MultiMedia Modeling

doi:10.1007/978-3-030-05710-7

Cited by 6 publications

(1 citation statement)

References 26 publications

(52 reference statements)

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“…Developments in the tourism industry require the originality, uniqueness, and intact resources of tourist attractions [23]. In tourist attraction recommendations, three important aspects must be considered: tourist preferences, tourist attraction themes, and sentiments on tourist attraction themes [24]. There are three components to tourist attractions, such as tourists, sites to see, and an image that makes an attraction different [25].…”

Section: Smart Tourism Technologymentioning

confidence: 99%

Visiting Tourism Destination: Is It Influenced by Smart Tourism Technology?

Susanto

Novianti

Rafdinal

et al. 2020

JITODE

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The development of technology has changed various sectors in life to be smart, including tourism. This study aims to analyze the effect of smart tourism technology attributes on visit intention and visiting tourist destinations. This study used a sample of 324 tourists in West Java Province, Indonesia. Partial Least Square is applied to test the relationship between variables. The results of the study revealed that smart tourism technology attributes such as smart information systems, smart tourism management, smart sightseeing, e-commerce systems, smart safety, smart traffic, and virtual tourism objects affect visit intention. The study also revealed the effect of visiting intentions on visits to tourist destinations. The findings of this study provide the basis for formulating strategies for implementing smart tourism technology that is appropriate in attracting tourist visits.

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Section: Smart Tourism Technologymentioning

confidence: 99%

Visiting Tourism Destination: Is It Influenced by Smart Tourism Technology?

Susanto

Novianti

Rafdinal

et al. 2020

JITODE

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Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition

Liu,

Yu,

Liu

2024

Applied Sciences

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Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence’s autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model’s accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability.

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Visualization and Cybersecurity in the Metaverse: A Survey

Chow

Susilo

et al. 2022

J. Imaging

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The popularity of the Metaverse has rapidly increased in recent years. However, despite the attention, investment, and promise of the Metaverse, there are various cybersecurity issues that must be addressed before the Metaverse can truly be adopted in practice for serious applications. The realization of the Metaverse is envisioned by many as requiring the use of visualization technologies such as Virtual Reality (VR) and Augmented Reality (AR). This visual aspect of the Metaverse will undoubtedly give rise to emerging cybersecurity threats that have not received much attention. As such, the purpose of this survey is to investigate cybersecurity threats faced by the Metaverse in relation to visualization technologies. Furthermore, this paper discusses existing work and open research directions on the development of countermeasures against such threats. As the Metaverse is a multidisciplinary topic, the intention of this work is to provide a background of the field to aid researchers in related areas.

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MultiMedia Modeling

Cited by 6 publications

References 26 publications

Visiting Tourism Destination: Is It Influenced by Smart Tourism Technology?

Visiting Tourism Destination: Is It Influenced by Smart Tourism Technology?

Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition

Visualization and Cybersecurity in the Metaverse: A Survey

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