n} that is required to be formulated in a manner such that train values can combine neural network with FCM [16]. Equation 2shows the efficient auto-encoder values by minimizing the training set:…”

Section: Extraction Of Features Using Fuzzy C-means Clustering (Fcm)mentioning

confidence: 99%

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An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Kumar,

Goyal,

Sharma

2024

Int. j. inf. tecnol.

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The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.

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Section: Pre-processing Of Datamentioning

confidence: 99%

Section: Extraction Of Features Using Fuzzy C-means Clustering (Fcm)mentioning

confidence: 99%

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Kumar,

Goyal,

Sharma

2024

Int. j. inf. tecnol.

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“…The prediction accuracy test results for machine learning in this study were compared with the study of Bi & Liu [43] for the hybrid intelligent categorization approach based on visitor selection behavior. This effectively assisted travelers in deciding whether or not to visit a specific vacation location by utilizing machine learning techniques to predict user behavior and travel decision-making.…”

Section: Discussionmentioning

confidence: 99%

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

Phuthong,

Anuntavoranich,

Chandrachai

et al. 2023

HighTech. Innov. J.

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Objectives: This research developed and evaluated the effectiveness of an innovative mobile application for wellness tourism destination competitiveness and also studied the adoption effectiveness of this application. Methods/Analysis: A mixed-methods research and development approach was applied to construct a wellness tourism destination competitiveness evaluation model for qualitative research using in-depth interviews, followed by quantitative research using a questionnaire. Weighted scores of criteria and indicators for wellness tourism destination competitiveness were evaluated by the DEMATEL method. The cut-off points for classifying the competitiveness level were set by K-means cluster analysis, while the internal and external accuracy of the model were validated by the confusion matrix technique and the Kruskal-Wallis test. The innovative mobile application was developed using a linear waterfall conceptual design consisting of five software development phases: requirement, design, implementation, verification, and maintenance. A questionnaire was also used to assess the adoption and commercialization of the innovative mobile application. Findings: Results showed that 1) the model gave high accuracy with the confusion matrix technique at 85.42% and the Kruskal-Wallis test classified destination competitiveness at a significance level of 0.0001; and 2) the level of adoption of the innovative mobile application was high. Target users were interested in purchasing a license as the commercial mode of the program. Novelty/Improvement:This research provides a tool to assess the overall competitiveness of wellness tourism destinations. Results can be used to support decision-making and provide practical suggestions for wellness tourism cluster users to adapt when conducting their own competitiveness assessment. The competitiveness assessment results were accurate and in line with the research objectives. Doi: 10.28991/HIJ-2023-04-03-010 Full Text: PDF

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“…Machine learning (ML) and artificial intelligence (AI) serve as essential IoT technologies in supporting smart tourism, playing a crucial role in shaping personalized and intelligent travel experiences (Bi and Liu 2022). ML algorithms, for instance, analyze extensive datasets derived from tourist activities, preferences, and historical trends, extracting valuable insights (Ma 2023;Mubarak et al 2021).…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of Things

Rosário,

Dias

2024

Administrative Sciences

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This study explores the transformative impact of IoT technologies on smart tourism, striving to boost operational efficiency and enrich the traveler experience. Using a systematic literature review with bibliometric analysis, we examined a sample of 83 studies indexed in SCOPUS to identify research activity on this topic until November 2023. The integration of cutting-edge technologies, including big data, smart sensors, cloud computing, machine learning, artificial intelligence, wearables, mobile applications, augmented reality, and virtual reality, establishes the foundation of the IoT-enabled smart tourism ecosystem. These innovations bring distinct capabilities, from facilitating data collection with embedded sensor technologies to using cloud computing for essential services like data storage and analytics. Examining various IoT applications in smart tourism, such as recommender systems, smart cities, payment systems, and electronic ticketing, reveals their positive effects on safety, efficiency, and personalized services. However, challenges like security, privacy, software complexity, scalability, and interoperability necessitate robust measures. This study identifies future research directions, including enhancing security and privacy, exploring blockchain integration, investigating edge computing, and improving interactions between tourists and smart tourism systems. These endeavors aim to address challenges and seize opportunities, fostering innovative solutions for the evolving needs of the tourism industry in a technologically advanced landscape.

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Machine learning-based cloud IOT platform for intelligent tourism information services

Cited by 13 publications

References 36 publications

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of Things

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