The implementation of leisure tourism enterprise management system based on deep learning

Qian, Wei; Ge, Yuemeng

doi:10.1007/s13198-021-01103-0

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…With process enhancement/automation, this refers to the application of DL for enhancing processes both for business and consumers, with majority of such articles being in the promote theme. Examples of such contributions are holiday/destination automated booking using chatbots (Jiménez-Barreto et al , 2021; Tuomi et al , 2021; Byrd et al , 2021; Loureiro et al , 2021), leisure tourism enterprise management systems (Qian and Ge, 2021) and tourism destination management and automation (Pelet et al , 2021). The third level of contribution, which we argue returns the highest value, is the application of DL for the development and implementation of novel concepts, business models and product offerings to consumers.…”

Section: Resultsmentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

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Section: Resultsmentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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“…The data inputs are varied between 100 and 1200 for 1 to 11 years. The existing DRL [34], PPO-RL [35], TEM-BPNN [27], ST-CNN [32], RNN [33], and TAR-DL + IoT [24] are used with the proposed ORDAA for analyzing the proposed approach performance. 4.2.…”

Section: Discussionmentioning

confidence: 99%

“…Qian and Ge [27] introduced a deep learning-(DL-) based leisure tourism enterprise management system. The backpropagation neural network (BPNN) model is used here to identify the risks and problems presented in computation.…”

Section: Related Workmentioning

confidence: 99%

Optimized Reinforcement Learning Approach on Sustainable Rural Tourism Development for Economic Growth

Chen

2023

Journal of Applied Mathematics

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A country’s economic development relies on different features such as export/import, industrial processes, and tourism. Rural tourism is a discussion-centric research field for analyzing its contribution to a country’s economic growth. This field generates voluptuous data for tourists, expenditure, location, etc. analysis; the information increases over the years and the density of visiting tourists. Therefore, this article introduces an optimized reinforcement data analysis approach (ORDAA) for generating precise guidance information. This information is two-faced, namely, summarized data for tourist guidance and summarized data for the country’s economic development. Data augmentation’s steep rise and downfall are analyzed using reinforcement learning, wherein decision agents are precise for a relevant summary. The relevance is identified using associated development targets over varying years. Besides, the guidance information that identifies low tourist summary or nonachievable development targets is separately identified. The identified targets are analyzed using reinforcement agents for economic growth improvements compared to the previous tourist densities. This improves the focus on rural tourism sights and economic contributions to an optimal level.

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“…Based on the development status of commercial banks, Wang analyzed that in the era of big data, the risk management of commercial banks has changed from internal control to external prevention, the data effect has changed from a loose association to a close association, and data security has changed from clear and controllable to fuzzy and difficult to control [17]. Risk characteristics, regard data and risk as the two major elements of bank operation, and propose methods to maintain the competitiveness of commercial banks by establishing a big data audit system that can predict bank risks, attaching importance to internal management process control audits, and strengthening bank risk-related audits [18]. Maintain the safe operation of the banking industry in the era of big data.…”

Section: Related Workmentioning

confidence: 99%

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

Zhao¹,

Wang

2023

IEEE Access

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In the big data era, traditional auditing methods are facing challenge such as limited audit scope, uneven distribution of audit power, and insufficient audit analysis. To pursue high efficiency, the utilization of big data analysis technique in financial auditing has been a novel tendency in this area. The deep learning has been popular in many areas due to its high freedom degree. Thus, this paper employs a typical deep learning model convolution neural network (CNN), and proposes a big data-driven financial auditing method using CNN. Specifically, the strong ability of feature abstraction of CNN is leveraged to extract multi-level features in materials, such as visual features, textual features, etc. Then, the multi-source features from auditing materials can be well fused for final discrimination. Some simulation experiments are conducted on real-world financial auditing scenes for assessment. And the results show that the designed the proposed financial auditing method possesses relatively high auditing accuracy.

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The implementation of leisure tourism enterprise management system based on deep learning

Cited by 7 publications

References 13 publications

Deep learning in hospitality and tourism: a research framework agenda for future research

Deep learning in hospitality and tourism: a research framework agenda for future research

Optimized Reinforcement Learning Approach on Sustainable Rural Tourism Development for Economic Growth

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

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